Publications

@article{pmid36398744,

title = {A novel tissue mechanics-based method for improved motion tracking in quasi-static ultrasound elastography},

author = {Niusha Kheirkhah and Sergio Dempsey and Ali Sadeghi-Naini and Abbas Samani},

doi = {10.1002/mp.16110},

issn = {2473-4209},

year  = {2023},

date = {2023-04-01},

journal = {Med Phys},

volume = {50},

number = {4},

pages = {2176--2194},

abstract = {PURPOSE: Most cancers are associated with biological and structural changes that lead to tissue stiffening. Therefore, imaging tissue stiffness using quasi-static ultrasound elastography (USE) can potentially be effective in cancer diagnosis. USE techniques developed for stiffness image reconstruction use noisy displacement data to obtain the stiffness images. In this study, we propose a technique to substantially improve the accuracy of the displacement data computed through ultrasound tissue motion tracking techniques, especially in the lateral direction.



METHODS: The proposed technique uses mathematical constraints derived from fundamental tissue mechanics principles to regularize displacement and strain fields obtained using Global Ultrasound Elastography (GLUE) and Second-Order Ultrasound Elastography (SOUL) methods. The principles include a novel technique to enforce (1) tissue incompressibility using 3D Boussinesq model and (2) deformation compatibility using the compatibility differential equation. The technique was validated thoroughly using metrics pertaining to Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR) and Normalized Cross Correlation (NCC) for four tissue-mimicking phantom models and two clinical breast ultrasound elastography cases.



RESULTS: The results show substantial improvement in the displacement and strain images generated using the proposed technique. The tissue-mimicking phantom study results indicate that the proposed method is superior in improving image quality compared to the GLUE and SOUL techniques as it shows an average axial strain SNR and CNR improvement of 44% and 63%, and lateral strain SNR and CNR improvement of 130% and 435%, respectively. The results of the phantom study also indicate higher accuracy of displacement images obtained using the proposed technique, including improvement ranges of 7-84% and 26-140% for axial and lateral displacement images, respectively. For the clinical cases, the results indicate average improvement of 48% and 64% in SNR and CNR, respectively, in the axial strain images, and average improvement of 40% and 41% in SNR and CNR, respectively, in the lateral strain images.



CONCLUSION: The proposed method is very effective in producing improved estimate of tissue displacement and strain images, especially with the lateral displacement and strain where the improvement is highly remarkable. While the method shows promise for clinical applications, further investigation is necessary for rigorous assessment of the method's performance in the clinic.},

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pubstate = {published},

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}

@article{pmid36911927,

title = {Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes},

author = {Romuald Ferre and Janne Elst and Seanthan Senthilnathan and Andrew Lagree and Sami Tabbarah and Fang-I Lu and Ali Sadeghi-Naini and William T Tran and Belinda Curpen},

doi = {10.3233/BD-220018},

issn = {1558-1551},

year  = {2023},

date = {2023-01-01},

journal = {Breast Dis},

volume = {42},

number = {1},

pages = {59--66},

abstract = {OBJECTIVES: Early diagnosis of triple-negative (TN) and human epidermal growth factor receptor 2 positive (HER2+) breast cancer is important due to its increased risk of micrometastatic spread necessitating early treatment and for guiding targeted therapies. This study aimed to evaluate the diagnostic performance of machine learning (ML) classification of newly diagnosed breast masses into TN versus non-TN (NTN) and HER2+ versus HER2 negative (HER2-) breast cancer, using radiomic features extracted from grayscale ultrasound (US) b-mode images.



MATERIALS AND METHODS: A retrospective chart review identified 88 female patients who underwent diagnostic breast US imaging, had confirmation of invasive malignancy on pathology and receptor status determined on immunohistochemistry available. The patients were classified as TN, NTN, HER2+ or HER2- for ground-truth labelling. For image analysis, breast masses were manually segmented by a breast radiologist. Radiomic features were extracted per image and used for predictive modelling. Supervised ML classifiers included: logistic regression, k-nearest neighbour, and Naïve Bayes. Classification performance measures were calculated on an independent (unseen) test set. The area under the receiver operating characteristic curve (AUC), sensitivity (%), and specificity (%) were reported for each classifier.



RESULTS: The logistic regression classifier demonstrated the highest AUC: 0.824 (sensitivity: 81.8%, specificity: 74.2%) for the TN sub-group and 0.778 (sensitivity: 71.4%, specificity: 71.6%) for the HER2 sub-group.



CONCLUSION: ML classifiers demonstrate high diagnostic accuracy in classifying TN versus NTN and HER2+ versus HER2- breast cancers using US images. Identification of more aggressive breast cancer subtypes early in the diagnostic process could help achieve better prognoses by prioritizing clinical referral and prompting adequate early treatment.},

keywords = {},

pubstate = {published},

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}

@article{pmid37018589,

title = {Automatic Assessment of Stereotactic Radiation Therapy Outcome in Brain Metastasis using Longitudinal Segmentation on Serial MRI},

author = {Seyed Ali Jalalifar and Hany Soliman and Arjun Sahgal and Ali Sadeghi-Naini},

doi = {10.1109/JBHI.2023.3235304},

issn = {2168-2208},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {IEEE J Biomed Health Inform},

volume = {27},

number = {6},

pages = {2681-2692},

abstract = {The standard clinical approach to assess the radiotherapy outcome in brain metastasis is through monitoring the changes in tumour size on longitudinal MRI. This assessment requires contouring the tumour on many volumetric images acquired before and at several follow-up scans after the treatment that is routinely done manually by oncologists with a substantial burden on the clinical workflow. In this work, we introduce a novel system for automatic assessment of stereotactic radiation therapy (SRT) outcome in brain metastasis using standard serial MRI. At the heart of the proposed system is a deep learning-based segmentation framework to delineate tumours longitudinally on serial MRI with high precision. Longitudinal changes in tumour size are then analyzed automatically to assess the local response and detect possible adverse radiation effects (ARE) after SRT. The system was trained and optimized using the data acquired from 96 patients (130 tumours) and evaluated on an independent test set of 20 patients (22 tumours; 95 MRI scans). The comparison between automatic therapy outcome evaluation and manual assessments by expert oncologists demonstrates a good agreement with an accuracy, sensitivity, and specificity of 91%, 89%, and 92%, respectively, in detecting local control/failure and 91%, 100%, and 89% in detecting ARE on the independent test set. This study is a step forward towards automatic monitoring and evaluation of radiotherapy outcome in brain tumours that can streamline the radio-oncology workflow substantially.},

keywords = {},

pubstate = {published},

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}

@article{pmid36478770,

title = {A Self-Attention-Guided 3D Deep Residual Network With Big Transfer to Predict Local Failure in Brain Metastasis After Radiotherapy Using Multi-Channel MRI},

author = {Seyed Ali Jalalifar and Hany Soliman and Arjun Sahgal and Ali Sadeghi-Naini},

doi = {10.1109/JTEHM.2022.3219625},

issn = {2168-2372},

year  = {2023},

date = {2023-01-01},

journal = {IEEE J Transl Eng Health Med},

volume = {11},

pages = {13--22},

abstract = {A noticeable proportion of larger brain metastases (BMs) are not locally controlled after stereotactic radiotherapy, and it may take months before local progression is apparent on standard follow-up imaging. This work proposes and investigates new explainable deep-learning models to predict the radiotherapy outcome for BM. A novel self-attention-guided 3D residual network is introduced for predicting the outcome of local failure (LF) after radiotherapy using the baseline treatment-planning MRI. The 3D self-attention modules facilitate capturing long-range intra/inter slice dependencies which are often overlooked by convolution layers. The proposed model was compared to a vanilla 3D residual network and 3D residual network with CBAM attention in terms of performance in outcome prediction. A training recipe was adapted for the outcome prediction models during pretraining and training the down-stream task based on the recently proposed big transfer principles. A novel 3D visualization module was coupled with the model to demonstrate the impact of various intra/peri-lesion regions on volumetric multi-channel MRI upon the network's prediction. The proposed self-attention-guided 3D residual network outperforms the vanilla residual network and the residual network with CBAM attention in accuracy, F1-score, and AUC. The visualization results show the importance of peri-lesional characteristics on treatment-planning MRI in predicting local outcome after radiotherapy. This study demonstrates the potential of self-attention-guided deep-learning features derived from volumetric MRI in radiotherapy outcome prediction for BM. The insights obtained via the developed visualization module for individual lesions can possibly be applied during radiotherapy planning to decrease the chance of LF.},

keywords = {},

pubstate = {published},

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}

@article{doi:10.1080/21681163.2022.2058415,

title = {Automatic characterization of breast lesions using multi-scale attention-guided deep learning of digital histology images},

author = {William T. Tran Khadijeh Saednia and Ali Sadeghi-Naini},

url = {https://doi.org/10.1080/21681163.2022.2058415},

doi = {10.1080/21681163.2022.2058415},

year  = {2023},

date = {2023-01-01},

journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization},

volume = {11},

number = {1},

pages = {103-111},

publisher = {Taylor & Francis},

abstract = {A multi-scale attention-guided deep learning model is proposed to characterise breast tissue in digital histology images (H&E stained) according to four different histological types including normal, benign, in situ carcinoma and invasive carcinoma. The framework includes two parallel convolutional neural networks with modified VGG16 architecture. The first network analyzes the whole-sample images at low magnification. The second network focuses on the patches extracted from the whole-sample images at high magnification. In the low-magnification network, a global average pooling layer was added at the end of the network to extract the class activation maps for the attention model. A long short-term memory network was adapted as a recurrent attention mechanism to increase the contribution of the relevant parts of each image for classification. In the high-magnification network, the probability vectors were averaged over all patches extracted from an image to obtain the probability vectors associated with the four histological types for each sample. The probability vectors for each sample from the high-magnification network and the attention model were fused using a multilayer perceptron network to generate a classification label. Obtained results on an independent test set demonstrated an average accuracy of 97.5% ± 1.0% for the proposed model. An average accuracy of 94.5%, 93.5%, and 96.3% was obtained, respectively, for the separate high- and low-magnification networks, and the multi-scale model without an attention mechanism. The results suggested that a multi-scale strategy coupled with an attention mechanism can improve the accuracy of deep learning models in classifying digital histology images.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{KHEIRKHAH2023102619,

title = {Enhanced full-inversion-based ultrasound elastography for evaluating tumor response to neoadjuvant chemotherapy in patients with locally advanced breast cancer},

author = {Niusha Kheirkhah and Anat Kornecki and Gregory J. Czarnota and Abbas Samani and Ali Sadeghi-Naini},

url = {https://www.sciencedirect.com/science/article/pii/S1120179723000960},

doi = {https://doi.org/10.1016/j.ejmp.2023.102619},

issn = {1120-1797},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Physica Medica},

volume = {112},

pages = {102619},

abstract = {Purpose 

An enhanced ultrasound elastography technique is proposed for early assessment of locally advanced breast cancer (LABC) response to neoadjuvant chemotherapy (NAC). 

Methods 

The proposed elastography technique inputs ultrasound radiofrequency data obtained through tissue quasi-static stimulation and adapts a strain refinement algorithm formulated based on fundamental principles of continuum mechanics, coupled with an iterative inverse finite element method to reconstruct the breast Young’s modulus (E) images. The technique was explored for therapy response assessment using data acquired from 25 LABC patients before and at weeks 1, 2, and 4 after the NAC initiation (100 scans). The E ratio of tumor to the surrounding tissue was calculated at different scans and compared to the baseline for each patient. Patients’ response to NAC was determined many months later using standard clinical and histopathological criteria. 

Results Reconstructed E ratio changes obtained as early as one week after the NAC onset demonstrate very good separation between the two cohorts of responders and non-responders to NAC. Statistically significant differences were observed in the E ratio changes between the two patient cohorts at weeks 1 to 4 after treatment (p-value < 0.001; statistical power greater than 97%). A significant difference in axial strain ratio changes was observed only at week 4 (p-value = 0.01; statistical power = 76%). No significant difference was observed in tumor size changes at weeks 1, 2 or 4. 

Conclusion 

The proposed elastography technique demonstrates a high potential for chemotherapy response monitoring in LABC patients and superior performance compared to strain imaging.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{genes14091768,

title = {Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning},

author = {Audrey Shiner and Alex Kiss and Khadijeh Saednia and Katarzyna J. Jerzak and Sonal Gandhi and Fang-I Lu and Urban Emmenegger and Lauren Fleshner and Andrew Lagree and Marie Angeli Alera and Mateusz Bielecki and Ethan Law and Brianna Law and Dylan Kam and Jonathan Klein and Christopher J. Pinard and Alex Shenfield and Ali Sadeghi-Naini and William T. Tran},

url = {https://www.mdpi.com/2073-4425/14/9/1768},

doi = {10.3390/genes14091768},

issn = {2073-4425},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Genes},

volume = {14},

number = {9},

pages = {1768},

abstract = {Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1002/mp.16574,

title = {A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre‑treatment tumor biopsies},

author = {Khadijeh Saednia and William T. Tran and Ali Sadeghi‑Naini},

url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.16574},

doi = {https://doi.org/10.1002/mp.16574},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Medical Physics},

volume = {50},

number = {12},

pages = {7852-7864},

abstract = {Background: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breast cancer (BC) patients. However, pCR rates to NAC are less than 30%, depending on the BC subtype. Early prediction of NAC response would facilitate therapeutic modifications for individual patients, potentially improving overall treatment outcomes and patient survival.

Purpose: This study, for the first time, proposes a hierarchical self-attention-guided deep learning framework to predict NAC response in breast cancer patients using digital histopathological images of pre-treatment biopsy specimens. 

Methods: Digitized hematoxylin and eosin-stained slides of BC core needle biopsies were obtained from 207 patients treated with NAC, followed by surgery. The response to NAC for each patient was determined using the standard clinical and pathological criteria after surgery. The digital pathology images were processed through the proposed hierarchical framework consisting of patch-level and tumor-level processing modules followed by a patient-level response prediction component. A combination of convolutional layers and transformer self-attention blocks were utilized in the patch-level processing architecture to generate optimized feature maps. The feature maps were analyzed through two vision transformer architectures adapted for the tumor-level processing and the patient-level response prediction components. The feature map sequences for these transformer architectures were defined based on the patch positions within the tumor beds and the bed positions within the biopsy slide, respectively. A five-fold cross-validation at the patient level was applied on the training set (144 patients with 9430 annotated tumor beds and 1,559,784 patches) to train the models and optimize the hyperparameters. An unseen independent test set (63 patients with 3574 annotated tumor beds and 173,637 patches) was used to evaluate the framework. 

Results: The obtained results on the test set showed an AUC of 0.89 and an F1-score of 90% for predicting pCR to NAC a priori by the proposed hierarchical framework. Similar frameworks with the patch-level, patch-level + tumor-level, and patch-level + patient-level processing components resulted in AUCs of 0.79, 0.81, and 0.84 and F1-scores of 86%, 87%, and 89%, respectively. 

Conclusions: The results demonstrate a high potential of the proposed hierarchical deep-learning methodology for analyzing digital pathology images of pre-treatment tumor biopsies to predict the pathological response of breast cancer to NAC.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35727568,

title = {Predicting the outcome of radiotherapy in brain metastasis by integrating the clinical and MRI-based deep learning features},

author = {Seyed Ali Jalalifar and Hany Soliman and Arjun Sahgal and Ali Sadeghi-Naini},

doi = {10.1002/mp.15814},

issn = {2473-4209},

year  = {2022},

date = {2022-11-01},

journal = {Med Phys},

volume = {49},

number = {11},

pages = {7167--7178},

abstract = {BACKGROUND: A considerable proportion of metastatic brain tumors progress locally despite stereotactic radiation treatment, and it can take months before such local progression is evident on follow-up imaging. Prediction of radiotherapy outcome in terms of tumor local failure is crucial for these patients and can facilitate treatment adjustments or allow for early salvage therapies.



PURPOSE: In this work, a novel deep learning architecture is introduced to predict the outcome of local control/failure in brain metastasis treated with stereotactic radiation therapy using treatment-planning magnetic resonance imaging (MRI) and standard clinical attributes.



METHODS: At the core of the proposed architecture is an InceptionResentV2 network to extract distinct features from each MRI slice for local outcome prediction. A recurrent or transformer network is integrated into the architecture to incorporate spatial dependencies between MRI slices into the predictive modeling. A visualization method based on prediction difference analysis is coupled with the deep learning model to illustrate how different regions of each lesion on MRI contribute to the model's prediction. The model was trained and optimized using the data acquired from 99 patients (116 lesions) and evaluated on an independent test set of 25 patients (40 lesions).



RESULTS: The results demonstrate the promising potential of the MRI deep learning features for outcome prediction, outperforming standard clinical variables. The prediction model with only clinical variables demonstrated an area under the receiver operating characteristic curve (AUC) of 0.68. The MRI deep learning models resulted in AUCs in the range of 0.72 to 0.83 depending on the mechanism to integrate information from MRI slices of each lesion. The best prediction performance (AUC = 0.86) was associated with the model that combined the MRI deep learning features with clinical variables and incorporated the inter-slice dependencies using a long short-term memory recurrent network. The visualization results highlighted the importance of tumor/lesion margins in local outcome prediction for brain metastasis.



CONCLUSIONS: The promising results of this study show the possibility of early prediction of radiotherapy outcome for brain metastasis via deep learning of MRI and clinical attributes at pre-treatment and encourage future studies on larger groups of patients treated with other radiotherapy modalities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid36291917,

title = {Impact of Tumour Segmentation Accuracy on Efficacy of Quantitative MRI Biomarkers of Radiotherapy Outcome in Brain Metastasis},

author = {Seyed Ali Jalalifar and Hany Soliman and Arjun Sahgal and Ali Sadeghi-Naini},

doi = {10.3390/cancers14205133},

issn = {2072-6694},

year  = {2022},

date = {2022-10-01},

journal = {Cancers (Basel)},

volume = {14},

number = {20},

abstract = {Significantly affecting patients' clinical course and quality of life, a growing number of cancer cases are diagnosed with brain metastasis (BM) annually. Stereotactic radiotherapy is now a major treatment option for patients with BM. However, it may take months before the local response of BM to stereotactic radiation treatment is apparent on standard follow-up imaging. While machine learning in conjunction with radiomics has shown great promise in predicting the local response of BM before or early after radiotherapy, further development and widespread application of such techniques has been hindered by their dependency on manual tumour delineation. In this study, we explored the impact of using less-accurate automatically generated segmentation masks on the efficacy of radiomic features for radiotherapy outcome prediction in BM. The findings of this study demonstrate that while the effect of tumour delineation accuracy is substantial for segmentation models with lower dice scores (dice score ≤ 0.85), radiomic features and prediction models are rather resilient to imperfections in the produced tumour masks. Specifically, the selected radiomic features (six shared features out of seven) and performance of the prediction model (accuracy of 80% versus 80%, AUC of 0.81 versus 0.78) were fairly similar for the ground-truth and automatically generated segmentation masks, with dice scores close to 0.90. The positive outcome of this work paves the way for adopting high-throughput automatically generated tumour masks for discovering diagnostic and prognostic imaging biomarkers in BM without sacrificing accuracy.},

keywords = {},

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tppubtype = {article}

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@article{pmid35690630,

title = {Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies},

author = {Khadijeh Saednia and Andrew Lagree and Marie A Alera and Lauren Fleshner and Audrey Shiner and Ethan Law and Brianna Law and David W Dodington and Fang-I Lu and William T Tran and Ali Sadeghi-Naini},

doi = {10.1038/s41598-022-13917-4},

issn = {2045-2322},

year  = {2022},

date = {2022-06-01},

journal = {Sci Rep},

volume = {12},

number = {1},

pages = {9690},

abstract = {Complete pathological response (pCR) to neoadjuvant chemotherapy (NAC) is a prognostic factor for breast cancer (BC) patients and is correlated with improved survival. However, pCR rates are variable to standard NAC, depending on BC subtype. This study investigates quantitative digital histopathology coupled with machine learning (ML) to predict NAC response a priori. Clinicopathologic data and digitized slides of BC core needle biopsies were collected from 149 patients treated with NAC. The nuclei within the tumor regions were segmented on the histology images of biopsy samples using a weighted U-Net model. Five pathomic feature subsets were extracted from segmented digitized samples, including the morphological, intensity-based, texture, graph-based and wavelet features. Seven ML experiments were conducted with different feature sets to develop a prediction model of therapy response using a gradient boosting machine with decision trees. The models were trained and optimized using a five-fold cross validation on the training data and evaluated using an unseen independent test set. The prediction model developed with the best clinical features (tumor size, tumor grade, age, and ER, PR, HER2 status) demonstrated an area under the ROC curve (AUC) of 0.73. Various pathomic feature subsets resulted in models with AUCs in the range of 0.67 and 0.87, with the best results associated with the graph-based and wavelet features. The selected features among all subsets of the pathomic and clinicopathologic features included four wavelet and three graph-based features and no clinical features. The predictive model developed with these features outperformed the other models, with an AUC of 0.90, a sensitivity of 85% and a specificity of 82% on the independent test set. The results demonstrated the potential of quantitative digital histopathology features integrated with ML methods in predicting BC response to NAC. This study is a step forward towards precision oncology for BC patients to potentially guide future therapies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35145158,

title = {Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy},

author = {Hamidreza Taleghamar and Seyed Ali Jalalifar and Gregory J Czarnota and Ali Sadeghi-Naini},

doi = {10.1038/s41598-022-06100-2},

issn = {2045-2322},

year  = {2022},

date = {2022-02-01},

journal = {Sci Rep},

volume = {12},

number = {1},

pages = {2244},

abstract = {In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the quantitative ultrasound (QUS) multi-parametric imaging at pre-treatment. QUS multi-parametric images of breast tumors were generated using the data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for NAC followed by surgery. The ground truth response to NAC was identified for each patient after the surgery using the standard clinical and pathological criteria. Two deep convolutional neural network (DCNN) architectures including the residual network and residual attention network (RAN) were explored for extracting optimal feature maps from the parametric images, with a fully connected network for response prediction. In different experiments, the features maps were derived from the tumor core only, as well as the core and its margin. Evaluation results on an independent test set demonstrate that the developed model with the RAN architecture to extract feature maps from the expanded parametric images of the tumor core and margin had the best performance in response prediction with an accuracy of 88% and an area under the receiver operating characteristic curve of 0.86. Ten-year survival analyses indicate statistically significant differences between the survival of the responders and non-responders identified based on the model prediction at pre-treatment and the standard criteria at post-treatment. The results of this study demonstrate the promising capability of DCNNs with attention mechanisms in predicting breast cancer response to NAC prior to the start of treatment using QUS multi-parametric images.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid34917262,

title = {Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound},

author = {Archya Dasgupta and Divya Bhardwaj and Daniel DiCenzo and Kashuf Fatima and Laurentius Oscar Osapoetra and Karina Quiaoit and Murtuza Saifuddin and Stephen Brade and Maureen Trudeau and Sonal Gandhi and Andrea Eisen and Frances Wright and Nicole Look-Hong and Ali Sadeghi-Naini and Belinda Curpen and Michael C Kolios and Lakshmanan Sannachi and Gregory J Czarnota},

doi = {10.18632/oncotarget.28139},

issn = {1949-2553},

year  = {2021},

date = {2021-12-01},

journal = {Oncotarget},

volume = {12},

number = {25},

pages = {2437--2448},

abstract = {BACKGROUND: The purpose of the study was to investigate the role of pre-treatment quantitative ultrasound (QUS)-radiomics in predicting recurrence for patients with locally advanced breast cancer (LABC).



MATERIALS AND METHODS: A prospective study was conducted in patients with LABC ( = 83). Primary tumours were scanned using a clinical ultrasound device before starting treatment. Ninety-five imaging features were extracted-spectral features, texture, and texture-derivatives. Patients were determined to have recurrence or no recurrence based on clinical outcomes. Machine learning classifiers with k-nearest neighbour (KNN) and support vector machine (SVM) were evaluated for model development using a maximum of 3 features and leave-one-out cross-validation.



RESULTS: With a median follow up of 69 months (range 7-118 months), 28 patients had disease recurrence (local or distant). The best classification results were obtained using an SVM classifier with a sensitivity, specificity, accuracy and area under curve of 71%, 87%, 82%, and 0.76, respectively. Using the SVM model for the predicted non-recurrence and recurrence groups, the estimated 5-year recurrence-free survival was 83% and 54% ( = 0.003), and the predicted 5-year overall survival was 85% and 74% ( = 0.083), respectively.



CONCLUSIONS: A QUS-radiomics model using higher-order texture derivatives can identify patients with LABC at higher risk of disease recurrence before starting treatment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid34732781,

title = {A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning},

author = {Majid Jaberipour and Hany Soliman and Arjun Sahgal and Ali Sadeghi-Naini},

doi = {10.1038/s41598-021-01024-9},

issn = {2045-2322},

year  = {2021},

date = {2021-11-01},

journal = {Sci Rep},

volume = {11},

number = {1},

pages = {21620},

abstract = {This study investigated the effectiveness of pre-treatment quantitative MRI and clinical features along with machine learning techniques to predict local failure in patients with brain metastasis treated with hypo-fractionated stereotactic radiation therapy (SRT). The predictive models were developed using the data from 100 patients (141 lesions) and evaluated on an independent test set with data from 20 patients (30 lesions). Quantitative MRI radiomic features were derived from the treatment-planning contrast-enhanced T1w and T2-FLAIR images. A multi-phase feature reduction and selection procedure was applied to construct an optimal quantitative MRI biomarker for predicting therapy outcome. The performance of standard clinical features in therapy outcome prediction was evaluated using a similar procedure. Survival analyses were conducted to compare the long-term outcome of the two patient cohorts (local control/failure) identified based on prediction at pre-treatment, and standard clinical criteria at last patient follow-up after SRT. The developed quantitative MRI biomarker consists of four features with two features quantifying heterogeneity in the edema region, one feature characterizing intra-tumour heterogeneity, and one feature describing tumour morphology. The predictive models with the radiomic and clinical feature sets yielded an AUC of 0.87 and 0.62, respectively on the independent test set. Incorporating radiomic features into the clinical predictive model improved the AUC of the model by up to 16%, relatively. A statistically significant difference was observed in survival of the two patient cohorts identified at pre-treatment using the radiomics-based predictive model, and at post-treatment using the the RANO-BM criteria. Results of this study revealed a good potential for quantitative MRI radiomic features at pre-treatment in predicting local failure in relatively large brain metastases undergoing SRT, and is a step forward towards a precision oncology paradigm for brain metastasis.},

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pubstate = {published},

tppubtype = {article}

}

@article{pmid34898544,

title = {Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade},

author = {Andrew Lagree and Audrey Shiner and Marie Angeli Alera and Lauren Fleshner and Ethan Law and Brianna Law and Fang-I Lu and David Dodington and Sonal Gandhi and Elzbieta A Slodkowska and Alex Shenfield and Katarzyna J Jerzak and Ali Sadeghi-Naini and William T Tran},

doi = {10.3390/curroncol28060366},

issn = {1718-7729},

year  = {2021},

date = {2021-10-01},

journal = {Curr Oncol},

volume = {28},

number = {6},

pages = {4298--4316},

abstract = {BACKGROUND: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence.



METHODS: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis.



RESULTS: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836.



CONCLUSION: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid34293685,

title = {Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning},

author = {Hadi Moghadas-Dastjerdi and Shan-E-Tallat Hira Rahman and Lakshmanan Sannachi and Frances C Wright and Sonal Gandhi and Maureen E Trudeau and Ali Sadeghi-Naini and Gregory J Czarnota},

doi = {10.1016/j.tranon.2021.101183},

issn = {1936-5233},

year  = {2021},

date = {2021-10-01},

journal = {Transl Oncol},

volume = {14},

number = {10},

pages = {101183},

abstract = {Although neoadjuvant chemotherapy (NAC) is a crucial component of treatment for locally advanced breast cancer (LABC), only about 70% of patients respond to it. Effective adjustment of NAC for individual patients can significantly improve survival rates of those resistant to standard regimens. Thus, the early prediction of NAC outcome is of great importance in facilitating a personalized paradigm for breast cancer therapeutics. In this study, quantitative computed tomography (qCT) parametric imaging in conjunction with machine learning techniques were investigated to predict LABC tumor response to NAC. Textural and second derivative textural (SDT) features of CT images of 72 patients diagnosed with LABC were analysed before the initiation of NAC to quantify intra-tumor heterogeneity. These quantitative features were processed through a correlation-based feature reduction followed by a sequential feature selection with a bootstrap 0.632+ area under the receiver operating characteristic (ROC) curve (AUC) criterion. The best feature subset consisted of a combination of one textural and three SDT features. Using these features, an AdaBoost decision tree could predict the patient response with a cross-validated AUC accuracy, sensitivity and specificity of 0.88, 85%, 88% and 75%, respectively. This study demonstrates, for the first time, that a combination of textural and SDT features of CT images can be used to predict breast cancer response NAC prior to the start of treatment which can potentially facilitate early therapy adjustments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid33710956,

title = {Combining First- and Second-Order Continuity Constraints in Ultrasound Elastography},

author = {Md Ashikuzzaman and Ali Sadeghi-Naini and Abbas Samani and Hassan Rivaz},

doi = {10.1109/TUFFC.2021.3065884},

issn = {1525-8955},

year  = {2021},

date = {2021-07-01},

journal = {IEEE Trans Ultrason Ferroelectr Freq Control},

volume = {68},

number = {7},

pages = {2407--2418},

abstract = {Ultrasound elastography is a prominent noninvasive medical imaging technique that estimates tissue elastic properties to detect abnormalities in an organ. A common approximation to tissue elastic modulus is tissue strain induced after mechanical stimulation. To compute tissue strain, ultrasound radio frequency (RF) data can be processed using energy-based algorithms. These algorithms suffer from ill-posedness to tackle. A continuity constraint along with the data amplitude similarity is imposed to obtain a unique solution to the time-delay estimation (TDE) problem. Existing energy-based methods exploit the first-order spatial derivative of the displacement field to construct a regularizer. This first-order regularization scheme alone is not fully consistent with the mechanics of tissue deformation while perturbed with an external force. As a consequence, state-of-the-art techniques suffer from two crucial drawbacks. First, the strain map is not sufficiently smooth in uniform tissue regions. Second, the edges of the hard or soft inclusions are not well-defined in the image. Herein, we address these issues by formulating a novel regularizer taking both first- and second-order derivatives of the displacement field into account. The second-order constraint, which is the principal novelty of this work, contributes both to background continuity and edge sharpness by suppressing spurious noisy edges and enhancing strong boundaries. We name the proposed technique: Second-Order Ultrasound eLastography (SOUL). Comparative assessment of qualitative and quantitative results shows that SOUL substantially outperforms three recently developed TDE algorithms called Hybrid, GLUE, and MPWC-Net++. SOUL yields 27.72%, 62.56%, and 81.37% improvements of the signal-to-noise ratio (SNR) and 72.35%, 54.03%, and 65.17% improvements of the contrast-to-noise ratio (CNR) over GLUE with data pertaining to simulation, phantom, and in vivo tissue, respectively. The SOUL code can be downloaded from code.sonography.ai.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid34290259,

title = {Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment},

author = {Hamidreza Taleghamar and Hadi Moghadas-Dastjerdi and Gregory J Czarnota and Ali Sadeghi-Naini},

doi = {10.1038/s41598-021-94004-y},

issn = {2045-2322},

year  = {2021},

date = {2021-07-01},

journal = {Sci Rep},

volume = {11},

number = {1},

pages = {14865},

abstract = {The efficacy of quantitative ultrasound (QUS) multi-parametric imaging in conjunction with unsupervised classification algorithms was investigated for the first time in characterizing intra-tumor regions to predict breast tumor response to chemotherapy before the start of treatment. QUS multi-parametric images of breast tumors were generated using the ultrasound radiofrequency data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for neo-adjuvant chemotherapy followed by surgery. A hidden Markov random field (HMRF) expectation maximization (EM) algorithm was applied to identify distinct intra-tumor regions on QUS multi-parametric images. Several features were extracted from the segmented intra-tumor regions and tumor margin on different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker consisting of four features for response prediction. Evaluation results on an independent test set indicated that the developed biomarker coupled with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patient at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In comparison, the biomarkers consisted of the features derived from the entire tumor core (without consideration of the intra-tumor regions), and the entire tumor core and the tumor margin could predict the treatment response of patients with an accuracy of 74.5% and 76.4%, and an AUC of 0.79 and 0.76, respectively. Standard clinical features could predict the therapy response with an accuracy of 69.1% and an AUC of 0.6. Long-term survival analyses indicated that the patients predicted by the developed model as responders had a significantly better survival compared to the non-responders. Similar findings were observed for the two response cohorts identified at post-treatment based on standard clinical and pathological criteria. The results obtained in this study demonstrated the potential of QUS multi-parametric imaging integrated with unsupervised learning methods in identifying distinct intra-tumor regions in breast cancer to characterize its responsiveness to chemotherapy prior to the start of treatment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid34262646,

title = {MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer},

author = {Christopher Kolios and Lakshmanan Sannachi and Archya Dasgupta and Harini Suraweera and Daniel DiCenzo and Gregory Stanisz and Arjun Sahgal and Frances Wright and Nicole Look-Hong and Belinda Curpen and Ali Sadeghi-Naini and Maureen Trudeau and Sonal Gandhi and Michael C Kolios and Gregory J Czarnota},

doi = {10.18632/oncotarget.28002},

issn = {1949-2553},

year  = {2021},

date = {2021-07-01},

journal = {Oncotarget},

volume = {12},

number = {14},

pages = {1354--1365},

abstract = {BACKGROUND: Radiomics involving quantitative analysis of imaging has shown promises in oncology to serve as non-invasive biomarkers. We investigated whether pre-treatment T2-weighted magnetic resonance imaging (MRI) can be used to predict response to neoadjuvant chemotherapy (NAC) in breast cancer.



MATERIALS AND METHODS: MRI scans were obtained for 102 patients with locally advanced breast cancer (LABC). All patients were treated with standard regimens of NAC as decided by the treating oncologist, followed by surgery and adjuvant treatment according to standard institutional practice. The primary tumor was segmented, and 11 texture features were extracted using the grey-level co-occurrence matrices analysis of the T2W-images from tumor cores and margins. Response assessment was done using clinical-pathological responses with patients classified into binary groups: responders and non-responders. Machine learning classifiers were used to develop a radiomics model, and a leave-one-out cross-validation technique was used to assess the performance.



RESULTS: 7 features were significantly ( < 0.05) different between the two response groups. The best classification accuracy was obtained using a k-nearest neighbor (kNN) model with sensitivity, specificity, accuracy, and area under curve of 63, 93, 87, and 0.78, respectively.



CONCLUSIONS: Pre-treatment T2-weighted MRI texture features can predict NAC response with reasonable accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid33850222,

title = {A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks},

author = {Andrew Lagree and Majidreza Mohebpour and Nicholas Meti and Khadijeh Saednia and Fang-I Lu and Elzbieta Slodkowska and Sonal Gandhi and Eileen Rakovitch and Alex Shenfield and Ali Sadeghi-Naini and William T Tran},

doi = {10.1038/s41598-021-87496-1},

issn = {2045-2322},

year  = {2021},

date = {2021-04-01},

journal = {Sci Rep},

volume = {11},

number = {1},

pages = {8025},

abstract = {Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of the specimen and the experience of the pathologist. This study's objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. Various deep convolutional neural networks were evaluated for the study, including U-Net, Mask R-CNN, and a novel network (GB U-Net). The networks were trained on a set of Hematoxylin and Eosin (H&E)-stained images of eight diverse types of tissues. GB U-Net demonstrated superior performance in segmenting sites of invasive diseases (AJI = 0.53, mAP = 0.39 & AJI = 0.54, mAP = 0.38), validated on two hold-out datasets exclusively containing breast tissue images of approximately 7,582 annotated cells. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid33486639,

title = {Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients},

author = {David W Dodington and Andrew Lagree and Sami Tabbarah and Majid Mohebpour and Ali Sadeghi-Naini and William T Tran and Fang-I Lu},

doi = {10.1007/s10549-020-06093-4},

issn = {1573-7217},

year  = {2021},

date = {2021-04-01},

journal = {Breast Cancer Res Treat},

volume = {186},

number = {2},

pages = {379--389},

abstract = {PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat patients with high-risk breast cancer. The tumor response to NAC can be classified as either a pathological partial response (pPR) or pathological complete response (pCR), defined as complete eradication of invasive tumor cells, with a pCR conferring a significantly lower risk of recurrence. Predicting the response to NAC, however, remains a significant clinical challenge. The objective of this study was to determine if analysis of nuclear features on core biopsies using artificial intelligence (AI) can predict response to NAC.



METHODS: Fifty-eight HER2-positive or triple-negative breast cancer patients were included in this study (pCR n = 37, pPR n = 21). Multiple deep convolutional neural networks were developed to automate tumor detection and nuclear segmentation. Nuclear count, area, and circularity, as well as image-based first- and second-order features including mean pixel intensity and correlation of the gray-level co-occurrence matrix (GLCM-COR) were determined.



RESULTS: In univariate analysis, the pCR group had fewer multifocal/multicentric tumors, higher nuclear intensity, and lower GLCM-COR compared to the pPR group. In multivariate binary logistic regression, tumor multifocality/multicentricity (OR = 0.14, p = 0.012), nuclear intensity (OR = 1.23, p = 0.018), and GLCM-COR (OR = 0.96, p = 0.043) were each independently associated with likelihood of achieving a pCR, and the model was able to successful classify 79% of cases (62% for pPR and 89% for pCR).



CONCLUSION: Analysis of tumor nuclear features using digital pathology/AI can significantly improve models to predict pathological response to NAC.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid33524903,

title = {In-vivo lung biomechanical modeling for effective tumor motion tracking in external beam radiation therapy},

author = {Parya Jafari and Sergio Dempsey and Douglas A Hoover and Elham Karami and Stewart Gaede and Ali Sadeghi-Naini and Ting Yim Lee and Abbas Samani},

doi = {10.1016/j.compbiomed.2021.104231},

issn = {1879-0534},

year  = {2021},

date = {2021-03-01},

journal = {Comput Biol Med},

volume = {130},

pages = {104231},

abstract = {Lung cancer is the most common cause of cancer-related death in both men and women. Radiation therapy is widely used for lung cancer treatment; however, respiratory motion presents challenges that can compromise the accuracy and/or effectiveness of radiation treatment. Respiratory motion compensation using biomechanical modeling is a common approach used to address this challenge. This study focuses on the development and validation of a lung biomechanical model that can accurately estimate the motion and deformation of lung tumor. Towards this goal, treatment planning 4D-CT images of lung cancer patients were processed to develop patient-specific finite element (FE) models of the lung to predict the patients' tumor motion/deformation. The tumor motion/deformation was modeled for a full respiration cycle, as captured by the 4D-CT scans. Parameters driving the lung and tumor deformation model were found through an inverse problem formulation. The CT datasets pertaining to the inhalation phases of respiration were used for validating the model's accuracy. The volumetric Dice similarity coefficient between the actual and simulated gross tumor volumes (GTVs) of the patients calculated across respiration phases was found to range between 0.80 ± 0.03 and 0.92 ± 0.01. The average error in estimating tumor's center of mass calculated across respiration phases ranged between 0.50 ± 0.10 (mm) and 1.04 ± 0.57 (mm), indicating a reasonably good accuracy of the proposed model. The proposed model demonstrates favorable accuracy for estimating the lung tumor motion/deformation, and therefore can potentially be used in radiation therapy applications for respiratory motion compensation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32865001,

title = {Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence},

author = {William T Tran and Ali Sadeghi-Naini and Fang-I Lu and Sonal Gandhi and Nicholas Meti and Muriel Brackstone and Eileen Rakovitch and Belinda Curpen},

doi = {10.1177/0846537120949974},

issn = {1488-2361},

year  = {2021},

date = {2021-02-01},

journal = {Can Assoc Radiol J},

volume = {72},

number = {1},

pages = {98--108},

abstract = {Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-field digital mammography (FFDM) has replaced traditional analog mammography, and this has opened new opportunities for developing computational frameworks to automate detection and diagnosis. Artificial intelligence (AI), and its subdomain of deep learning, is showing promising results and improvements on diagnostic accuracy, compared to previous computer-based methods, known as computer-aided detection and diagnosis.In this commentary, we review the current status of computational radiology, with a focus on deep neural networks used in breast cancer screening and diagnosis. Recent studies are developing a new generation of computer-aided detection and diagnosis systems, as well as leveraging AI-driven tools to efficiently interpret digital mammograms, and breast tomosynthesis imaging. The use of AI in computational radiology necessitates transparency and rigorous testing. However, the overall impact of AI to radiology workflows will potentially yield more efficient and standardized processes as well as improve the level of care to patients with high diagnostic accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid33439725,

title = {Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features},

author = {Nicholas Meti and Khadijeh Saednia and Andrew Lagree and Sami Tabbarah and Majid Mohebpour and Alex Kiss and Fang-I Lu and Elzbieta Slodkowska and Sonal Gandhi and Katarzyna Joanna Jerzak and Lauren Fleshner and Ethan Law and Ali Sadeghi-Naini and William T Tran},

doi = {10.1200/CCI.20.00078},

issn = {2473-4276},

year  = {2021},

date = {2021-01-01},

journal = {JCO Clin Cancer Inform},

volume = {5},

pages = {66--80},

abstract = {PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and high-risk early breast cancer (BC). Pathological complete response (pCR) has prognostic value depending on BC subtype. Rates of pCR, however, can be variable. Predictive modeling is desirable to help identify patients early who may have suboptimal NAC response. Here, we test and compare the predictive performances of machine learning (ML) prediction models to a standard statistical model, using clinical and pathological data.



METHODS: Clinical and pathological variables were collected in 431 patients, including tumor size, patient demographics, histological characteristics, molecular status, and staging information. A standard multivariable logistic regression (MLR) was developed and compared with five ML models: k-nearest neighbor classifier, random forest (RF) classifier, naive Bayes algorithm, support vector machine, and multilayer perceptron model. Model performances were measured using a receiver operating characteristic (ROC) analysis and statistically compared.



RESULTS: MLR predictors of NAC response included: estrogen receptor (ER) status, human epidermal growth factor-2 (HER2) status, tumor size, and Nottingham grade. The strongest MLR predictors of pCR included HER2+ versus HER2- BC (odds ratio [OR], 0.13; 95% CI, 0.07 to 0.23;  < .001) and Nottingham grade G3 versus G1-2 (G1-2: OR, 0.36; 95% CI, 0.20 to 0.65;  < .001). The area under the curve (AUC) for the MLR was AUC = 0.64. Among the various ML models, an RF classifier performed best, with an AUC = 0.88, sensitivity of 70.7%, and specificity of 84.6%, and included the following variables: menopausal status, ER status, HER2 status, Nottingham grade, tumor size, nodal status, and presence of inflammatory BC.



CONCLUSION: Modeling performances varied between standard versus ML classification methods. RF ML classifiers demonstrated the best predictive performance among all models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid33144919,

title = {Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer},

author = {Archya Dasgupta and Stephen Brade and Lakshmanan Sannachi and Karina Quiaoit and Kashuf Fatima and Daniel DiCenzo and Laurentius O Osapoetra and Murtuza Saifuddin and Maureen Trudeau and Sonal Gandhi and Andrea Eisen and Frances Wright and Nicole Look-Hong and Ali Sadeghi-Naini and William T Tran and Belinda Curpen and Gregory J Czarnota},

doi = {10.18632/oncotarget.27742},

issn = {1949-2553},

year  = {2020},

date = {2020-10-01},

journal = {Oncotarget},

volume = {11},

number = {42},

pages = {3782--3792},

abstract = {BACKGROUND: To investigate quantitative ultrasound (QUS) based higher-order texture derivatives in predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).



MATERIALS AND METHODS: 100 Patients with LABC were scanned before starting NAC. Five QUS parametric image-types were generated from radio-frequency data over the tumor volume. From each QUS parametric-image, 4 grey level co-occurrence matrix-based texture images were derived (20 QUS-Tex), which were further processed to create texture derivatives (80 QUS-Tex-Tex). Patients were classified into responders and non-responders based on clinical/pathological responses to treatment. Three machine learning algorithms based on linear discriminant (FLD), -nearest-neighbors (KNN), and support vector machine (SVM) were used for developing radiomic models of response prediction.



RESULTS: A KNN-model provided the best results with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 81%, 82%, and 0.86, respectively. The most helpful features in separating the two response groups were QUS-Tex-Tex features. The 5-year recurrence-free survival (RFS) calculated for KNN predicted responders and non-responders using QUS-Tex-Tex model were comparable to RFS for the actual response groups.



CONCLUSIONS: We report the first study demonstrating QUS texture-derivative methods in predicting NAC responses in LABC, which leads to better results compared to using texture features alone.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid33235811,

title = {Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies},

author = {William T Tran and Harini Suraweera and Karina Quiaoit and Daniel DiCenzo and Kashuf Fatima and Deok Jang and Divya Bhardwaj and Christopher Kolios and Irene Karam and Ian Poon and Lakshmanan Sannachi and Mehrdad Gangeh and Ali Sadeghi-Naini and Archya Dasgupta and Gregory J Czarnota},

doi = {10.2144/fsoa-2020-0073},

issn = {2056-5623},

year  = {2020},

date = {2020-09-01},

journal = {Future Sci OA},

volume = {6},

number = {9},

pages = {FSO624},

abstract = {AIM: We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT).



MATERIALS & METHODS: QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders.



RESULTS: Single-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively.



CONCLUSION: QUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32602222,

title = {Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi-institutional study},

author = {Daniel DiCenzo and Karina Quiaoit and Kashuf Fatima and Divya Bhardwaj and Lakshmanan Sannachi and Mehrdad Gangeh and Ali Sadeghi-Naini and Archya Dasgupta and Michael C Kolios and Maureen Trudeau and Sonal Gandhi and Andrea Eisen and Frances Wright and Nicole Look Hong and Arjun Sahgal and Greg Stanisz and Christine Brezden and Robert Dinniwell and William T Tran and Wei Yang and Belinda Curpen and Gregory J Czarnota},

doi = {10.1002/cam4.3255},

issn = {2045-7634},

year  = {2020},

date = {2020-08-01},

journal = {Cancer Med},

volume = {9},

number = {16},

pages = {5798--5806},

abstract = {BACKGROUND: This study was conducted in order to develop a model for predicting response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) using pretreatment quantitative ultrasound (QUS) radiomics.



METHODS: This was a multicenter study involving four sites across North America, and appropriate approval was obtained from the individual ethics committees. Eighty-two patients with LABC were included for final analysis. Primary tumors were scanned using a clinical ultrasound system before NAC was started. The tumors were contoured, and radiofrequency data were acquired and processed from whole tumor regions of interest. QUS spectral parameters were derived from the normalized power spectrum, and texture analysis was performed based on six QUS features using a gray level co-occurrence matrix. Patients were divided into responder or nonresponder classes based on their clinical-pathological response. Classification analysis was performed using machine learning algorithms, which were trained to optimize classification accuracy. Cross-validation was performed using a leave-one-out cross-validation method.



RESULTS: Based on the clinical outcomes of NAC treatment, there were 48 responders and 34 nonresponders. A K-nearest neighbors (K-NN) approach resulted in the best classifier performance, with a sensitivity of 91%, a specificity of 83%, and an accuracy of 87%.



CONCLUSION: QUS-based radiomics can predict response to NAC based on pretreatment features with acceptable accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid33018409,

title = {Analytical Estimation of Out-of-plane Strain in Ultrasound Elastography to Improve Axial and Lateral Displacement Fields},

author = {Niusha Kheirkhah and Ali Sadeghi-Naini and Abbas Samani},

doi = {10.1109/EMBC44109.2020.9176086},

issn = {2694-0604},

year  = {2020},

date = {2020-07-01},

journal = {Annu Int Conf IEEE Eng Med Biol Soc},

volume = {2020},

pages = {2055--2058},

abstract = {Many types of cancers are associated with changes in tissue mechanical properties. This has led to the development of elastography as a clinically viable method where tissue mechanical properties are mapped and visualized for cancer detection and staging. In quasi-static ultrasound elastography, a mechanical stimulation is applied to the tissue using ultrasound probe. Using ultrasound radiofrequency (RF) data acquired before and after the stimulation, the tissue displacement field can be estimated. Elasticity image reconstruction algorithms use this displacement data to generate images of the tissue elasticity properties. The accuracy of the generated elasticity images depends highly on the accuracy of the tissue displacement estimation. Tissue incompressibility can be used as a constraint to improve the estimation of axial and, more importantly, the lateral displacements in 2D ultrasound elastography. Especially in clinical applications, this requires accurate estimation of the out-of-plane strain. Here, we propose a method for providing an accurate estimate of the out-of-plane strain which is incorporated in the incompressibility equation to improve the axial and lateral displacements estimation before elastography image reconstruction. The method was validated using in silico and tissue mimicking phantom studies, leading to significant improvement in the estimated displacement.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32616912,

title = {A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning},

author = {Hadi Moghadas-Dastjerdi and Hira Rahman Sha-E-Tallat and Lakshmanan Sannachi and Ali Sadeghi-Naini and Gregory J Czarnota},

doi = {10.1038/s41598-020-67823-8},

issn = {2045-2322},

year  = {2020},

date = {2020-07-01},

journal = {Sci Rep},

volume = {10},

number = {1},

pages = {10936},

abstract = {Response to Neoadjuvant chemotherapy (NAC) has demonstrated a high correlation to survival in locally advanced breast cancer (LABC) patients. An early prediction of responsiveness to NAC could facilitate treatment adjustments on an individual patient basis that would be expected to improve treatment outcomes and patient survival. This study investigated, for the first time, the efficacy of quantitative computed tomography (qCT) parametric imaging to characterize intra-tumour heterogeneity and its application in predicting tumour response to NAC in LABC patients. Textural analyses were performed on CT images acquired from 72 patients before the start of chemotherapy to determine quantitative features of intra-tumour heterogeneity. The best feature subset for response prediction was selected through a sequential feature selection with bootstrap 0.632 + area under the receiver operating characteristic (ROC) curve ([Formula: see text]) as a performance criterion. Several classifiers were evaluated for response prediction using the selected feature subset. Amongst the applied classifiers an Adaboost decision tree provided the best results with cross-validated [Formula: see text], accuracy, sensitivity and specificity of 0.89, 84%, 80% and 88%, respectively. The promising results obtained in this study demonstrate the potential of the proposed biomarkers to be used as predictors of LABC tumour response to NAC prior to the start of treatment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32111456,

title = {Quantitative Ultrasound Monitoring of Breast Tumour Response to Neoadjuvant Chemotherapy: Comparison of Results Among Clinical Scanners},

author = {Lakshmanan Sannachi and Mehrdad Gangeh and Ali-Sadeghi Naini and Priya Bhargava and Aparna Jain and William Tyler Tran and Gregory Jan Czarnota},

doi = {10.1016/j.ultrasmedbio.2020.01.022},

issn = {1879-291X},

year  = {2020},

date = {2020-05-01},

journal = {Ultrasound Med Biol},

volume = {46},

number = {5},

pages = {1142--1157},

abstract = {Quantitative ultrasound (QUS) techniques have been demonstrated to detect cell death in vitro and in vivo. Recently, multi-feature classification models have been incorporated into QUS texture-feature analysis methods to increase further the sensitivity and specificity of detecting treatment response in locally advanced breast cancer patients. To effectively incorporate these analytic methods into clinical applications, QUS and texture-feature estimations should be independent of data acquisition systems. The study here investigated the consistencies of QUS and texture-feature estimation techniques relative to several factors. These included the ultrasound system properties, the effects of tissue heterogeneity and the effects of these factors on the monitoring of response to neoadjuvant chemotherapy. Specifically, tumour-response-detection performance based on QUS and texture parameters using two clinical ultrasound systems was compared. Observed variations in data between the systems were small and the results exhibited good agreement in tumour response predictions obtained from both ultrasound systems. The results obtained in this study suggest that tissue heterogeneity was a dominant feature in the parameters measured with the two different ultrasound systems; whereas differences in ultrasound system beam properties only exhibited a minor impact on texture features. The McNemar statistical test performed on tumour response prediction results from the two systems did not reveal significant differences. Overall, the results in this study demonstrate the potential to achieve reliable and consistent QUS and texture-based analyses across different ultrasound imaging platforms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32415137,

title = {An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI},

author = {Wilfred W Lam and Wendy Oakden and Elham Karami and Margaret M Koletar and Leedan Murray and Stanley K Liu and Ali Sadeghi-Naini and Greg J Stanisz},

doi = {10.1038/s41598-020-64912-6},

issn = {2045-2322},

year  = {2020},

date = {2020-05-01},

journal = {Sci Rep},

volume = {10},

number = {1},

pages = {8063},

abstract = {Saturation transfer MRI can be useful in the characterization of different tumour types. It is sensitive to tumour metabolism, microstructure, and microenvironment. This study aimed to use saturation transfer to differentiate between intratumoural regions, demarcate tumour boundaries, and reduce data acquisition times by identifying the imaging scheme with the most impact on segmentation accuracy. Saturation transfer-weighted images were acquired over a wide range of saturation amplitudes and frequency offsets along with T and T maps for 34 tumour xenografts in mice. Independent component analysis and Gaussian mixture modelling were used to segment the images and identify intratumoural regions. Comparison between the segmented regions and histopathology indicated five distinct clusters: three corresponding to intratumoural regions (active tumour, necrosis/apoptosis, and blood/edema) and two extratumoural (muscle and a mix of muscle and connective tissue). The fraction of tumour voxels segmented as necrosis/apoptosis quantitatively matched those calculated from TUNEL histopathological assays. An optimal protocol was identified providing reasonable qualitative agreement between MRI and histopathology and consisting of T and T maps and 22 magnetization transfer (MT)-weighted images. A three-image subset was identified that resulted in a greater than 90% match in positive and negative predictive value of tumour voxels compared to those found using the entire 24-image dataset. The proposed algorithm can potentially be used to develop a robust intratumoural segmentation method.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid31982495,

title = {Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine Learning},

author = {Khadijeh Saednia and Sami Tabbarah and Andrew Lagree and Tina Wu and Jonathan Klein and Eduardo Garcia and Michael Hall and Edward Chow and Eileen Rakovitch and Charmaine Childs and Ali Sadeghi-Naini and William T Tran},

doi = {10.1016/j.ijrobp.2019.12.032},

issn = {1879-355X},

year  = {2020},

date = {2020-04-01},

journal = {Int J Radiat Oncol Biol Phys},

volume = {106},

number = {5},

pages = {1071--1083},

abstract = {PURPOSE: Radiation-induced dermatitis is a common side effect of breast radiation therapy (RT). Current methods to evaluate breast skin toxicity include clinical examination, visual inspection, and patient-reported symptoms. Physiological changes associated with radiation-induced dermatitis, such as inflammation, may also increase body-surface temperature, which can be detected by thermal imaging. Quantitative thermal imaging markers were identified and used in supervised machine learning to develop a predictive model for radiation dermatitis.



METHODS AND MATERIALS: Ninety patients treated for adjuvant whole-breast RT (4250 cGy/f = 16) were recruited for the study. Thermal images of the treated breast were taken at 4 intervals: before RT, then weekly at f = 5, f = 10, and f = 15. Parametric thermograms were analyzed and yielded 26 thermal-based features that included surface temperature (°C) and texture parameters obtained from (1) gray-level co-occurrence matrix, (2) gray-level run-length matrix, and (3) neighborhood gray-tone difference matrix. Skin toxicity was evaluated at the end of RT using the Common Terminology Criteria for Adverse Events (CTCAE) guidelines (Ver.5). Binary group classes were labeled according to a CTCAE cut-off score of ≥2, and thermal features obtained at f = 5 were used for supervised machine learning to predict skin toxicity. The data set was partitioned for model training, independent testing, and validation. Fifteen patients (∼17% of the whole data set) were randomly selected as an unseen test data set, and 75 patients (∼83% of the whole data set) were used for training and validation of the model. A random forest classifier with leave-1-patient-out cross-validation was employed for modeling single and hybrid parameters. The model performance was reported using receiver operating characteristic analysis on patients from an independent test set.



RESULTS: Thirty-seven patients presented with adverse skin effects, denoted by a CTCAE score ≥2, and had significantly higher local increases in skin temperature, reaching 36.06°C at f = 10 (P = .029). However, machine-learning models demonstrated early thermal signals associated with skin toxicity after the fifth RT fraction. The cross-validated model showed high prediction accuracy on the independent test data (test accuracy = 0.87) at f = 5 for predicting skin toxicity at the end of RT.



CONCLUSIONS: Early thermal markers after 5 fractions of RT are predictive of radiation-induced skin toxicity in breast RT.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32716959,

title = {Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results},

author = {Karina Quiaoit and Daniel DiCenzo and Kashuf Fatima and Divya Bhardwaj and Lakshmanan Sannachi and Mehrdad Gangeh and Ali Sadeghi-Naini and Archya Dasgupta and Michael C Kolios and Maureen Trudeau and Sonal Gandhi and Andrea Eisen and Frances Wright and Nicole Look-Hong and Arjun Sahgal and Greg Stanisz and Christine Brezden and Robert Dinniwell and William T Tran and Wei Yang and Belinda Curpen and Gregory J Czarnota},

doi = {10.1371/journal.pone.0236182},

issn = {1932-6203},

year  = {2020},

date = {2020-01-01},

journal = {PLoS One},

volume = {15},

number = {7},

pages = {e0236182},

abstract = {BACKGROUND: Neoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting.



METHODS: Fifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA), and Princess Margaret Cancer Centre (Toronto, Canada)). QUS data were collected before starting NAC and subsequently at weeks 1 and 4 during chemotherapy. Spectral tumour parametric maps were generated, and textural features determined using grey-level co-occurrence matrices. Patients were divided into two groups based on their pathological outcomes following surgery: responders and non-responders. Machine learning algorithms using Fisher's linear discriminant (FLD), K-nearest neighbour (K-NN), and support vector machine (SVM-RBF) were used to generate response classification models.



RESULTS: Thirty-six patients were classified as responders and twenty-three as non-responders. Among all the models, SVM-RBF had the highest accuracy of 81% at both weeks 1 and week 4 with area under curve (AUC) values of 0.87 each. The inclusion of week 1 and 4 features led to an improvement of the classifier models, with the accuracy and AUC from baseline features only being 76% and 0.68, respectively.



CONCLUSION: QUS data obtained during NAC reflect the ongoing treatment-related changes during chemotherapy and can lead to better classifier performances in predicting the ultimate pathologic response to treatment compared to baseline features alone.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid31882597,

title = {Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis},

author = {Elham Karami and Hany Soliman and Mark Ruschin and Arjun Sahgal and Sten Myrehaug and Chia-Lin Tseng and Gregory J Czarnota and Pejman Jabehdar-Maralani and Brige Chugh and Angus Lau and Greg J Stanisz and Ali Sadeghi-Naini},

doi = {10.1038/s41598-019-56185-5},

issn = {2045-2322},

year  = {2019},

date = {2019-12-01},

journal = {Sci Rep},

volume = {9},

number = {1},

pages = {19830},

abstract = {About 20-40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value < 0.0001) and overall survival (p = 0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid31447230,

title = {Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics},

author = {William T Tran and Katarzyna Jerzak and Fang-I Lu and Jonathan Klein and Sami Tabbarah and Andrew Lagree and Tina Wu and Ivan Rosado-Mendez and Ethan Law and Khadijeh Saednia and Ali Sadeghi-Naini},

doi = {10.1016/j.jmir.2019.07.010},

issn = {1876-7982},

year  = {2019},

date = {2019-12-01},

journal = {J Med Imaging Radiat Sci},

volume = {50},

number = {4 Suppl 2},

pages = {S32--S41},

abstract = {Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities are growing in medical specialties, such as radiology, pathology, and oncology. As medical imaging and pathology are becoming increasingly digitized, it is recently recognized that harnessing data from digital images can yield parameters that reflect the underlying biology and physiology of various malignancies. This greater understanding of the behaviour of cancer can potentially improve on therapeutic strategies. In addition, the use of AI is particularly appealing in oncology to facilitate the detection of malignancies, to predict the likelihood of tumor response to treatments, and to prognosticate the patients' risk of cancer-related mortality. AI will be critical for identifying candidate biomarkers from digital imaging and developing robust and reliable predictive models. These models will be used to personalize oncologic treatment strategies, and identify confounding variables that are related to the complex biology of tumors and diversity of patient-related factors (ie, mining "big data"). This commentary describes the growing body of work focussed on AI for precision oncology. Advances in AI-driven computer vision and machine learning are opening new pathways that can potentially impact patient outcomes through response-guided adaptive treatments and targeted therapies based on radiomic and pathomic analysis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid31915534,

title = {Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer},

author = {William T Tran and Harini Suraweera and Karina Quaioit and Daniel Cardenas and Kai X Leong and Irene Karam and Ian Poon and Deok Jang and Lakshmanan Sannachi and Mehrdad Gangeh and Sami Tabbarah and Andrew Lagree and Ali Sadeghi-Naini and Gregory J Czarnota},

doi = {10.2144/fsoa-2019-0048},

issn = {2056-5623},

year  = {2019},

date = {2019-11-01},

journal = {Future Sci OA},

volume = {6},

number = {1},

pages = {FSO433},

abstract = {AIM: We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer.



MATERIALS & METHODS: Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and -nearest neighbor classifiers.



RESULTS: There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%.



CONCLUSION: Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response .},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid31325763,

title = {Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models},

author = {Lakshmanan Sannachi and Mehrdad Gangeh and Hadi Tadayyon and Sonal Gandhi and Frances C Wright and Elzbieta Slodkowska and Belinda Curpen and Ali Sadeghi-Naini and William Tran and Gregory J Czarnota},

doi = {10.1016/j.tranon.2019.06.004},

issn = {1936-5233},

year  = {2019},

date = {2019-10-01},

journal = {Transl Oncol},

volume = {12},

number = {10},

pages = {1271--1281},

abstract = {PURPOSE: The purpose of this study was to develop computational algorithms to best determine tumor responses early after the start of neoadjuvant chemotherapy, based on quantitative ultrasound (QUS) and textural analysis in patients with locally advanced breast cancer (LABC).



METHODS: A total of 100 LABC patients treated with neoadjuvant chemotherapy were included in this study. Breast tumors were scanned with a clinical ultrasound system prior to treatment, during the first, fourth and eighth weeks of treatment, and prior to surgery. QUS parameters were calculated from ultrasound radio frequency data within tumor regions. Texture features were extracted from each QUS parametric map. Patients were classified into two groups based on identified clinical/pathological response: responders and non-responders. In order to differentiate treatment responders, three multi-feature response classification algorithms, namely a linear discriminant, a k-nearest-neighbor and a nonlinear support vector machine classifier were compared.



RESULTS: All algorithms distinguished responders and non-responders with accuracies ranging between 68% and 92%. In particular, support vector machine performed the best in differentiating responders from non-responders with accuracies of 78%, 90% and 92% at weeks 1, 4 and 8 after the start of treatment, respectively. The most relevant features in separating the two response groups at early stages (weeks 1and 4) were texture features and at a later stage (week 8) were mean QUS parameters, particularly ultrasound backscatter intensity-based parameters.



CONCLUSION: An early stage treatment response prediction model developed by quantitative ultrasound and texture analysis combined with modern computational methods permits offering effective alternatives to standard treatment for refractory patients.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid31226518,

title = {Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography},

author = {Jason Fernandes and Lakshmanan Sannachi and William T Tran and Alexander Koven and Elyse Watkins and Farnoosh Hadizad and Sonal Gandhi and Frances Wright and Belinda Curpen and Ahmed El Kaffas and Joanna Faltyn and Ali Sadeghi-Naini and Gregory Czarnota},

doi = {10.1016/j.tranon.2019.05.004},

issn = {1936-5233},

year  = {2019},

date = {2019-09-01},

journal = {Transl Oncol},

volume = {12},

number = {9},

pages = {1177--1184},

abstract = {Strain elastography was used to monitor response to neoadjuvant chemotherapy (NAC) in 92 patients with biopsy-proven, locally advanced breast cancer. Strain elastography data were collected before, during, and after NAC. Relative changes in tumor strain ratio (SR) were calculated over time, and responder status was classified according to tumor size changes. Statistical analyses determined the significance of changes in SR over time and between response groups. Machine learning techniques, such as a naïve Bayes classifier, were used to evaluate the performance of the SR as a marker for Miller-Payne pathological endpoints. With pathological complete response (pCR) as an endpoint, a significant difference (P < .01) in the SR was observed between response groups as early as 2 weeks into NAC. Naïve Bayes classifiers predicted pCR with a sensitivity of 84%, specificity of 85%, and area under the curve of 81% at the preoperative scan. This study demonstrates that strain elastography may be predictive of NAC response in locally advanced breast cancer as early as 2 weeks into treatment, with high sensitivity and specificity, granting it the potential to be used for active monitoring of tumor response to chemotherapy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}