Publications by authors named "Ali Sadeghi-Naini"

73 Publications

Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning.

Transl Oncol 2021 Oct 19;14(10):101183. Epub 2021 Jul 19.

Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada. Electronic address:

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.
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http://dx.doi.org/10.1016/j.tranon.2021.101183DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8319580PMC
October 2021

Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment.

Sci Rep 2021 Jul 21;11(1):14865. Epub 2021 Jul 21.

Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada.

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.
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http://dx.doi.org/10.1038/s41598-021-94004-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295369PMC
July 2021

MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.

Oncotarget 2021 Jul 6;12(14):1354-1365. Epub 2021 Jul 6.

Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.

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.
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http://dx.doi.org/10.18632/oncotarget.28002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274727PMC
July 2021

Reply to A. Pfob et al.

JCO Clin Cancer Inform 2021 06;5:656-657

Nicholas Meti, MD, Division of Medical Oncology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Ali Sadeghi-Naini, PhD, Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Ontario, Canada, Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada; and William T. Tran, MRT(T), MSc, PhD, Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada, Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.

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http://dx.doi.org/10.1200/CCI.21.00059DOI Listing
June 2021

A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks.

Sci Rep 2021 Apr 13;11(1):8025. Epub 2021 Apr 13.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.

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.
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http://dx.doi.org/10.1038/s41598-021-87496-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044238PMC
April 2021

Combining First- and Second-Order Continuity Constraints in Ultrasound Elastography.

IEEE Trans Ultrason Ferroelectr Freq Control 2021 07 29;68(7):2407-2418. Epub 2021 Jun 29.

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.
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http://dx.doi.org/10.1109/TUFFC.2021.3065884DOI Listing
July 2021

In-vivo lung biomechanical modeling for effective tumor motion tracking in external beam radiation therapy.

Comput Biol Med 2021 03 20;130:104231. Epub 2021 Jan 20.

School of Biomedical Engineering, Western University, London, ON, Canada; Department of Medical Biophysics, Western University, London, ON, Canada; Department of Electrical and Computer Engineering, Western University, London, ON, Canada; Imaging Research Laboratories, Robarts Research Institute, London, ON, Canada. Electronic address:

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.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104231DOI Listing
March 2021

Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients.

Breast Cancer Res Treat 2021 Apr 23;186(2):379-389. Epub 2021 Jan 23.

Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.

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.
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http://dx.doi.org/10.1007/s10549-020-06093-4DOI Listing
April 2021

Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.

JCO Clin Cancer Inform 2021 01;5:66-80

Temerty Centre for AI Research and Education in Medicine, University of Toronto, ON, Toronto, Canada.

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.
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http://dx.doi.org/10.1200/CCI.20.00078DOI Listing
January 2021

Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies.

Future Sci OA 2020 Sep 4;6(9):FSO624. Epub 2020 Sep 4.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.

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.
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http://dx.doi.org/10.2144/fsoa-2020-0073DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7668124PMC
September 2020

Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer.

Oncotarget 2020 Oct 20;11(42):3782-3792. Epub 2020 Oct 20.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.

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.
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http://dx.doi.org/10.18632/oncotarget.27742DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584238PMC
October 2020

Analytical Estimation of Out-of-plane Strain in Ultrasound Elastography to Improve Axial and Lateral Displacement Fields

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:2055-2058

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.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176086DOI Listing
July 2020

A Tissue Mechanics Based Method to Improve Tissue Displacement Estimation in Ultrasound Elastography

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:2051-2054

Cancer is known to induce significant structural changes to tissue. In most cancers, including breast cancer, such changes yield tissue stiffening. As such, imaging tissue stiffness can be used effectively for cancer diagnosis. One such imaging technique, ultrasound elastography, has emerged with the aim of providing a low-cost imaging modality for effective breast cancer diagnosis. In quasi-static breast ultrasound elastography, the breast is stimulated by ultrasound probe, leading to tissue deformation. The tissue displacement data can be estimated using a pair of acquired ultrasound radiofrequency (RF) data pertaining to pre- and post-deformation states. The data can then be used within a mathematical framework to construct an image of the tissue stiffness distribution. Ultrasound RF data is known to include significant noise which lead to corruption of estimated displacement fields, especially the lateral displacements. In this study, we propose a tissue mechanics-based method aiming at improving the quality of estimated displacement data. We applied the method to RF data acquired from a tissue-mimicking phantom. The results indicated that the method is effective in improving the quality of the displacement data.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175869DOI Listing
July 2020

Predicting Local Failure after Stereotactic Radiation Therapy in Brain Metastasis using Quantitative CT and Machine Learning

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1323-1326

Despite recent advances in cancer treatment, the prognosis of patients diagnosed with brain metastasis is still poor. The median survival is limited to months even for patients undergoing treatment. Radiation therapy is a main component of treatment for brain metastasis. However, radiotherapy cannot control local progression in up to 20% of the metastatic brain tumours. An early prediction of radiotherapy outcome for individual patients could facilitate therapy adjustments to improve its efficacy. This study investigated the potential of quantitative CT biomarkers in conjunction with machine learning methods to predict local failure after radiotherapy in brain metastasis. Volumetric CT images were acquired for radiation treatment planning from 120 patients undergoing stereotactic radiotherapy. Quantitative features characterizing the morphology and texture were extracted from different regions of each lesion. A feature reduction/selection framework was adapted to define a quantitative CT biomarker of radiotherapy outcome. Different machine learning methods were applied and evaluated to predict the local failure outcome at pre-treatment. The optimum biomarker consisting of two features in conjunction with an AdaBoost with decision tree could predict the local failure outcome with 71% accuracy on an independent test set (20 patients, 31 lesions). This study is a step forward towards prediction of radiotherapy outcome in brain metastasis using quantitative imaging and machine learning.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175746DOI Listing
July 2020

An Attention-Guided Deep Neural Network for Annotating Abnormalities in Chest X-ray Images: Visualization of Network Decision Basis

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1258-1261

Despite the potential of deep convolutional neural networks for classification of thorax diseases from chest X-ray images, this task is still challenging as it is categorized as a weakly supervised learning problem, and deep neural networks in general suffer from a lack of interpretability. In this paper, a deep convolutional neural network framework with recurrent attention mechanism was investigated to annotate abnormalities in chest X-ray images. A modified MobileNet architecture was adapted in the framework for classification and the prediction difference analysis method was utilized to visualize the basis of network's decision on each image. A long short-term memory network was utilized as the attention model to focus on relevant regions of each image for classification. The framework was evaluated on NIH chest X-ray dataset. The attention-guided model versus the model with no attention mechanism could annotate the images in an independent test set with an F1-score of 0.58 versus 0.46, and an AUC of 0.94 versus 0.73. The obtained results implied that the proposed attention-guided model could outperform the other methods investigated previously for annotating the same dataset.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175378DOI Listing
July 2020

Machine Learning-Based A Priori Chemotherapy Response Prediction in Breast Cancer Patients using Textural CT Biomarkers

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1250-1253

Early prediction of cancer response to neoadjuvant chemotherapy (NAC) could permit personalized treatment adjustments for patients, which would improve treatment outcomes and patient survival. For the first time, the efficiency of quantitative computed tomography (qCT) textural and second derivative of textural (SDT) features were investigated and compared in this study. It was demonstrated that intra-tumour heterogeneity can be probed through these biomarkers and used as chemotherapy tumour response predictors in breast cancer patients prior to the start of treatment. These features were used to develop a machine learning approach which provided promising results with cross-validated AUC0.632+, accuracy, sensitivity and specificity of 0.86, 81%, 74% and 88%, respectively.Clinical Relevance- The results obtained in this study demonstrate the potential of textural CT biomarkers as response predictors of standard NAC before treatment initiation.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176099DOI Listing
July 2020

A Brain Tumor Segmentation Framework Based on Outlier Detection Using One-Class Support Vector Machine.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1067-1070

Accurate segmentation of brain tumors is a challenging task and also a crucial step in diagnosis and treatment planning for cancer patients. Magnetic resonance imaging (MRI) is the standard imaging modality for detection, characterization, treatment planning and outcome evaluation of brain tumors. MRI scans are usually acquired at multiple sessions before and after the treatment. An automatic segmentation framework is highly desirable to segment brain tumors in MR images as it streamlines the image-guided radiation therapy workflow considerably. Automatic segmentation of brain tumors also facilitates an incremental development of data-driven systems for therapy outcome prediction based on radiomics analysis. In this study, an outlier-detection-based segmentation framework is proposed to delineate brain tumors in magnetic resonance (MR) images automatically. The proposed method considers the tumor and edema pixels in an MR image as outliers compared to the pixels associated with the healthy tissue. The framework generates two outlier masks using independent one-class support vector machines that operate on post-contrast T1-weighted (T1w) and T2-weighted-fluid-attenuation-inversion-recovery (T2-FLAIR) images. The outlier masks are subsequently refined and fused using a number of morphological and logical operators to estimate a tumor mask for each image slice. The framework was constructed and evaluated using the MRI data acquired from 35 and 5 patients with brain metastasis, respectively. The obtained results demonstrated an average Dice similarity coefficient and Hausdorff distance of 0.84 ± 0.06 and 1.85 ± 0.48 mm, respectively, between the manual (ground truth) and automatic tumor contours, on the independent test set.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176263DOI Listing
July 2020

A Cascaded Deep-Learning Framework for Segmentation of Metastatic Brain Tumors Before and After Stereotactic Radiation Therapy

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1063-1066

Radiation therapy is a major treatment option for brain metastasis. For radiation treatment planning and outcome evaluation, magnetic resonance (MR) images are acquired before and at multiple sessions after the treatment. Accurate segmentation of brain tumors on MR images is crucial for treatment planning, response evaluation, and developing data-driven models for outcome prediction. Due to the high volume of imaging data acquired from each patient at multiple follow-up sessions, manual tumor segmentation is resource- and time-consuming in clinic, hence developing an automatic segmentation framework is highly desirable. In this work, we proposed a cascaded 2D-3D Unet framework to segment brain tumors automatically on contrast-enhanced T1- weighted images acquired before and at multiple scan sessions after radiotherapy. 2D Unet is a well-known structure for medical image segmentation. 3D Unet is an extension of 2D Unet with a volumetric input image to provide richer spatial information. The limitation of 3D Unet is that it is memory consuming and cannot process large volumetric images. To address this limitation, a large volumetric input of 3D Unet is often patched to smaller volumes which leads to loss of context. To overcome this problem, we proposed using two cascaded 2D Unets to crop the input volume around the tumor area and reduce the input size of the 3D Unet, obviating the need to patch the input images. The framework was trained using images acquired from 96 patients before radiation therapy and tested using images acquired from 10 patients before and at four follow-up scans after radiotherapy. The segmentation results for the images of independent test set demonstrated that the cascaded framework outperformed the 2D and 3D Unets alone, with an average Dice score of 0.9 versus 0.86 and 0.88 for the baseline, and 0.87 versus 0.83 and 0.84 for the first followup. Similar results were obtained for the other follow-up scans.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175489DOI Listing
July 2020

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence.

Can Assoc Radiol J 2021 Feb 31;72(1):98-108. Epub 2020 Aug 31.

Division of Breast Imaging, 71545Sunnybrook Health Sciences Centre, Toronto, Canada.

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.
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http://dx.doi.org/10.1177/0846537120949974DOI Listing
February 2021

Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results.

PLoS One 2020 27;15(7):e0236182. Epub 2020 Jul 27.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236182PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384762PMC
September 2020

A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning.

Sci Rep 2020 07 2;10(1):10936. Epub 2020 Jul 2.

Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.

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.
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http://dx.doi.org/10.1038/s41598-020-67823-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331583PMC
July 2020

Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi-institutional study.

Cancer Med 2020 08 29;9(16):5798-5806. Epub 2020 Jun 29.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.

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.
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http://dx.doi.org/10.1002/cam4.3255DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7433820PMC
August 2020

An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI.

Sci Rep 2020 05 15;10(1):8063. Epub 2020 May 15.

Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada.

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.
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http://dx.doi.org/10.1038/s41598-020-64912-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7228927PMC
May 2020

Quantitative Ultrasound Monitoring of Breast Tumour Response to Neoadjuvant Chemotherapy: Comparison of Results Among Clinical Scanners.

Ultrasound Med Biol 2020 05 25;46(5):1142-1157. Epub 2020 Feb 25.

Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada. Electronic address:

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.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2020.01.022DOI Listing
May 2020

Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine Learning.

Int J Radiat Oncol Biol Phys 2020 04 23;106(5):1071-1083. Epub 2020 Jan 23.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada; Evaluative Clinical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Radiotherapy & Oncology, Sheffield Hallam University, Sheffield, United Kingdom; Department of Biomedical Physics, Ryerson University, Toronto, Canada. Electronic address:

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.
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http://dx.doi.org/10.1016/j.ijrobp.2019.12.032DOI Listing
April 2020

Incorporating Pathology-Induced Heterogeneities in a Patient-Specific Biomechanical Model of the Lung for Accurate Tumor Motion Estimation.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:6964-6967

Radiation therapy (RT) is an important component of treatment for lung cancer. However, the accuracy of this method can be affected by the complex respiratory motion/deformation of the target tumor during treatment. To improve the accuracy of RT, patient-specific biomechanical models of the lung have been proposed for estimating the tumor's respiratory motion/deformation. Chronic obstructive pulmonary disease (COPD) has a high incidence among lung cancer patients and is associated with heterogeneous destruction of lung parenchyma. This key heterogeneity element, however, has not been incorporated in lung biomechanical models developed in previous studies. In this work, we have developed a physiologically and patho-physiologically realistic lung biomechanical model that accounts for lung tissue heterogeneity. Four-dimensional computed tomography (4DCT) images were used to build a patient-specific finite element (FE) model of the lung. Image information was used to identify and incorporate inhomogeneities within the model. Mechanical properties of normal and diseased regions in the lung and the transpulmonary pressure driving the respiratory motion were estimated using an optimization algorithm that maximizes the similarity between the actual and simulated tumor and lung image data. Results from this proof of concept study on a lung cancer patient indicated improved accuracy of tumor motion estimation when COPD-induced lung tissue heterogeneities were incorporated in the model.
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http://dx.doi.org/10.1109/EMBC.2019.8856707DOI Listing
July 2019

4DCT Ventilation Map Construction Using Biomechanics-base Image Registration and Enhanced Air Segmentation.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:6263-6266

Current lung radiation therapy (RT) treatment planning algorithms used in most centers assume homogeneous lung function. However, co-existing pulmonary dysfunctions present in many non-small cell lung cancer (NSCLC) patients, particularly smokers, cause regional variations in both perfusion and ventilation, leading to inhomogeneous lung function. An adaptive RT treatment planning that deliberately avoids highly functional lung regions can potentially reduce pulmonary toxicity and morbidity. The ventilation component of lung function can be measured using a variety of techniques. Recently, 4DCT ventilation imaging has emerged as a cost-effective and accessible method. Current 4DCT ventilation calculation methods, including the intensity-based and Jacobian models, suffer from inaccurate estimations of air volume distribution and unreliability of intensity-based image registration algorithms. In this study, we propose a novel method that utilizes a biomechanical model-based registration along with an accurate air segmentation algorithm to calculate 4DCT ventilation maps. The results show a successful development of ventilation maps using the proposed method.
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http://dx.doi.org/10.1109/EMBC.2019.8857931DOI Listing
July 2019

An MR Radiomics Framework for Predicting the Outcome of Stereotactic Radiation Therapy in Brain Metastasis.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:1022-1025

Despite recent advances in cancer treatment, patients with brain metastasis still suffer from poor overall survival (OS) after standard treatment. Predicting the treatment outcome before or early after the treatment can potentially assist the physicians in improving the therapy outcome by adjusting a standard treatment on an individual patient basis. In this study, a data-driven computational framework was proposed and investigated to predict the local control/failure (LC/LF) outcome in patients with brain metastasis treated with hypo-fractionated stereotactic radiation therapy (SRT). The framework extracted several geometrical and textural features from the magnetic resonance (MR) images of the tumour and edema regions acquired for 38 patients. Subsequent to a multi-step feature reduction/selection, a quantitative MR biomarker consisting of two features was constructed. A support vector machine classifier was used for outcome prediction using the constructed MR biomarker. The bootstrap .632+ and leave-one-patient-out cross-validation methods were used to assess the model's performance. The results indicated that the outcome of LF after SRT could be predicted with an area under the curve of 0.80 and a cross-validated accuracy of 82%. The results obtained implied a good potential of the proposed framework for local outcome prediction in patients with brain metastasis treated with SRT and encourage further investigations on a larger cohort of patients.
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http://dx.doi.org/10.1109/EMBC.2019.8856558DOI Listing
July 2019

An Automatic Framework for Segmentation of Brain Tumours at Follow-up Scans after Radiation Therapy

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:463-466

Brain metastasis is the most common intracranial malignancy with a poor overall survival (OS) after treatment. The standard stereotactic radiation therapy (SRT) planning procedure for brain metastasis requires delineating the tumour volume on magnetic resonance (MR) images. MR images are also acquired at multiple follow-up scans after SRT to monitor the treatment outcome through measuring changes in the physical dimensions of the tumour. Such measurements require manual segmentation of the tumour volume on multiple slices of several follow-up images which is tedious and impedes the SRT evaluation work flow considerably. In this study, an automatic framework was proposed to segment the tumour volume on longitudinal MR images acquired at standard follow-up scans after SRT. The multi-step segmentation framework was based on region growing and morphological snakes models that applied the standard SRT planning tumour contour as a basis to approximate the tumour shape and location at each follow-up scan for an accurate automatic segmentation of tumour volume. The framework was evaluated using the MR imaging data acquired from five patients prior to and at three follow-up scans after SRT. The preliminary results indicated that the Dice similarity coefficient between the ground truth tumour masks and their automatically segmented counterparts ranged between 0.84 and 0.90, while the average Dice coefficient for all the follow-up scans was 0.88. The results obtained implied a good potential of the proposed framework for being incorporated into the SRT treatment planning and evaluation systems as well as outcome prediction models.
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http://dx.doi.org/10.1109/EMBC.2019.8856858DOI Listing
July 2019

Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer.

Future Sci OA 2019 Nov 26;6(1):FSO433. Epub 2019 Nov 26.

Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.

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 .
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http://dx.doi.org/10.2144/fsoa-2019-0048DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920736PMC
November 2019
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