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Article

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

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Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada
2
Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
3
Temerty Centre for AI Research and Education, University of Toronto, Toronto, ON M5S 1A8, Canada
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Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada
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Department of Laboratory Medicine and Molecular Diagnostics, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada
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Division of Medical Oncology, Department of Medicine, University of Toronto, Toronto, ON M5S 3H2, Canada
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Department of Engineering and Mathematics, Sheffield Hallam University, Howard St, Sheffield S1 1WB, UK
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Department of Electrical Engineering and Computer Science, York University, Toronto, ON M3J 2S5, Canada
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Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Curr. Oncol. 2021, 28(6), 4298-4316; https://doi.org/10.3390/curroncol28060366
Received: 16 September 2021 / Revised: 17 October 2021 / Accepted: 23 October 2021 / Published: 27 October 2021
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. View Full-Text
Keywords: breast cancer; Nottingham grade; tumor; biopsy; imaging biomarkers; computational oncology breast cancer; Nottingham grade; tumor; biopsy; imaging biomarkers; computational oncology
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MDPI and ACS Style

Lagree, A.; Shiner, A.; Alera, M.A.; Fleshner, L.; Law, E.; Law, B.; Lu, F.-I.; Dodington, D.; Gandhi, S.; Slodkowska, E.A.; Shenfield, A.; Jerzak, K.J.; Sadeghi-Naini, A.; Tran, W.T. Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade. Curr. Oncol. 2021, 28, 4298-4316. https://doi.org/10.3390/curroncol28060366

AMA Style

Lagree A, Shiner A, Alera MA, Fleshner L, Law E, Law B, Lu F-I, Dodington D, Gandhi S, Slodkowska EA, Shenfield A, Jerzak KJ, Sadeghi-Naini A, Tran WT. Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade. Current Oncology. 2021; 28(6):4298-4316. https://doi.org/10.3390/curroncol28060366

Chicago/Turabian Style

Lagree, Andrew, Audrey Shiner, Marie A. Alera, Lauren Fleshner, Ethan Law, Brianna Law, Fang-I Lu, David Dodington, Sonal Gandhi, Elzbieta A. Slodkowska, Alex Shenfield, Katarzyna J. Jerzak, Ali Sadeghi-Naini, and William T. Tran. 2021. "Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade" Current Oncology 28, no. 6: 4298-4316. https://doi.org/10.3390/curroncol28060366

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