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

• Published in: Current oncology (Toronto) Vol. 28; no. 6; pp. 4298 - 4316
• Main Authors: Lagree, Andrew, Shiner, Audrey, Alera, Marie Angeli, Fleshner, Lauren, Law, Ethan, Law, Brianna, Lu, Fang-I, Dodington, David, Gandhi, Sonal, Slodkowska, Elzbieta A., Shenfield, Alex, Jerzak, Katarzyna J., Sadeghi-Naini, Ali, Tran, William T.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 27.10.2021; MDPI AG
• Subjects: Artificial Intelligence; Biomarkers; biopsy; breast cancer; Breast Neoplasms - diagnostic imaging; computational oncology; Female; Humans; imaging biomarkers; Neural Networks, Computer; Nottingham grade; Retrospective Studies; tumor; biopsy; imaging biomarkers; computational oncology; Nottingham grade; breast cancer; tumor
• DOI: 10.3390/curroncol28060366
• ISSN: 1718-7729; 1198-0052

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.