A robust AI-pipeline for ovarian cancer classification on histopathology images - Kussaibi, Haitham; Alibrahim , Elaf; Alamer, Eman; Al hajji, Ghadah; Alshehab , Shrooq; Shabib, Zahra; Alsafwani, Noor; Menezes, Ritesh G. (Acta Biomedica Atenei Parmensis, 2024)
Background and aim: Ovarian cancer is the leading cause of gynecological cancer deaths due to late diagnosis and high recurrence rates. While histopathological analysis is the gold standard for diagnosis, artificial intelligence (AI) models have shown promise in accurately classifying ovarian cancer subtypes using histopathology images. Herein, we introduce an end-to-end AI pipeline for automated identification of epithelial ovarian cancer (EOC) subtypes based on histopathology images and evaluate its performance compared to pathologists’ diagnoses. Methods: A dataset of over 2 million image tiles from 82 whole slide images (WSIs) of major EOC subtypes (clear cell, endometrioid, mucinous, and serous) was curated from public and institutional sources. A convolutional neural network (ResNet50) was used to extract features, which were then introduced to 2 classifiers (NN and LightGBM) to predict cancer subtypes. Results: Both AI classifiers achieved patch-level accuracy (97-98%) on a test set. Furthermore, adding a class-weighted cross-entropy loss function to the pipeline showed better discriminative performance among subtypes. Conclusions: AI models trained on histopathology images can accurately classify EOC subtypes, potentially assisting pathologists and reducing subjectivity in ovarian cancer diagnosis.