Deep learning-based analysis of gross features for ovarian epithelial tumors classification: A tool to assist pathologists for frozen section sampling

Abstract

Computational pathology has primarily focused on analyzing tissue slides, neglecting the valuable information contained in gross images. To bridge this gap, we proposed a novel approach leveraging the Swin Transformer architecture to develop a Swin-Transformer based Gross Features Detective Network (SGFD-network), which assist pathologists for locating diseased area in ovarian epithelial tumors based on their gross features. Our model was trained on 4129 gross images and achieved high accuracy rates of 88.9 %, 86.4 %, and 93.0 % for benign, borderline, and carcinoma group, respectively, demonstrating strong agreement with pathologist evaluations. Notably, we trained a new classifier to distinguish between borderline tumors and those with microinvasion or microinvasive carcinoma, addressing a significant challenge in frozen section sampling. Our study was the first to propose a solution to this challenge, showcasing high accuracy rates of 85.0 % and 92.2 % for each group, respectively. To further elucidate the decision-making process, we employed Class Activation Mapping-grad to identify high-contribution zones and applied k-means clustering to summarize these features. The resulting clustered features can effectively complement existing knowledge of gross examination, improving the distinction between borderline tumors and those with microinvasion or microinvasive carcinoma. Our model identifies high-risk areas for microinvasion or microinvasive carcinoma, enabling pathologists to target sampling more effectively during frozen sections. Furthermore, SGFD-network requires only a single 4090 graphics card and completes a single interpretation task in 3 min. This study demonstrates the potential of deep learning-based analysis of gross features to aid in ovarian epithelial tumors sampling, especially in frozen section.