ATEC23 Challenge: Automated prediction of treatment effectiveness in ovarian cancer using histopathological images

Ovarian cancer, predominantly epithelial ovarian cancer (EOC), is a global health concern due to its high mortality rate. Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70% of advanced patients are with recurrent cancer and disease. Bevacizumab is a humanized monoclonal antibody, which blocks VEGF signaling in cancer, inhibits angiogenesis and causes tumor shrinkage, and has been recently approved by the FDA as a monotherapy for advanced ovarian cancer in combination with chemotherapy. Unfortunately, Bevacizumab may also induce harmful adverse effects, such as hypertension, bleeding, arterial thromboembolism, poor wound healing and gastrointestinal perforation. Given the expensive cost and unwanted toxicities, there is an urgent need for predictive methods to identify who could benefit from bevacizumab. Of the 18 (approved) requests from 5 countries, 6 teams using 284 whole section WSIs for training to develop fully automated systems submitted their predictions on a test set of 180 tissue core images, with the corresponding ground truth labels kept private. This paper summarizes the 5 qualified methods successfully submitted to the international challenge of automated prediction of treatment effectiveness in ovarian cancer using the histopathologic images (ATEC23) held at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023 and evaluates the methods in comparison with 5 state of the art deep learning approaches. This study further assesses the effectiveness of the presented prediction models as indicators for patient selection utilizing both Cox proportional hazards analysis and Kaplan-Meier survival analysis. A robust and cost-effective deep learning pipeline for digital histopathology tasks has become a necessity within the context of the medical community. This challenge highlights the limitations of current MIL methods, particularly within the context of prognosis-based classification tasks, and the importance of DCNNs like inception that has nonlinear convolutional modules at various resolutions to facilitate processing the data in multiple resolutions, which is a key feature required for pathology related prediction tasks. This further suggests the use of feature reuse at various scales to improve models for future research directions. In particular, this paper releases the labels of the testing set and provides applications for future research directions in precision oncology to predict ovarian cancer treatment effectiveness and facilitate patient selection via histopathological images.