Contrastive Integrated Gradients: A Feature Attribution-Based Method for Explaining Whole Slide Image Classification

A recent study has developed a graph neural network (GNN)-based model for real-time prediction of breast cancer sites during biopsy procedures. This model addresses the challenges of creating accurate deformable breast models, which are essential for indirect MRI-guided biopsies. By incorporating individual-specific finite element models derived from MRI images, the approach aims to enhance the accuracy and efficiency of breast cancer diagnosis, ultimately improving patient outcomes.

Randomized controlled trials (RCTs) are essential for validating the clinical effectiveness of medical AI tools, but their high costs and lengthy timelines pose significant challenges. The proposed BRIDGE design offers a solution by reusing participant-level data from previous trials when AI models yield similar predictions. This approach can significantly reduce enrollment requirements by 46.6% and save over $2.8 million while maintaining an 80% statistical power, demonstrating its potential for efficient AI model validation in areas like breast cancer, cardiovascular disease, and sepsis.

Current cancer screening guidelines are limited to a few cancer types and rely on specific criteria like age or smoking history to identify high-risk individuals. A study evaluates the effectiveness of predictive models using electronic health records (EHRs) to identify high-risk groups by detecting subtle prediagnostic signals of cancer. The research focuses on eight major cancers, including breast and lung cancer, and compares EHR-based models to traditional risk factors. Evidence suggests EHR-based models may be more effective in identifying true cancer cases.