AI in Biomedical Research @ NIH Seminar Series
Leveraging AI for Precision Oncology: From Predicting Therapeutic Response to Enhancing CNS Tumor Diagnosis
When: Oct. 24th, 2024 1:00 pm - 2:00 pm
Seminar Series Details:
About Eldad Shulman, Ph.D. (CDSL)
Postdoctoral Researcher, NCI CCR Cancer Data Science Lab
About this Class
Recent advances in artificial intelligence (AI) have revolutionized the use of hematoxylin and eosin (H&E)-stained tumor slides for precision oncology, enabling data-driven approaches to predict molecular characteristics and therapeutic outcomes. In my talk, I will present ENLIGHT–DeepPT, a novel two-step AI framework. The first step, DeepPT, leverages deep learning to predict genome-wide tumor mRNA expression from H&E slides. The second step, ENLIGHT, utilizes these inferred expression values to predict patient response to targeted and immune therapies. We validate this framework across 16 cohorts from The Cancer Genome Atlas (TCGA) and independent datasets, demonstrating successful prediction of true responders in five patient cohorts spanning six cancer types, with a 39.5% increased response rate and an odds ratio of 2.28.
In addition, I will introduce DEPLOY, a deep learning model designed to enhance the diagnosis of central nervous system (CNS) tumors by predicting tumor categories from histopathology slides. DEPLOY integrates three components: a direct classifier based on histopathology images, an indirect model that predicts DNA methylation profiles for tumor classification, and a model that uses patient demographics. Trained on a dataset of 1,796 patients and tested on independent cohorts of 2,156 patients, DEPLOY achieves 95% overall accuracy and 91% balanced accuracy. These results underscore the potential of DEPLOY to assist pathologists in classifying CNS tumors rapidly, offering a promising tool for improving diagnostic precision in clinical settings.