Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials.

ABSTRACT: Prostate cancer is the most frequent cancer in men and a leading cause of cancer death. Determining a patient's optimal therapy is a challenge, where oncologists must select a therapy with the highest likelihood of success and the lowest likelihood of toxicity. International standards for prognostication rely on non-specific and semi-quantitative tools, commonly leading to over- and under-treatment. Tissue-based molecular biomarkers have attempted to address this, but most have limited validation in prospective randomized trials and expensive processing costs, posing substantial barriers to widespread adoption. There remains a significant need for accurate and scalable tools to support therapy personalization. Here we demonstrate prostate cancer therapy personalization by predicting long-t

SUBMITTER: Esteva A 

PROVIDER: S-EPMC9177850 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature