Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics.

ABSTRACT: The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approac

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC10439253 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature