Adaptive segmentation-to-survival learning for survival prediction from multi-modality medical images.

ABSTRACT: Early survival prediction is vital for the clinical management of cancer patients, as tumors can be better controlled with personalized treatment planning. Traditional survival prediction methods are based on radiomics feature engineering and/or clinical indicators (e.g., cancer staging). Recently, survival prediction models with advances in deep learning techniques have achieved state-of-the-art performance in end-to-end survival prediction by exploiting deep features derived from medical images. However, existing models are heavily reliant on the prognostic information within primary tumors and cannot effectively leverage out-of-tumor prognostic information characterizing local tumor metastasis and adjacent tissue invasion. Also, existing models are sub-optimal in leveraging multi-modali

SUBMITTER: Meng M 

PROVIDER: S-EPMC11473954 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature