The development of a prediction model based on deep learning for prognosis prediction of gastrointestinal stromal tumor: a SEER-based study.

ABSTRACT: Accurately predicting the prognosis of Gastrointestinal stromal tumor (GIST) patients is an important task. The goal of this study was to create and assess models for GIST patients' survival patients using the Surveillance, Epidemiology, and End Results Program (SEER) database based on the three different deep learning models. Four thousand five hundred thirty-eight patients were enrolled in this study and divided into training and test cohorts with a 7:3 ratio; the training cohort was used to develop three different models, including Cox regression, RSF, and DeepSurv model. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The net benefits at risk score stratification of GIST patients based on the optimal model

SUBMITTER: Zeng J 

PROVIDER: S-EPMC10951333 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature