Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation.

ABSTRACT:

Background

High-grade colorectal neuroendocrine carcinoma (HCNEC) is a rare but aggressive subset of neuroendocrine tumors. This study was designed to construct a risk model based on comprehensive clinical and mutational genomics data to facilitate clinical decision making.

Methods

A retrospective analysis was conducted using data from the Surveillance, Epidemiology, and End Results (SEER) database, spanning 2000 to 2019. The external validation cohort was sourced from two tertiary hospitals in Southwest China. Independent factors influencing both overall survival (OS) and cancer-specific survival (CSS) were identified using LASSO, Random Forest, and XGBoost regression techniques. Molecular data with the most common mutations in CNEC were extracted from the Catalogue of Soma

SUBMITTER: Wu R 

PROVIDER: S-EPMC11813786 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature