Metabolism score and machine learning models for the prediction of esophageal squamous cell carcinoma progression.

ABSTRACT: The incomplete prediction of prognosis in esophageal squamous cell carcinoma (ESCC) patients is attributed to various therapeutic interventions and complex prognostic factors. Consequently, there is a pressing demand for enhanced predictive biomarkers that can facilitate clinical management and treatment decisions. This study recruited 491 ESCC patients who underwent surgical treatment at Huashan Hospital, Fudan University. We incorporated 14 blood metabolic indicators and identified independent prognostic indicators for overall survival through univariate and multivariate analyses. Subsequently, a metabolism score formula was established based on the biochemical markers. We constructed a nomogram and machine learning models utilizing the metabolism score and clinically significant prognos

SUBMITTER: Chen L 

PROVIDER: S-EPMC11462955 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature