An online tool for survival prediction of extrapulmonary small cell carcinoma with random forest.

ABSTRACT:

Purpose

Extrapulmonary small cell carcinoma (EPSCC) is rare, and its knowledge is mainly extrapolated from small cell lung carcinoma. Reliable survival prediction tools are lacking.

Methods

A total of 3,921 cases of EPSCC were collected from the Surveillance Epidemiology and End Results (SEER) database, which form the training and internal validation cohorts of the survival prediction model. The endpoint was an overall survival of 0.5-5 years. Internal validation performances of machine learning algorithms were compared, and the best model was selected. External validation (n = 68) was performed to evaluate the generalization ability of the selected model.

Results

Among machine learning algorithms, the random forest model performs best on internal validation, w

SUBMITTER: Zhang X 

PROVIDER: S-EPMC10346459 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature