The prognostic analysis and a machine-learning based disease-specific survival state model in pulmonary large-cell neuroendocrine carcinomas.

ABSTRACT:

Background

Pulmonary large-cell neuroendocrine carcinoma (PLCNEC) is a rare and highly malignant lung cancer. Due to the paucity of data from clinical studies, its clinical characteristics and treatment remain controversial. The present study explored factors influencing the prognosis and survival outcomes of patients with PLCNEC and developed a dependable prognostic model using machine learning.

Methods

The clinical data of PLCNEC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2020. A total of 2,897 PLCNEC patients were enrolled and univariate and multivariate Cox regression analyses were performed to explore independent prognostic factors for disease-specific survival (DSS). Ten machine learning algorithms were

SUBMITTER: Xu X 

PROVIDER: S-EPMC11388250 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature