A prognostic framework for predicting lung signet ring cell carcinoma via a machine learning based cox proportional hazard model.

ABSTRACT:

Purpose

Signet ring cell carcinoma (SRCC) is a rare type of lung cancer. The conventional survival nomogram used to predict lung cancer performs poorly for SRCC. Therefore, a novel nomogram specifically for studying SRCC is highly required.

Methods

Baseline characteristics of lung signet ring cell carcinoma were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression and random forest analysis were performed on the training group data, respectively. Subsequently, we compared results from these two types of analyses. A nomogram model was developed to predict 1-year, 3-year, and 5-year overall survival (OS) for patients, and receiver operating characteristic (ROC) curves and calibration curves were used to asses

SUBMITTER: Chen H 

PROVIDER: S-EPMC11272739 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature