Development of prediction models for liver metastasis in colorectal cancer based on machine learning: a population-level study.

ABSTRACT:

Background

Liver metastasis (LM) is of vital importance in making treatment-related decisions in patients with colorectal cancer (CRC). The aim of our study was to develop and validate prediction models for LM in CRC by making use of machine learning.

Methods

We selected patients diagnosed with CRC from 2010 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Four machine-learning methods, eXtreme gradient boost (XGB), decision tree (DT), random forest (RF), and support vector machine (SVM), were employed to develop a predictive model. The receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves and calibration curves were adopted to evaluate the model performance. The SHapley Additive exPlanation (SHAP) technique was cho

SUBMITTER: Xing Y 

PROVIDER: S-EPMC11651848 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature