AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter.

ABSTRACT: Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clini

SUBMITTER: Tan Y 

PROVIDER: S-EPMC11226620 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature