Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study.

ABSTRACT: The accuracy of indices widely used to evaluate lung metastasis (LM) in patients with kidney cancer (KC) is insufficient. Therefore, we aimed at developing a model to estimate the risk of developing LM in KC based on a large population size and machine learning algorithms. Demographic and clinicopathologic variables of patients with KC diagnosed between 2004 and 2017 were retrospectively analyzed. We performed a univariate logistic regression analysis to identify risk factors for LM in patients with KC. Six machine learning (ML) classifiers were established and tuned using the ten-fold cross-validation method. External validation was performed using clinicopathologic information from 492 patients from the Southwest Hospital, Chongqing, China. Algorithm performance was estimated by analyzin

SUBMITTER: Yi X 

PROVIDER: S-EPMC10299882 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature