A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study.

ABSTRACT:

Objective

To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to preoperatively predict human epidermal growth factor receptor 2 (HER2) status in bladder cancer (BCa) with multicenter validation.

Methods

In this retrospective study, 207 patients with pathologically confirmed BCa were enrolled and divided into the training set (n = 154) and test set (n = 53). Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in the training set. Five radiomics-based ML models, namely logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN), eXtreme Gradient Boosting (XGBoost) and random forest (RF), were developed. The predictive performance of establishe

SUBMITTER: Wei Z 

PROVIDER: S-EPMC11519251 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature