An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer.

ABSTRACT: IntroductionTumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TIL levels is essential for guiding personalized treatment strategies. This study aimed to develop and evaluate machine learning models using ultrasound-derived radiomics and clinical features to predict TIL levels in BC.MethodsThis retrospective study included 256 BC patients between January 2019 and August 2023, who were randomly divided into training (n = 179) and test (n = 77) cohorts. Radiomics features were extracted from the intratumor and peritumor regions in ultrasound images. Feature selection was performed using the "Boruta" package in R to iteratively remove non-significant features. Extra Trees Classifier was used to construct radi

SUBMITTER: Liu B 

PROVIDER: S-EPMC12035158 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature