Prediction of High and Low Expression of Tumor-Infiltrating Lymphocytes in Breast Cancer Using MRI Features Combined with Molecular Subtypes

Motivation: Tumor-infiltration lymphocytes (TILs) is a key prognostic factor for breast cancer (BC). Goal(s): To differentiate high vs. low TILs using MRI features, histology, and molecular subtypes. Approach: MRI features were reviewed by radiologists using BI-RADS lexicon. The combined models were built using nomogram and machine learning (ML) algorithms. Results: The classification model built based on MRI features showed AUC of 0.81 and 0.75 in training and testing data, respectively. When combining MRI and clinical parameters, the nomogram showed AUC of 0.82 and 0.79, and the SVM ML model had the best performance, showing an AUC of 0.86 and 0.80. Impact: MRI features could predict high vs. low TILs expression. The three molecular subtypes (HR+/HER2-, HER2+, triple-negative) had distinctly different TILs, and more sophisticated models by combining MRI features with clinical and histological information could improve the TILs prediction accuracy.