Prediction of Breast Cancer Lymph Node Metastasis by a Nomogram Model Integrating Pathomics, Radiomics, and Immunoscore.

This study aimed to develop a noninvasive nomogram that integrates deep learning-pathomics, radiomics, and immunoscore to predict lymph node metastasis (LNM) in breast cancer. Pathological features from 1133 TCGA-BRCA slides were extracted via ResNet50 and Lasso. Radiomics features from 137 MRI images (TCIA) were analyzed using pyradiomics. Immunoscore was calculated via ESTIMATE. A nomogram was constructed and validated with 10-fold cross-validation. The pathomics model achieved an AUC of 0.65 (95% CI: 0.61-0.68), sensitivity 0.62, specificity 0.67; radiomics 0.61 (95% CI: 0.50-0.72), sensitivity 0.59, specificity 0.63; and the combined nomogram 0.69 (95% CI: 0.59-0.79), sensitivity 0.66, specificity 0.71. Radiomics score was the strongest predictor. The nomogram provides a reliable noninvasive tool for predicting lymph node involvement, potentially reducing unnecessary biopsies.