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.
- 95% CI 0.61-0.68
APA
Xu T, Feng J, et al. (2026). Prediction of Breast Cancer Lymph Node Metastasis by a Nomogram Model Integrating Pathomics, Radiomics, and Immunoscore.. Chemical biology & drug design, 107(1), e70244. https://doi.org/10.1111/cbdd.70244
MLA
Xu T, et al.. "Prediction of Breast Cancer Lymph Node Metastasis by a Nomogram Model Integrating Pathomics, Radiomics, and Immunoscore.." Chemical biology & drug design, vol. 107, no. 1, 2026, pp. e70244.
PMID
41559849
Abstract
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.
MeSH Terms
Humans; Breast Neoplasms; Nomograms; Female; Lymphatic Metastasis; Magnetic Resonance Imaging; Deep Learning; Lymph Nodes; Middle Aged; Radiomics
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