ArticleBreast cancer research and treatment2025
Predicting nodal response to neoadjuvant treatment in breast cancer with core biopsy biomarkers of tumor microenvironment using data mining.
Article in Breast cancer research and treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Early identification of neoadjuvant therapy non-response via multimodal immune-imaging biomarkers in breast cancer.Frontiers in immunology · 2026Article
- Prediction of pathological complete response after neoadjuvant therapy in breast cancer using pretreatment core biopsy pathology reports: a pathology-report-driven model with exploratory clinical-laboratory fusion.Frontiers in oncology · 2026Article
- Critical appraisal of a data mining model for predicting nodal response to neoadjuvant treatment in breast cancer.Breast cancer research and treatment · 2025Article
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6 authors.
Funding
Abstract
purposeTo generate a model for predicting nodal response to neoadjuvant systemic treatment (NAST) in biopsy-proven node-positive breast cancer patients (cN+) that incorporates tumor microenvironment (TME) characteristics and could be used for planning the axillary surgical staging procedure.
methodsClinical and pathologic features were retrospectively collected for 437 patients. Core biopsy (CB) samples were reviewed for stromal content and tumor-infiltrating lymphocytes (TIL). Orange Datamining Toolbox was used for model generation and assessment.
results151/437 (34.6%) patients achieved nodal pCR (ypN0). The following 5 variables were included in the prediction model: ER, Her-2, grade, stroma content and TILs. After stratified tenfold cross-validation, the logistic regression algorithm achieved and area under the ROC curve (AUC) of 0.86 and F1 score of 0.72. Nomogram was used for visualization.
conclusionsWe developed a clinical tool to predict nodal pCR for cN+ patients after NAST that includes biomarkers of TME and achieves an AUC of 0.86 after tenfold cross-validation.
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