Predicting nodal response to neoadjuvant treatment in breast cancer with core biopsy biomarkers of tumor microenvironment using data mining
- Nina Pislar 1,2, Gorana Gasljevic 3,4, Erika Matos 2,5, Gasper Pilko 1,2, Janez Zgajnar 1,2, Andraz Perhavec 6,7
- Nina Pislar 1,2, Gorana Gasljevic 3,4, Erika Matos 2,5
- 1Department of Surgical Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia.
- 2Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
- 3Department of Pathology, Institute of Oncology Ljubljana, Ljubljana, Slovenia.
- 4Faculty of Medicine, University of Maribor, Maribor, Slovenia.
- 5Department of Medical Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia.
- 6Department of Surgical Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia. aperhavec@onko-i.si.
- 7Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia. aperhavec@onko-i.si.
- 0Department of Surgical Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia.
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View abstract on PubMed
Summary
This summary is machine-generated.A new model predicts nodal response to neoadjuvant systemic treatment (NAST) in node-positive breast cancer patients. Incorporating tumor microenvironment (TME) factors, this tool aids surgical planning and improves prediction accuracy.
Area Of Science
- Oncology
- Breast Cancer Research
- Tumor Microenvironment
Background
- Accurate prediction of nodal response to neoadjuvant systemic treatment (NAST) is crucial for staging node-positive (cN+) breast cancer patients.
- Integrating tumor microenvironment (TME) characteristics can enhance predictive models for treatment response.
Purpose Of The Study
- To develop a predictive model for nodal response to NAST in cN+ breast cancer patients.
- The model aims to incorporate TME features for improved axillary surgical staging planning.
Main Methods
- Retrospective collection of clinical and pathological data from 437 cN+ breast cancer patients.
- Core biopsy samples were analyzed for stromal content and tumor-infiltrating lymphocytes (TILs).
- Orange Datamining Toolbox was utilized for model development and validation.
Main Results
- 34.6% of patients achieved pathological complete response (pCR) in the nodes (ypN0).
- A prediction model was built using ER, Her-2, grade, stromal content, and TILs.
- The logistic regression model achieved an AUC of 0.86 and an F1 score of 0.72.
Conclusions
- A novel clinical tool was developed to predict nodal pCR in cN+ breast cancer patients post-NAST.
- The model effectively integrates TME biomarkers, achieving a high predictive performance (AUC 0.86).
- This tool can aid in refining surgical staging strategies for breast cancer patients receiving NAST.
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