Clinical trial · Observational
Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Machine Learning Models.
Clinicopathology-based Machine Learning Model for Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This retrospective observational study aims to develop and validate a clinicopathology-based machine learning model to predict pathological complete response (pCR) following neoadjuvant chemotherapy in patients with breast cancer. Clinical and pathological data collected between 2010 and 2025 were used to train and evaluate multiple machine learning algorithms using cross-validation and independent holdout testing. The primary outcome was pathological complete response after neoadjuvant chemotherapy. Model performance was assessed using discrimination and classification metrics, including ROC-AUC, precision-recall AUC, F1-score, and Matthews correlation coefficient. The resulting model is intended to support clinical decision-making by providing individualized probability estimates of treatment response.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (1)
- label
- Patients with no residual invasive cancer in surgical pathology following neoadjuvant chemotherapy
Primary outcomes (1)
- measure
- Pathological Complete Response (pCR)
- timeFrame
- At time of surgery following completion of neoadjuvant chemotherapy (approximately 4-6 months after treatment initiation)
- description
- Pathological complete response is defined as the absence of residual invasive cancer in the breast and axillary lymph nodes at the time of surgery following completion of neoadjuvant chemotherapy.
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
- Maximum age
- 90 Years
Show eligibility criteria text
Inclusion Criteria: * Histologically confirmed breast cancer * Receipt of neoadjuvant chemotherapy * Available clinicopathological data required for model development * Surgical treatment performed following neoadjuvant chemotherapy * Pathological response assessment available * Recorded pathological details Exclusion Criteria: * Missing pathological response information * Incomplete clinicopathological data required for model analysis * Patients not treated with neoadjuvant chemotherapy * Non-invasive breast cancer without indication for neoadjuvant treatment
References
Publications (1)
- BACKGROUNDKim JY, Jeon E, Kwon S, Jung H, Joo S, Park Y, Lee SK, Lee JE, Nam SJ, Cho EY, Park YH, Ahn JS, Im YH. Prediction of pathologic complete response to neoadjuvant chemotherapy using machine learning models in patients with breast cancer. Breast Cancer Res Treat. 2021 Oct;189(3):747-757. doi: 10.1007/s10549-021-06310-8. Epub 2021 Jul 5. PMID 34224056