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Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Machine Learning Models.

NCT07426653 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 298

Last updated 2026-02-23

No results posted yet for this study

Summary

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

Sponsors & Collaborators

  • Florence Nightingale Hospital, Istanbul

    lead OTHER

Principal Investigators

  • Enver Özkurt, Assoc. Prof. · Demiroğlu Bilim University, Faculty of Medicine

Eligibility

Min Age
18 Years
Max Age
90 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2010-01-01
Primary Completion
2025-12-31
Completion
2025-12-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07426653 on ClinicalTrials.gov