Magnetic resonance imaging radiomics for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer
- 1Afyonkarahisar University of Health Sciences, Faculty of Medicine, Department of Radiology – Afyonkarahisar, Turkey.
Abstract
SUMMARY: OBJECTIVE:: The aim of this study was to develop and validate machine learning models integrating clinical and pre-treatment dynamic contrast-enhanced magnetic resonance imaging radiomic features for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer.
METHODS:: This retrospective study analyzed 299 breast cancer patients from the publicly available Duke Breast Cancer magnetic resonance imaging dataset who received neoadjuvant chemotherapy. From pre-treatment dynamic contrast-enhanced magnetic resonance imaging, 534 IBSI-compliant radiomic features were extracted. A five-method consensus feature selection pipeline retained 24 features. Notably, three prediction models were developed: clinical-only (Model A), radiomic-only (Model B), and combined clinical–radiomic (Model C). In total, seven machine learning algorithms were compared using 20 repeated five-fold stratified cross-validation with SMOTE applied within each training fold and assessed on a held-out test set (30%, n=90). Performance was evaluated using AUC with bootstrap confidence intervals, calibration (Brier score), and decision curve analysis.
RESULTS:: The pathologic complete response rate was 20.4% (61/299). The combined model (Gradient Boosting) achieved the highest test AUC of 0.723 (95%CI 0.595–0.835), with sensitivity of 0.333 and specificity of 0.833. DeLong tests revealed no statistically significant differences between model pairs (all p>0.05). Decision curve analysis demonstrated the highest net benefit for the combined model across 10–50% threshold probabilities.
CONCLUSION:: The combined clinical–radiomic model demonstrated modest discriminative ability for pathologic complete response prediction. The limited sensitivity suggests potential utility as a rule-out test to identify patients unlikely to achieve pathologic complete response. These hypothesis-generating findings require external validation in independent multi-center cohorts.
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BibTeX
@article{demirel2026magnetic,
title = {{Magnetic resonance imaging radiomics for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer}},
author = {Emin Demirel and Huseyin Coskun},
journal = {Revista da Associação Médica Brasileira},
year = {2026},
month = nov,
doi = {10.1590/1806-9282.20260878},
url = {https://doi.org/10.1590/1806-9282.20260878},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Magnetic resonance imaging radiomics for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer
AU - Emin Demirel
AU - Huseyin Coskun
PY - 2026
DA - 2026/11/02
JO - Revista da Associação Médica Brasileira
DO - 10.1590/1806-9282.20260878
UR - https://doi.org/10.1590/1806-9282.20260878
AB - SUMMARY: OBJECTIVE:: The aim of this study was to develop and validate machine learning models integrating clinical and pre-treatment dynamic contrast-enhanced magnetic resonance imaging radiomic features for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer. METHODS:: This retrospective study analyzed 299 breast cancer patients from the publicly available Duke Breast Cancer magnetic resonance imaging dataset who received neoadjuvant chemotherapy. From pre-treatment dynamic contrast-enhanced magnetic resonance imaging, 534 IBSI-compliant radiomic features were extracted. A five-method consensus feature selection pipeline retained 24 features. Notably, three prediction models were developed: clinical-only (Model A), radiomic-only (Model B), and combined clinical–radiomic (Model C). In total, seven machine learning algorithms were compared using 20 repeated five-fold stratified cross-validation with SMOTE applied within each training fold and assessed on a held-out test set (30%, n=90). Performance was evaluated using AUC with bootstrap confidence intervals, calibration (Brier score), and decision curve analysis. RESULTS:: The pathologic complete response rate was 20.4% (61/299). The combined model (Gradient Boosting) achieved the highest test AUC of 0.723 (95%CI 0.595–0.835), with sensitivity of 0.333 and specificity of 0.833. DeLong tests revealed no statistically significant differences between model pairs (all p>0.05). Decision curve analysis demonstrated the highest net benefit for the combined model across 10–50% threshold probabilities. CONCLUSION:: The combined clinical–radiomic model demonstrated modest discriminative ability for pathologic complete response prediction. The limited sensitivity suggests potential utility as a rule-out test to identify patients unlikely to achieve pathologic complete response. These hypothesis-generating findings require external validation in independent multi-center cohorts.
SN - 0104-4230
SN - 1806-9282
ER -