Magnetic resonance imaging radiomics for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer
Year: 2026
Type: article
Abstract: 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.
Authors: Emin Demirel, Huseyin Coskun
Institution: Afyonkarahisar Sağlık Bilimleri Üniversitesi
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7225082340