A Simple and Robust MRI Radiomics Feature for Predicting Pathological Complete Response: A Proof‐of‐Concept Study in Breast Cancer Neoadjuvant Chemotherapy

BACKGROUND: Early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer remains challenging. This study aimed to explore the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired after the second cycle of NAC for early prediction of pCR. METHODS AND RESULTS: A retrospective analysis was conducted on 119 breast cancer patients who underwent NAC at our hospital between March 2020 and August 2023. Patients were categorized into pCR and non-pCR groups based on postoperative Miller-Payne pathological grading as the gold standard. Tumor regions of interest (ROIs) were manually delineated on phase-three DCE-MRI sequences. PyRadiomics extracted 851 features. A rigorous dimensionality reduction process-including stability screening, intergroup differential analysis, and decorrelation analysis-yielded 88 key features. LASSO regression (10-fold cross-validation) ultimately selected three optimal wavelet-based texture features that formed the core components of our radiomics signature: wavelet. LLH_glcm_Idn (inverse difference normalized), wavelet. LLH_glcm_MCC (maximum correlation coefficient), and wavelet. LHL_firstorder_Skewness. The dataset was randomly split into a training set (83 cases) and a validation set (36 cases) at a 7:3 ratio. A support vector machine (SVM) classifier was constructed, and model performance and clinical utility were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis. Among 119 patients, 43(36.13%) achieved pCR. The constructed radiomics model demonstrated an area under the curve (AUC) of 0.667 and 0.647 in the training and validation sets, respectively, with accuracy rates of 66.27% and 73.49%. Decision curve analysis suggested potential clinial utility under hypothetical scenarios when the probability threshold exceeded 0.3, although this finding is exploratory and requires prospective validation. CONCLUSION: This study developed and internally validated a minimalist radiomics model based on mid-treatment MRI, demonstrating moderate and stable predictive capability for pCR after NAC in breast cancer and showing potential for aiding clinical decision-making. As an exploratory proof-of-concept study, the findings underscore the necessity for future multicenter external validation and integration of multimodal features.