Radiomics-driven prediction of pathologic complete response in non-mass breast cancer using post-neoadjuvant...

This study aims to evaluate the clinical utility of a radiomics model derived from post-neoadjuvant chemotherapy (post-NAC) preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting pathologic complete response (pCR) to NAC in patients with breast cancer exhibiting non-mass lesions (NMLs).

This retrospective study included patients with biopsy-proven breast cancer with NMLs who underwent pre-treatment DCE-MRI and completed standard NAC. Patients were randomly assigned to training and validation cohorts in a 7:3 ratio. Three-dimensional regions of interest (ROIs) of the tumors were manually delineated on pre-NAC DCE-MRI images and spatially registered to post-NAC preoperative images. Radiomic features were extracted from the post-NAC preoperative DCE-MRI ROIs using the Deepwise Multimodal Research Platform. After dimensionality reduction and feature selection, predictive classifiers were constructed based on a logistic regression algorithm for the radiomics-only model and the combined radiomics-clinical model. Subsequently, a triple-integration model further incorporated radiologist assessment. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. SHapley Additive exPlanations (SHAP) analysis was applied to identify the most influential features.

A total of 121 patients were included (training: n = 85; validation: n = 36), of whom 56 achieved pCR. The radiomics-only model demonstrated good discriminative performance (training AUC: 0.927; validation AUC: 0.867), outperforming both the clinical data model (AUCs: 0.577, 0.608) and radiologist assessment (AUCs: 0.708, 0.571). Incorporating clinical variables further improved predictive accuracy (training AUC: 0.933; validation AUC: 0.870). The triple-integration model attained AUCs of 0.936 and 0.810, with no statistically significant difference compared with the radiomics-only model P = 0.450 and P = 0.235 for training and validation, respectively. In addition, SHAP analysis showed radiomic features contributed most to prediction, followed by human epidermal growth factor receptor 2 and hormone receptor status.

Post-NAC preoperative DCE-MRI-based radiomics provides a non-invasive method for predicting pCR in non-mass enhancement breast cancer. The combined radiomics-clinical model achieves superior performance and offers potential value for individualized NAC response assessment. Radiomic features effectively characterize the chemotherapy-altered tissue phenotype, offering an objective and quantitative approach for preoperative treatment response assessment in complex NML-type breast cancer, supporting individualized treatment planning.

Accurate early prediction of pCR could help identify patients most likely to benefit from NAC and avoid ineffective treatment in non-responders. The developed radiomics model offers an interpretable and reproducible tool; upon successful external validation, it has the potential to support personalized treatment planning in patients with NML-type breast cancer.