Habitat-Based MRI Radiomics for Predicting Breast-Conserving Surgery Feasibility After Neoadjuvant Chemotherapy in...

Neoadjuvant chemotherapy (NAC) is widely used in the management of breast cancer, as it can downstage tumors and increase the rate of breast-conserving surgery (BCS). Accurate preoperative prediction of BCS eligibility after NAC is essential for optimizing treatment planning and surgical decision-making. However, reliable noninvasive tools for evaluating BCS eligibility remain limited.

This retrospective multicenter study included 315 patients with pathologically confirmed breast cancer who underwent NAC between January 2018 and December 2024. Patients from Center 1 (n = 227) were randomly divided into a training cohort (n = 181) and an internal validation cohort (n = 46), while data from Center 2 (n = 88) served as an external validation cohort. Habitat radiomics features were extracted from dynamic contrast-enhanced MRI (DCE-MRI) to construct a habitat model. Clinical and radiological variables associated with BCS feasibility were identified using univariate and multivariate logistic regression to establish a clinical-radiological model. A combined model integrating radiomic and clinical predictors was subsequently developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).

The overall BCS rate was 73.65% (232/315). The habitat model achieved AUCs of 0.886, 0.778, and 0.725 in the training, internal validation, and external validation cohorts, respectively. The clinical-radiological model yielded AUCs of 0.742, 0.733, and 0.707. The combined model demonstrated improved performance with AUCs of 0.910, 0.839, and 0.755 across the 3 cohorts.

The combined model integrating habitat radiomics and clinical-radiological variables demonstrated favorable performance for predicting BCS feasibility after NAC. This noninvasive approach may assist clinicians in preoperative surgical planning and facilitate individualized breast-conserving treatment strategies.