PURPOSE:To develop and externally validate an ultrasound-based habitat subregional radiomics model for preoperative prediction of invasive breast cancer with concomitant ductal carcinoma in situ (IBC-DCIS). METHODS:A total of 1063 pathologically confirmed breast cancer patients from two centers were retrospectively enrolled and divided into a training cohort (n = 637) and an external validation cohort (n = 426). Tumor regions of interest were manually delineated on two-dimensional ultrasound images and further partitioned into three intratumoral habitat subregions using unsupervised clustering. Radiomics features were extracted from the whole tumor and each subregion. Feature selection was performed using Pearson correlation analysis and least absolute shrinkage and selection operator regression. Multiple machine learning models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals, calibration curves, and decision curve analysis. Model comparisons were conducted using the DeLong test. RESULTS:The support vector machine-based combined model achieved the highest AUC in the external validation cohort, with an AUC of 0.910 (95% CI: 0.883-0.936), and showed acceptable calibration and clinical net benefit across a broad range of threshold probabilities. DeLong test results showed that the combined model significantly outperformed imaging-based and single-region radiomics models (p < 0.05). To account for potential class imbalance, model performance was further assessed using multiple complementary metrics, including sensitivity, specificity, predictive values, balanced accuracy, F1-score, PR-AUC, and Brier score. CONCLUSION:An ultrasound-based habitat subregional radiomics model showed favorable performance for the preoperative prediction of IBC-DCIS and may provide supplementary information for preoperative risk stratification.
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