Multimodal habitat radiomics based on automated breast volume scanning and conventional ultrasound for risk stratification of biopsy-confirmed BI-RADS 4A breast lesions.
Authors
Affiliations (1)
Affiliations (1)
- Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People's Hospital, WuHu), Wuhu, Anhui, China.
Abstract
To develop and validate a multimodal habitat radiomics model integrating automated breast volume scanning (ABVS) and conventional two-dimensional ultrasound (2D-US) for risk stratification of biopsy-selected BI-RADS 4A breast lesions. This retrospective single-center study included 160 consecutive patients with BI-RADS 4A breast lesions confirmed by histopathology between January 2024 and May 2025. Tumoral and peritumoral regions were manually segmented on ABVS and 2D-US images. Habitat subregions were generated using a local spatial autocorrelation-based heterogeneity analysis. Radiomic features were extracted using PyRadiomics. Feature selection was performed using t-test filtering, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression within the training folds. Multiple machine learning classifiers were constructed using three-fold cross-validation. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, calibration curves, and decision curve analysis (DCA). Of the 160 lesions, 51 (31.9%) were malignant and 109 (68.1%) were benign. The habitat radiomics model outperformed the clinical-ultrasound model. The optimal multilayer perceptron classifier achieved an AUC of 0.910 and an accuracy of 0.869 in the validation cohort. Decision curve analysis demonstrated higher net benefit across a range of threshold probabilities, and calibration curves indicated good agreement between predicted and observed outcomes. Multimodal habitat radiomics integrating ABVS and conventional ultrasound demonstrated promising performance for risk stratification of biopsy-selected BI-RADS 4A lesions and may provide supportive information for individualized clinical decision-making. Further prospective multicenter validation is warranted before clinical application.