A 2.5D multichannel deep learning model using contrast-enhanced ultrasound for predicting malignancy in breast nodules: a two-center study

Objective To evaluate a novel multichannel deep learning (DL) model using contrast-enhanced ultrasound (CEUS) data with multiple regions of interest (ROIs) and time-intensity curve (TIC)-derived key frames for predicting breast nodule malignancy. Clinical features were integrated into a combined model for robust, generalizable breast lesion classification. The model was further evaluated as an AI-assisted decision support tool through direct comparison with BI-RADS classification by senior radiologists. Methods This retrospective two-center study enrolled 141 patients with breast nodules: 89 from Institution 1 (June 2016–October 2017; training cohort, n=62; internal validation, n=27) and 52 from Institution 2 (November 2022–November 2024; external validation). BI-RADS categories were extracted from original radiology reports and binarized at ≥4B for malignancy prediction. Tumors were segmented on B-mode and CEUS images to define intratumoral ROIs, tumor bounding boxes, and peritumoral expansions (2 mm and 5 mm). TIC phases (initial, ascending, peak, descending, wash-out) were stacked into multichannel 2.5-dimensional (2.5D) inputs. DenseNet201 models, pretrained on ImageNet, were trained for 2D and 2.5D DL across ROI types. Outputs from the clinical model and optimal intratumoral plus 2-mm peritumoral ROI models were fused via logistic regression. Performance was evaluated using area under the receiver operating characteristic curve (AUC), Hosmer–Lemeshow calibration, decision curve analysis (DCA).and DeLong test for comparison with BI-RADS. Results Among 2.5D models, the multichannel variant with intratumoral plus 2-mm peritumoral ROI showed highest external validation performance. The combined model, constructed by fusing the output of the optimal MultiChannel_2.5D_DL architecture (intratumoral + 2-mm peritumoral ROI) with the 2D_DL and clinical models via logistic regression, outperformed individual models externally (AUC 0.949 [95% CI: 0.888, 1.000] vs. clinical AUC 0.821 [95% CI: 0.671, 0.970], p=0.04; vs. 2D AUC 0.789 [95% CI: 0.660, 0.918], p=0.01; vs. 2.5D AUC 0.824 [95% CI: 0.677, 0.972], p=0.03). In direct comparison in the external validation cohort, the combined model demonstrated diagnostic performance comparable to that of senior radiologists (AUC 0.949 [95% CI: 0.888, 1.000] vs. 0.897 [95% CI: 0.808, 0.986], p=0.15). Conclusion This combined model, integrating the optimal MultiChannel_2.5D_DL output with 2D_DL and clinical features, offers promising accuracy and generalizability as a decision support tool for CEUS-based breast nodule malignancy prediction, potentially assisting radiologists in reducing interobserver variability and unnecessary biopsies.