Integrating Ultrasound and DCE-MRI Improves Accuracy in Differentiating Breast Adenosis from Carcinoma

Background: Discriminating breast adenosis from carcinoma remains challenging due to their overlapping imaging appearances. This study aimed to develop and validate a fusion model combining ultrasound (US) and MRI-based radiomics for improved differentiation. Methods: In this retrospective study, 260 patients (147 adenosis, 113 carcinoma) with preoperative US and MRI from March 2019 to July 2025 were enrolled and randomly split into training (n=182) and testing (n=78) cohorts at a 7:3 ratio. Radiomic features were extracted from grayscale US images depicting the largest lesion diameter and from the second-phase enhancement of DCE-MRI. After selection via maximum relevance minimum redundancy(mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) regression, radiomics models (US_Rad, MRI_Rad) were built based on selected features using the optimal algorithm among 12 candidates. A BI-RADS model based on significant US and MRI BI-RADS features was also constructed. A fusion model integrated US_Rad, MRI_Rad, BI-RADS, and age. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of the four models. Results: In the training cohort, the area under the curve (AUC) for BI-RADS and radiomic models based on US (US_Rad) and MRI (MRI_Rad) were 0.89 (95% CI, 0.84– 0.93), 0.84 (95% CI, 0.78– 0.90), and 0.85 (95% CI, 0.79– 0.90), respectively. In the testing cohort, the AUCs were 0.91 (95% CI, 0.83– 0.97), 0.82 (95% CI, 0.73– 0.91), and 0.82 (95% CI, 0.73– 0.90). The fusion model achieved superior AUCs of 0.96 (95% CI, 0.94– 0.98, training) and 0.97 (95% CI, 0.92– 1.00, testing), significantly outperforming both the unimodal radiomic model and the BI-RADS model (all p < 0.01). Conclusion: Our findings suggest that the US-MRI radiomics fusion model exhibits potential in distinguishing breast adenosis from carcinoma, which may help reduce unnecessary surgeries. However, further multi-center validation is required to evaluate its generalizability before clinical application.