Breast cancer (Dove Medical Press) · 2026
Integrating Ultrasound and DCE-MRI Improves Accuracy in Differentiating Breast Adenosis from Carcinoma.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhang Yuli, Fan Yuhong, Yao Xiaojing, Zhou Haiyan, Han Dong, Xiao Yao, Zhong Peng, Fang Jingqin
Аннотация
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.
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.