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Ultrasound-based deep learning radiomics for multi-stage assisted diagnosis in reducing unnecessary biopsies of
Xiangyu Lu1, Yun Lu2, Wuyuan Zhao1
1School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
Background:
Even with the Breast Imaging Reporting and Data System (BI-RADS) guiding risk stratification on ultrasound (US) images, inconsistencies in diagnostic accuracy still exist, leading patients being subjected to unnecessary biopsies in clinical practice. This study investigated the construction of deep learning radiomics (DLR) models to improve the diagnostic consistency and reduce the unnecessary biopsies for BI-RADS 4A lesions.
Methods:
A total of 746 patients with breast lesions were enrolled in this retrospective study. Two DLR models based on US images and clinical variables were developed to conduct breast lesion risk re-stratification as BI-RADS 3 or lower and BI-RADS 4A or higher (DLR_LH), while simultaneously identifying BI-RADS 4A lesions with low malignancy probabilities to avoid unnecessary biopsy (DLR_BM). A three-round reader study with a two-stage artificial intelligence (AI)-assisted diagnosis process was performed to verify the assistive capability and practical benefits of the models in clinical applications.
Results:
The DLR_LH model achieved areas under the receiver operating characteristic curve (AUCs) of 0.963 and 0.889 with sensitivities of 92.0% and 83.3%, in the internal and external validation cohorts, respectively. The DLR_BM model exhibited AUCs of 0.977 and 0.942, with sensitivities of 94.1% and 86.4%, respectively. Both models were evaluated using integrated features of US images and clinical variables. Ultimately, 27.7% of BI-RADS 4A lesions avoided unnecessary biopsies. In the three-round reader study, all readers achieved significantly higher diagnostic accuracy and specificity, while maintaining outstanding sensitivity comparable to human experts, both before and after model assistance (P<0.05). These findings demonstrate the positive impact of the DLR models in assisting radiologists to enhance their diagnostic capabilities.
Conclusions:
The models performed well in breast US imaging interpretation and BI-RADS risk re-stratification, and demonstrated potential in reducing unnecessary biopsies of BI-RADS 4A lesions, indicating the promising applicability of the DLR models in clinical diagnosis.
