Evaluating breast ultrasound S-detect image analysis for small focal breast lesions - Xing, Boyuan; Chen, Xiangyi; Wang, Yalin; Li, Shuang; Liang, Ying-Kui; Wang, Dawei (Frontiers in Oncology, 2022)
Background: S-Detect is a computer-assisted, artificial intelligence based system of image analysis, that has been integrated into the software of ultrasound (US) equipment, and has the capacity to independently differentiate between benign and malignant focal breast lesions. Since the revision and upgrade in both the breast imaging-reporting and data system (BI-RADS) US lexicon and the S-Detect software in 2013, evidence has accumulated that supports improved accuracy and specificity of radiologists’ assessment of breast lesions. However, such assessment using S-Detect technology to distinguish malignant from breast lesions with a diameter no greater than 2 centimeters requires further investigation. Methods: The ultrasound images of focal breast lesions from 295 patients in our hospital from January 2019 to June 2022 were collected. The BI-RADS data were evaluated by the embedded program and as manually modified prior to the determination of a pathological diagnosis. The receiver operator characteristics (ROC) curves were constructed to compare the diagnostic accuracy between the assessments of the conventional ultrasound images, the S-Detect classification, and the combination of the two. Results: There were 326 lesions identified in 295 patients, of which pathological confirmation demonstrated that 239 were benign, and 87 were malignant. The sensitivity, specificity, and accuracy of the conventional imaging group were 75.86%, 93.31%, and 88.65%. The sensitivity, specificity, and accuracy of the S-Detect classification group were 87.36%, 88.28%, and 88.04%. The assessment of the amended combination of S-Detect with ultrasound image analysis (Co-Detect group) was improved with sensitivity, specificity, and accuracy of 90.80%, 94.56%, and 93.56%. Comparison of the diagnostic accuracy using area under curves of the conventional US group, S-Detect group, and the Co-Detect group were 0.85, 0.88 and 0.93, respectively. The Co-Detect group had the best diagnostic efficiency compared with the conventional US group (Z=3.882, P=0.0001) and the S-Detect group (Z=3.861, P=0.0001). There was no significant difference in distinguishing benign from malignant small breast lesions comparing conventional US and S-Detect techniques. Conclusions: The addition of S-Detect technology to conventional US imaging provided a novel and feasible method to differentiate benign from malignant small breast nodules.