Abstract：
OBJECTIVE: Artificial intelligence (AI) systems have been increasingly applied to breast ultrasonography. They are expected to decrease the workload of radiologists and to improve diagnostic accuracy. The aim of this study is to evaluate the performance of an AI system for the BI-RADS category assessment in breast masses detected on breast ultrasound. MATERIALS AND METHODS: A total of 715 masses detected in 530 patients were analyzed. Three breast imaging centers of the same institution and nine breast radiologists participated in this study. Ultrasound was performed by one radiologist who obtained two orthogonal views of each detected lesion. These images were retrospectively reviewed by a second radiologist blinded to the patient\'s clinical data. A commercial AI system evaluated images. The level of agreement between the AI system and the two radiologists and their diagnostic performance were calculated according to dichotomic BI-RADS category assessment.
RESULTS: This study included 715 breast masses. Of these, 134 (18.75%) were malignant, and 581 (81.25%) were benign. In discriminating benign and probably benign from suspicious lesions, the agreement between AI and the first and second radiologists was moderate statistically. The sensitivity and specificity of radiologist 1, radiologist 2, and AI were calculated as 98.51% and 80.72%, 97.76% and 75.56%, and 98.51% and 65.40%, respectively. For radiologist 1, the positive predictive value (PPV) was 54.10%, the negative predictive value (NPV) was 99.58%, and the accuracy was 84.06%. Radiologist 2 achieved a PPV of 47.99%, NPV of 99.32%, and accuracy of 79.72%. The AI system exhibited a PPV of 39.64%, NPV of 99.48%, and accuracy of 71.61%. Notably, none of the lesions categorized as BI-RADS 2 by AI were malignant, while 2 of the lesions classified as BI-RADS 3 by AI were subsequently confirmed as malignant. By considering AI-assigned BI-RADS 2 as safe, we could potentially avoid 11% (18 out of 163) of benign lesion biopsies and 46.2% (110 out of 238) of follow-ups.
CONCLUSIONS: AI proves effective in predicting malignancy. Integrating it into the clinical workflow has the potential to reduce unnecessary biopsies and short-term follow-ups, which, in turn, can contribute to sustainability in healthcare practices.
RESULTS: This study included 715 breast masses. Of these, 134 (18.75%) were malignant, and 581 (81.25%) were benign. In discriminating benign and probably benign from suspicious lesions, the agreement between AI and the first and second radiologists was moderate statistically. The sensitivity and specificity of radiologist 1, radiologist 2, and AI were calculated as 98.51% and 80.72%, 97.76% and 75.56%, and 98.51% and 65.40%, respectively. For radiologist 1, the positive predictive value (PPV) was 54.10%, the negative predictive value (NPV) was 99.58%, and the accuracy was 84.06%. Radiologist 2 achieved a PPV of 47.99%, NPV of 99.32%, and accuracy of 79.72%. The AI system exhibited a PPV of 39.64%, NPV of 99.48%, and accuracy of 71.61%. Notably, none of the lesions categorized as BI-RADS 2 by AI were malignant, while 2 of the lesions classified as BI-RADS 3 by AI were subsequently confirmed as malignant. By considering AI-assigned BI-RADS 2 as safe, we could potentially avoid 11% (18 out of 163) of benign lesion biopsies and 46.2% (110 out of 238) of follow-ups.
CONCLUSIONS: AI proves effective in predicting malignancy. Integrating it into the clinical workflow has the potential to reduce unnecessary biopsies and short-term follow-ups, which, in turn, can contribute to sustainability in healthcare practices.
摘要：
目的：人工智能（AI）系统已越来越多地应用于乳腺超声检查。预计它们将减少放射科医师的工作量并提高诊断准确性。这项研究的目的是评估AI系统在乳腺超声检测到的乳腺肿块中进行BI-RADS类别评估的性能。材料与方法：对530例患者中检测到的715个肿块进行分析。同一机构的三个乳腺成像中心和九名乳腺放射科医师参与了这项研究。超声由一名放射科医师进行，他获得了每个检测到的病变的两个正交视图。由对患者临床数据不知情的第二位放射科医师对这些图像进行回顾性审查。商业AI系统评估图像。根据二元BI-RADS类别评估计算AI系统与两位放射科医师之间的一致性水平及其诊断性能。
结果：本研究包括715个乳腺肿块。其中,134例（18.75%）为恶性，581例(81.25%)为良性。在区分良性和可能良性与可疑病变时，AI与第一和第二放射科医师之间的一致性在统计学上是中等的.放射科医师1、放射科医师2和AI的敏感性和特异性分别计算为98.51%和80.72%,97.76%和75.56%，98.51%和65.40%，分别。对于放射科医生1，阳性预测值(PPV)为54.10%，阴性预测值(NPV)为99.58%，准确率为84.06%。放射科医生2的PPV为47.99%，NPV为99.32%，准确率为79.72%。AI系统表现出39.64%的PPV,NPV为99.48%，准确率为71.61%。值得注意的是,AI分类为BI-RADS2的病变均无恶性，而AI分类为BI-RADS3的2个病变随后被确认为恶性。通过将AI分配的BI-RADS2视为安全的，我们有可能避免11%(163例中的18例)的良性病变活检和46.2%(238例中的110例)的随访.
结论：AI在预测恶性肿瘤方面被证明是有效的。将其整合到临床工作流程中有可能减少不必要的活检和短期随访，which,反过来,可以促进医疗保健实践的可持续性。
结果：本研究包括715个乳腺肿块。其中,134例（18.75%）为恶性，581例(81.25%)为良性。在区分良性和可能良性与可疑病变时，AI与第一和第二放射科医师之间的一致性在统计学上是中等的.放射科医师1、放射科医师2和AI的敏感性和特异性分别计算为98.51%和80.72%,97.76%和75.56%，98.51%和65.40%，分别。对于放射科医生1，阳性预测值(PPV)为54.10%，阴性预测值(NPV)为99.58%，准确率为84.06%。放射科医生2的PPV为47.99%，NPV为99.32%，准确率为79.72%。AI系统表现出39.64%的PPV,NPV为99.48%，准确率为71.61%。值得注意的是,AI分类为BI-RADS2的病变均无恶性，而AI分类为BI-RADS3的2个病变随后被确认为恶性。通过将AI分配的BI-RADS2视为安全的，我们有可能避免11%(163例中的18例)的良性病变活检和46.2%(238例中的110例)的随访.
结论：AI在预测恶性肿瘤方面被证明是有效的。将其整合到临床工作流程中有可能减少不必要的活检和短期随访，which,反过来,可以促进医疗保健实践的可持续性。
