Chen Peijun, Tong Jiahui, Lin Ting, Wang Ying, Yu Yuehui, Chen Menghan, Yang Gaoyi
Department of Ultrasonography, The Second Clinical Medical College, Zhejiang Chinese Medicine University, Hangzhou, China.
Department of Ultrasonography, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Chinese and Western Hospital of Zhejiang Province (Hangzhou Red Cross Hospital), Hangzhou, China.
Gland Surg. 2022 Dec;11(12):1946-1960. doi: 10.21037/gs-22-643.
S-detect is an emerging computer-aided diagnosis (CAD) technique that provides a reference for radiologists to identify breast cancer. Some studies have shown that US (ultrasound) + S-detect can improve the diagnostic accuracy of junior radiologists more than senior radiologists, but the results are inconsistent in various studies. Therefore, this meta-analysis aimed to assess the value of S-detect combined with the US outcomes from senior and junior radiologists for the diagnosis of breast cancer.
We searched the PubMed, Cochrane Library, Embase, Web of Science, and Wanfang databases, China Biology Medicine disc, China National Knowledge Infrastructure (CNKI), and VIP database for trials on the diagnostic accuracy of US + S-detect for the diagnosis of breast masses. The search time frame was from the date of establishment of the database to August 20, 2022. Two researchers independently screened the literature, extracted the information, and evaluated the quality of the included literature using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) scale. StataSE 15.1 software was utilized to assess pooled metrics, including sensitivity, specificity, and the area under the curve (AUC).
A total of 19 articles with 3,349 patients and 3,895 breast masses were included in this meta-analysis. Of these, seventeen articles evaluated the diagnostic performance of senior radiologists' US + S-detect for breast cancer, while twelve articles reported junior radiologists' diagnostic performance. The risk of bias was primarily attributed to patient selection, flow and timing. In the senior radiologist group, the pooled sensitivity and specificity of US + S-detect were 0.93 [95% confidence interval (CI): 0.89-0.95] and 0.86 (95% CI: 0.80-0.90), respectively, with an AUC of 0.96. As for the junior radiologist group, the pooled sensitivity and specificity of US + S-detect were 0.89 (95% CI: 0.83-0.93) and 0.79 (95% CI: 0.72-0.84), respectively, and the AUC was 0.91.
The results of this meta-analysis showed that the pooled sensitivity and the AUC of both the senior and junior radiologist groups were high, with good diagnostic efficacy and high clinical application. However, the results of this study are highly heterogeneous and need to be validated by collecting more high-quality studies and accumulating a larger sample size.
S-detect是一种新兴的计算机辅助诊断（CAD）技术，可为放射科医生识别乳腺癌提供参考。一些研究表明，超声（US）+S-detect对初级放射科医生诊断准确性的提升作用比对高级放射科医生更显著，但不同研究结果并不一致。因此，本荟萃分析旨在评估S-detect联合高级和初级放射科医生的US检查结果对乳腺癌诊断的价值。
我们检索了PubMed、Cochrane图书馆、Embase、Web of Science、万方数据库、中国生物医学文献数据库、中国知网（CNKI）和维普数据库，以查找关于US+S-detect诊断乳腺肿块准确性的试验。检索时间范围为各数据库建库之日至2022年8月20日。两名研究人员独立筛选文献、提取信息，并使用诊断准确性研究质量评估-2（QUADAS-2）量表评估纳入文献的质量。使用StataSE 15.1软件评估合并指标，包括敏感性、特异性和曲线下面积（AUC）。
本荟萃分析共纳入19篇文章，涉及3349例患者和3895个乳腺肿块。其中，17篇文章评估了高级放射科医生US+S-detect对乳腺癌的诊断性能，12篇文章报告了初级放射科医生的诊断性能。偏倚风险主要归因于患者选择、流程和时间安排。在高级放射科医生组中，US+S-detect的合并敏感性和特异性分别为0.93[95%置信区间（CI）：0.89-0.95]和0.86（95%CI：0.80-0.90），AUC为0.96。对于初级放射科医生组，US+S-detect的合并敏感性和特异性分别为0.89（95%CI：0.83-0.93）和0.79（95%CI：0.72-0.84），AUC为0.91。
本荟萃分析结果显示，高级和初级放射科医生组的合并敏感性和AUC均较高，诊断效能良好，临床应用价值高。然而，本研究结果异质性较高，需要通过收集更多高质量研究并积累更大样本量进行验证。
