四平面法在超声乳腺病变计算机辅助诊断中的评价:卡塔尔世界杯8强波胆分析前瞻性单中心研究%阿永平，梁%阿娟，张%阿周，平%阿永峰，赵%阿刘，文刚%阿时，一帆%+中南大学湘雅医学院，湖南省长沙市天心区老东西路172号，410015，中国，86 731 88618403，zhouping1000@hotmail.com %K超声检查%K乳腺肿瘤%K乳腺成像报告和数据系统(双rad) %K乳腺肿瘤诊断%K癌症筛查%K计算机辅助诊断%K乳腺癌%D 2020 %7 5.5.2020 %9原始论文%J JMIR Med Inform %G英文%X背景:计算机辅助诊断(CAD)是一种帮助放射科医生通过超声检查诊断乳腺病变的工具。以前的研究已经证明CAD可以帮助减少放射科医生误诊的发生率。然而，利用诊断平面将CAD应用于乳腺病变的最佳方法尚未得到评估。目的:本研究的目的是比较不同经验水平的放射科医师在使用CAD和四平面法检测乳腺肿瘤时的表现。方法:从2018年11月至2019年10月，我们在研究中招募了以乳房肿块为最显著症状的患者。我们指派了2名超声放射科医生(分别有1年和5年的经验)在没有CAD的情况下阅读乳腺超声图像，然后在使用CAD和四平面方法的情况下进行二次阅读。然后我们比较了两个读数(无CAD和有CAD)的阅读器的诊断性能。配对数据采用McNemar检验进行统计分析。 Results: A total of 331 patients were included in this study (mean age 43.88 years, range 17-70, SD 12.10), including 512 lesions (mean diameter 1.85 centimeters, SD 1.19; range 0.26-9.5); 200/512 (39.1%) were malignant, and 312/512 (60.9%) were benign. For CAD, the area under the receiver operating characteristic curve (AUC) improved significantly from 0.76 (95% CI 0.71-0.79) with the cross-planes method to 0.84 (95% CI 0.80-0.88; P<.001) with the quadri-planes method. For the novice reader, the AUC significantly improved from 0.73 (95% CI 0.69-0.78) for the without-CAD mode to 0.83 (95% CI 0.80-0.87; P<.001) for the combined-CAD mode with the quadri-planes method. For the experienced reader, the AUC improved from 0.85 (95% CI 0.81-0.88) to 0.87 (95% CI 0.84-0.91; P=.15). The kappa indicating consistency between the experienced reader and the novice reader for the combined-CAD mode was 0.63. For the novice reader, the sensitivity significantly improved from 60.0% for the without-CAD mode to 79.0% for the combined-CAD mode (P=.004). The specificity, negative predictive value, positive predictive value, and accuracy improved from 84.9% to 87.8% (P=.53), 76.8% to 86.7% (P=.07), 71.9% to 80.6% (P=.13), and 75.2% to 84.4% (P=.12), respectively. For the experienced reader, the sensitivity improved significantly from 76.0% for the without-CAD mode to 87.0% for the combined-CAD mode (P=.045). The NPV and accuracy moderately improved from 85.8% and 86.3% to 91.0% (P=.27) and 87.0% (P=.84), respectively. The specificity and positive predictive value decreased from 87.4% to 81.3% (P=.25) and from 87.2% to 93.0% (P=.16), respectively. Conclusions: S-Detect is a feasible diagnostic tool that can improve the sensitivity, accuracy, and AUC of the quadri-planes method for both novice and experienced readers while also improving the specificity for the novice reader. It demonstrates important application value in the clinical diagnosis of breast cancer. Trial Registration: ChiCTR.org.cn 1800019649; http://www.chictr.org.cn/showproj.aspx?proj=33094 %M 32369039 %R 10.2196/18251 %U https://medinform.www.mybigtv.com/2020/5/e18251 %U https://doi.org/10.2196/18251 %U http://www.ncbi.nlm.nih.gov/pubmed/32369039