Kim Kiwook, Song Mi Kyung, Kim Eun-Kyung, Yoon Jung Hyun
Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Seoul, Korea.
Department of Research Affairs, Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea.
Ultrasonography. 2017 Jan;36(1):3-9. doi: 10.14366/usg.16012. Epub 2016 Apr 14.
The purpose of this study was to evaluate the diagnostic performance of S-Detect when applied to breast ultrasonography (US), and the agreement with an experienced radiologist specializing in breast imaging.
From June to August 2015, 192 breast masses in 175 women were included. US features of the breast masses were retrospectively analyzed by a radiologist who specializes in breast imaging and S-Detect, according to the fourth edition of the American College of Radiology Breast Imaging Reporting and Data System lexicon and final assessment categories. Final assessments from S-Detect were in dichotomized form: possibly benign and possibly malignant. Kappa statistics were used to analyze the agreement between the radiologist and S-Detect. Diagnostic performance of the radiologist and S-Detect was calculated, including sensitivity, specificity, positive predictive value (PPV), negative predictive value, accuracy, and area under the receiving operator characteristics curve.
Of the 192 breast masses, 72 (37.5%) were malignant, and 120 (62.5%) were benign. Benign masses among category 4a had higher rates of possibly benign assessment on S-Detect for the radiologist, 63.5% to 36.5%, respectively (P=0.797). When the cutoff was set at category 4a, the specificity, PPV, and accuracy was significantly higher in S-Detect compared to the radiologist (all P<0.05), with a higher area under the receiver operator characteristics curve of 0.725 compared to 0.653 (P=0.038). Moderate agreement (k=0.58) was seen in the final assessment between the radiologist and S-Detect.
S-Detect may be used as an additional diagnostic tool to improve the specificity of breast US in clinical practice, and guide in decision making for breast masses detected on US.
本研究旨在评估S-Detect应用于乳腺超声检查(US)时的诊断性能,以及与一位经验丰富的乳腺影像专家诊断结果的一致性。
纳入2015年6月至8月期间175名女性的192个乳腺肿块。由一位乳腺影像专家和S-Detect根据美国放射学会乳腺影像报告和数据系统第四版词汇表及最终评估类别,对乳腺肿块的超声特征进行回顾性分析。S-Detect的最终评估采用二分法形式:可能为良性和可能为恶性。采用Kappa统计分析专家与S-Detect之间的一致性。计算专家和S-Detect的诊断性能,包括敏感性、特异性、阳性预测值(PPV)、阴性预测值、准确性以及受试者操作特征曲线下面积。
192个乳腺肿块中,72个(37.5%)为恶性,120个(62.5%)为良性。在4a类肿块中,专家对S-Detect评估为可能良性的良性肿块率更高,分别为63.5%和36.5%(P=0.797)。当临界值设定为4a类时,S-Detect的特异性、PPV和准确性显著高于专家(均P<0.05),受试者操作特征曲线下面积为0.725,高于专家的0.653(P=0.038)。专家与S-Detect的最终评估一致性为中等(k=0.58)。
S-Detect可作为一种辅助诊断工具,在临床实践中提高乳腺超声检查的特异性,并为超声检查发现的乳腺肿块的决策提供指导。