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计算机辅助诊断在乳腺超声检查中的应用:根据不同经验水平评估诊断性能及放射科医生之间的一致性

Application of Computer-Aided Diagnosis on Breast Ultrasonography: Evaluation of Diagnostic Performances and Agreement of Radiologists According to Different Levels of Experience.

作者信息

Cho Eun, Kim Eun-Kyung, Song Mi Kyung, Yoon Jung Hyun

机构信息

Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea.

Department of Research Affairs, Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea.

出版信息

J Ultrasound Med. 2018 Jan;37(1):209-216. doi: 10.1002/jum.14332. Epub 2017 Aug 1.

Abstract

OBJECTIVES

To investigate the feasibility of a computer-aided diagnosis (CAD) system (S-Detect; Samsung Medison, Co, Ltd, Seoul, Korea) for breast ultrasonography (US), according to radiologists with various degrees of experience in breast imaging.

METHODS

From December 2015 to March 2016, 119 breast masses in 116 women were included. Ultrasonographic images of the breast masses were retrospectively reviewed and analyzed by 2 radiologists specializing in breast imaging (7 and 1 years of experience, respectively) and S-Detect, according to the individual ultrasonographic descriptors from the fifth edition of the American College of Radiology Breast Imaging Reporting and Data System and final assessment categories. Diagnostic performance and the interobserver agreement among the radiologists and S-Detect was calculated and compared.

RESULTS

Among the 119 breast masses, 54 (45.4%) were malignant, and 65 (54.6%) were benign. Compared to the radiologists, S-Detect had higher specificity (90.8% compared to 49.2% and 55.4%) and positive predictive value (PPV; 86.7% compared to 60.7% and 63.8%) (all P < .001). Both radiologists had significantly improved specificity, PPV, and accuracy when using S-Detect compared to US alone (all P < .001). The area under the receiving operating characteristic curves of the both radiologists did not show a significant improvement when applying S-Detect compared to US alone (all P > .05). Moderate agreement was seen in final assessments made by each radiologist and S-Detect (κ = 0.40 and 0.45, respectively).

CONCLUSIONS

S-Detect is a clinically feasible diagnostic tool that can be used to improve the specificity, PPV, and accuracy of breast US, with a moderate degree of agreement in final assessments, regardless of the experience of the radiologist.

摘要

目的

根据在乳腺成像方面具有不同经验程度的放射科医生,探讨计算机辅助诊断(CAD)系统(S-Detect;三星麦迪逊有限公司,韩国首尔)用于乳腺超声检查(US)的可行性。

方法

2015年12月至2016年3月,纳入116名女性的119个乳腺肿块。根据美国放射学会乳腺影像报告和数据系统第五版的个体超声描述符及最终评估类别,由2名专门从事乳腺成像的放射科医生(分别有7年和1年经验)和S-Detect对乳腺肿块的超声图像进行回顾性审查和分析。计算并比较放射科医生和S-Detect之间的诊断性能及观察者间一致性。

结果

在119个乳腺肿块中,54个(45.4%)为恶性,65个(54.6%)为良性。与放射科医生相比,S-Detect具有更高的特异性(分别为90.8%,而放射科医生为49.2%和55.4%)和阳性预测值(PPV;分别为86.7%,而放射科医生为60.7%和63.8%)(所有P < 0.001)。与单独使用超声相比,两名放射科医生在使用S-Detect时特异性、PPV和准确性均有显著提高(所有P < 0.001)。与单独使用超声相比,应用S-Detect时两名放射科医生的受试者操作特征曲线下面积均未显示出显著改善(所有P > 0.05)。每位放射科医生和S-Detect的最终评估显示中度一致性(κ分别为0.40和0.45)。

结论

S-Detect是一种临床可行的诊断工具,可用于提高乳腺超声检查的特异性、PPV和准确性,在最终评估中具有中度一致性,无论放射科医生的经验如何。

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