Lee Jeongmin, Kim Sanghee, Kang Bong Joo, Kim Sung Hun, Park Ga Eun
Department of Radiology Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea.
Med Ultrason. 2019 Aug 31;21(3):239-245. doi: 10.11152/mu-1889.
To investigate the effect of a computer-aided diagnosis (CAD) system on breast ultrasound (US) for inexperienced radiologists in describing and determining breast lesions.
Between October 2015 to January 2017, 500 suspicious or probable benign lesions in 413 patients were reviewed. Five experienced readers retrospectively reviewed for each of 100 lesions according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and category, with CAD system (S-detectTM). The readers then made final decisions by combining CAD results to their US results. Using the nested experiment design, five inexperienced readers were asked to select the appropriate BI-RADS lexicons, categories, CAD results, and combination results for each of the 100 lesions, retrospectively. Diagnostic performance of experienced and inexperienced radiologists and CAD were assessed. For each case, agreements in the lexicons and categories were analyzed among the experienced reader, inexperienced reader and CAD.
Indicators of the diagnostic performance for breast malignancy of the experienced group (AUC=0.83, 95%CI [0.80, 0.86]) were similar or higher than those of CAD (AUC = 0.79, 95%CI[0.74, 0.83], p=0.101), except for specificity. Conversely, indicators of diagnostic performance of inexperienced group (AUC=0.65, 95%CI[0.58, 0.71]) did not differ from or were lower than those of CAD(AUC=0.73, 95%CI[0.67, 0.78], p=0.013). Also, the diagnostic performance of the inexperienced group after combination with the CAD result was significantly improved (0.71, 95% CI [0.65, 0.77], p=0.001), whereas that of the experienced group did not change after combination with the CAD result, except for specificity and positive predictive value (PPV). Kappa values for the agreement of the categorization between CAD and each radiologist group were increased after applying the CAD result to their result of general US. Especially, the increase of the Kappa value was higher in the inexperienced group than in the experienced group. Also, for all the lexicons, the Kappa values between the experienced group and CAD were higher than those between the inexperienced group and CAD.
By using the CAD system for classification of breast lesions, diagnostic performance of the inexperienced radiologists for malignancy was significantly improved, and better agreement was observed in lexicons between the experienced group and CAD than between the inexperienced group and CAD. CAD may be beneficial and educational for the inexperienced group.
探讨计算机辅助诊断（CAD）系统对经验不足的放射科医生在描述和判定乳腺超声（US）病变方面的影响。
回顾2015年10月至2017年1月期间413例患者的500个可疑或可能为良性的病变。五名经验丰富的阅片者根据乳腺影像报告和数据系统（BI-RADS）词典及分类，使用CAD系统（S-detectTM）对100个病变中的每一个进行回顾性阅片。阅片者随后将CAD结果与他们的US结果相结合做出最终诊断。采用嵌套实验设计，要求五名经验不足的阅片者对100个病变中的每一个进行回顾性选择合适的BI-RADS词典、分类、CAD结果及综合结果。评估经验丰富和经验不足的放射科医生以及CAD的诊断性能。对于每例病例，分析经验丰富的阅片者、经验不足的阅片者和CAD之间在词典和分类方面的一致性。
经验丰富组对乳腺恶性病变的诊断性能指标（AUC=0.83，95%CI[0.80, 0.86]）与CAD的指标（AUC = 0.79，95%CI[0.74, 0.83]，p=0.101）相似或更高，除了特异性。相反，经验不足组的诊断性能指标（AUC=0.65，95%CI[0.58, 0.71]）与CAD的指标无差异或低于CAD（AUC=0.73，95%CI[0.67, 0.78]，p=0.013）。此外，经验不足组结合CAD结果后的诊断性能显著提高（0.71，95% CI [0.65, 0.77]，p=0.001），而经验丰富组结合CAD结果后除特异性和阳性预测值（PPV）外诊断性能未改变。将CAD结果应用于其普通US结果后，CAD与每个放射科医生组之间分类一致性的Kappa值增加。特别是，经验不足组的Kappa值增加幅度高于经验丰富组。而且，对于所有词典，经验丰富组与CAD之间的Kappa值高于经验不足组与CAD之间的Kappa值。
通过使用CAD系统对乳腺病变进行分类，经验不足的放射科医生对恶性病变的诊断性能显著提高，并且经验丰富组与CAD之间在词典方面的一致性优于经验不足组与CAD之间的一致性。CAD对经验不足的放射科医生可能是有益且具有教育意义的。
