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计算机辅助诊断在乳腺超声中的评估:提高经验不足的放射科医生的诊断性能。

Evaluation of computer-aided diagnosis in breast ultrasonography: Improvement in diagnostic performance of inexperienced radiologists.

机构信息

Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy.

Department of Emergency and Organ Transplantation, Section of Anatomic Pathology, School of Medicine, University "Aldo Moro", 70124 Bari, Italy.

出版信息

Clin Imaging. 2022 Feb;82:150-155. doi: 10.1016/j.clinimag.2021.11.006. Epub 2021 Nov 22.

DOI:10.1016/j.clinimag.2021.11.006
PMID:34826773
Abstract

PURPOSE

To evaluate if a computer-aided diagnosis (CAD) system on ultrasound (US) can improve the diagnostic performance of inexperienced radiologists.

METHODS

We collected ultrasound images of 256 breast lesions taken between March and May 2020. We asked two experienced and two inexperienced radiologists to retrospectively review the US features of each breast lesion according to the Breast Imaging Reporting and Data System (BI-RADS) categories. A CAD examination with S-Detect™ software (Samsung Healthcare, Seoul, South Korea) was conducted retrospectively by another uninvolved radiologist blinded to the BIRADS values previously attributed to the lesions. Diagnostic performances of experienced and inexperienced radiologists and CAD were compared and the inter-observer agreement among radiologists was calculated.

RESULTS

The diagnostic performance of the experienced group in terms of sensitivity was significantly higher than CAD (p < 0.001). Conversely, the diagnostic performance of inexperienced group in terms of both sensitivity and specificity was significantly lower than CAD (p < 0.001). We obtained an excellent agreement in the evaluation of the lesions among the two expert radiologists (Kappa coefficient: 88.7%), and among the two non-expert radiologists (Kappa coefficient: 84.9%).

CONCLUSION

The US CAD system is a useful additional tool to improve the diagnostic performance of the inexperienced radiologists, eventually reducing the number of unnecessary biopsies. Moreover, it is a valid second opinion in case of experienced radiologists.

摘要

目的

评估计算机辅助诊断(CAD)系统在超声(US)中的应用是否能提高经验不足的放射科医生的诊断性能。

方法

我们收集了 2020 年 3 月至 5 月期间的 256 个乳腺病变的超声图像。我们请两名有经验的放射科医生和两名无经验的放射科医生根据乳腺影像报告和数据系统(BI-RADS)类别回顾性地检查每个乳腺病变的 US 特征。另一名不参与的放射科医生对 S-Detect™软件(韩国首尔三星医疗保健公司)进行回顾性 CAD 检查,该医生对之前分配给病变的 BI-RADS 值不知情。比较了有经验和无经验的放射科医生的诊断性能以及放射科医生之间的观察者间一致性。

结果

在敏感性方面,经验丰富组的诊断性能明显高于 CAD(p<0.001)。相反,无经验组在敏感性和特异性方面的诊断性能明显低于 CAD(p<0.001)。我们在两名专家放射科医生之间(Kappa 系数:88.7%)和两名非专家放射科医生之间(Kappa 系数:84.9%)获得了对病变评估的极好一致性。

结论

US CAD 系统是一种有用的辅助工具,可以提高经验不足的放射科医生的诊断性能,最终减少不必要的活检数量。此外,对于有经验的放射科医生来说,这也是一个有效的第二意见。

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