Barczyński Marcin, Stopa-Barczyńska Małgorzata, Wojtczak Beata, Czarniecka Agnieszka, Konturek Aleksander
Department of Endocrine Surgery, Third Chair of General Surgery, Jagiellonian University Medical College, Kraków, Poland.
Clinical Ward of General Surgery and Oncology, Gabriel Narutowicz Memorial Municipal Hospital, Kraków, Poland.
Gland Surg. 2020 Feb;9(Suppl 2):S77-S85. doi: 10.21037/gs.2019.12.23.
In recent years well-recognized scientific societies introduced guidelines for ultrasound (US) malignancy risk stratification of thyroid nodules. These guidelines categorize the risk of malignancy in relation to a combination of several US features. Based on these US image lexicons an US-based computer-aided diagnosis (CAD) systems were developed. Nevertheless, their clinical utility has not been evaluated in any study of surgeon-performed office US of the thyroid. Hence, the aim of this pilot study was to validate s-Detect mode in semi-automated US classification of thyroid lesions during surgeon-performed office US.
This is a prospective study of 50 patients who underwent surgeon-performed thyroid US (basic US skills without CAD with CAD expert US skills without CAD) in the out-patient office as part of the preoperative workup. The real-time CAD system software using artificial intelligence (S-Detect for Thyroid; Samsung Medison Co.) was integrated into the RS85 US system. Primary outcome was CAD system added-value to the surgeon-performed office US evaluation. Secondary outcomes were: diagnostic accuracy of CAD system, intra and interobserver variability in the US assessment of thyroid nodules. Surgical pathology report was used to validate the pre-surgical diagnosis.
CAD system added-value to thyroid assessment by a surgeon with basic US skills was equal to 6% (overall accuracy of 82% for evaluation with CAD 76% for evaluation without CAD system; P<0.001), and final diagnosis was different than predicted by US assessment in 3 patients (1 more true-positive and 2 more true-negative results). However, CAD system was inferior to thyroid assessment by a surgeon with expert US skills in 6 patients who had false-positive results (P<0.001).
The sensitivity and negative predictive value of CAD system for US classification of thyroid lesions were similar as surgeon with expert US skills whereas specificity and positive predictive value were significantly inferior but markedly better than judgement of a surgeon with basic US skills alone.
近年来，一些知名科学学会推出了甲状腺结节超声（US）恶性风险分层指南。这些指南根据多种超声特征的组合对恶性风险进行分类。基于这些超声图像术语，开发了基于超声的计算机辅助诊断（CAD）系统。然而，在任何关于外科医生进行的甲状腺门诊超声检查的研究中，它们的临床效用尚未得到评估。因此，本初步研究的目的是在外科医生进行的甲状腺门诊超声检查中，验证s-Detect模式在甲状腺病变半自动超声分类中的有效性。
这是一项对50例患者的前瞻性研究，这些患者在门诊接受了外科医生进行的甲状腺超声检查（具备基本超声技能但无CAD、具备CAD、具备专家级超声技能但无CAD），作为术前检查的一部分。使用人工智能的实时CAD系统软件（甲状腺S-Detect；三星麦迪逊公司）被集成到RS85超声系统中。主要结果是CAD系统对外科医生进行的甲状腺门诊超声评估的附加值。次要结果包括：CAD系统的诊断准确性、甲状腺结节超声评估中观察者内和观察者间的变异性。手术病理报告用于验证术前诊断。
对于具备基本超声技能的外科医生，CAD系统对甲状腺评估的附加值为6%（使用CAD评估的总体准确率为82%，不使用CAD系统评估的准确率为76%；P<0.001），最终诊断与超声评估预测的不同，有3例患者（1例假阳性和2例假阴性结果）。然而，在6例假阳性结果的患者中，CAD系统不如具备专家级超声技能的外科医生对甲状腺的评估（P<0.001）。
CAD系统对甲状腺病变超声分类的敏感性和阴性预测值与具备专家级超声技能的外科医生相似，而特异性和阳性预测值明显较低，但明显优于仅具备基本超声技能的外科医生的判断。
