Clinical validation of S-DetectTM mode in semi-automated ultrasound classification of thyroid lesions in surgical office

Abstract

Background: 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 TM mode in semi-automated US classification of thyroid lesions during surgeon-performed office US.Methods: This is a prospective study of 50 patients who underwent surgeon-performed thyroid US (basic US skills without CAD vs. with CAD vs. 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 TM 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.Results: 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 vs. 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). Conclusions: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.

S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules

“…The only study performed in a similar population to ours (but including a lower number of lesions) was the one by Barczyński et al They found that the overall accuracy of S-Detect was 82% if compared to 76% for the assessment by a surgeon with basic skills. However, the authors simultaneously indicated that CAD was inferior to an expert surgeon, due to six false-positive results [16]. A similar conclusion can be derived from the study by Chung et al [27], where CAD performed better than a less experienced radiologist, while at least as good as an experienced physician.…”

“…The purpose of CAD is to increase the diagnostic confidence, achieve interpretation constancy of US features, and eliminate the inter-observer variability in order to increase diagnostic accuracy, especially when the examination is performed by ultrasonographers outside referenced centers for thyroid cancer diagnostics. The practical potential of CAD has been suggested in a few recent studies [8,9,14,16]. Kwak TIRADS has high sensitivity and low specificity.…”

“…Thus, it is very useful for discarding the benign cases and reducing the number of biopsies [17]. CAD is supposed to be an applicable tool in TN diagnostics and clinical decision-making for medical practitioners with basic US skills [15,16]. It may be useful especially for less experienced operators, increasing the specificity of the examination [18].…”

The value of S-Detect in improving the diagnostic performance of radiologists for the differential diagnosis of thyroid nodules

“…Barczyński et al evaluated 50 thyroid nodules (including 10 papillary thyroid carcinomas and 40 benign nodules) and found that the intra-observer variability of the surgeon with ordinary US skills was significantly higher than surgeons with professional US skills in assessing the lexicon of margin, shape, calcification and spongiform [29]. In our study, the least experienced radiologist also had significantly higher intra-observer variability than the most experienced radiologist in assessing the margin and echogenic foci of the thyroid lexicon items.…”

“…At present, only a few studies have evaluated the diagnostic performance of S-detect for the differential diagnosis of breast masses, and it is believed that S-detect can be used as an additional tool for the diagnosis of breast cancer [19][20][21][22][23]. However, only a few articles reported the diagnostic performance of S-Detect for diagnosing thyroid cancer, and most of them were published by Korean researchers [24][25][26][27][28][29]. In order to further study the diagnostic value of S-detect in thyroid ultrasound, more validation sets from different countries are required.…”