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

Wei, Qi; Zeng, Shu-E; Wang, Liping; Yan, Yichao; Wang, Ting; Xu, Jianwei; Zhang, Meng-Yi; Lv, Wenzhi; Cui, Xin‐Wu; Dietrich, Christoph F.

doi:10.11152/mu-2501

Cited by 21 publications

(14 citation statements)

References 46 publications

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“…Wei Q et al found that S-detect can improve diagnostic performance for less experienced radiologists, and the sensitivity and specificity of S-detect are 91.3% and 65.2%, respectively ( Wei et al, 2020 ). Kim HL et al evaluated the diagnostic performance of S-Detect 1 and S-Detect 2 for detecting thyroid cancers and found that the sensitivity and specificity of S-Detect 1 are 80.2% and 82.6% and that of S-Detect 2 are 81.4% and 68.2%, respectively ( Kim et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…S-thyroid, similar to S-detect, is a computer-aided diagnostic software for ultrasound identification and differentiation of benign and malignant thyroid nodules. Some studies have investigated the diagnostic value of S-detect (Samsung Medison Co., Seoul, South Korea) for benign and malignant thyroid nodules ( Choi et al, 2017 ; Kim et al, 2019 ; Xia et al, 2019 ; Barczyński et al, 2020 ; Wei et al, 2020 ), but no studies have been done on the diagnostic accuracy of S-thyroid for thyroid nodules. What is more, whether it is S-detect or S-thyroid, their diagnoses are based on a single ultrasound image.…”

Section: Introductionmentioning

confidence: 99%

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S-Thyroid Computer-Aided Diagnosis Ultrasound System of Thyroid Nodules: Correlation Between Transverse and Longitudinal Planes

Yang

Chen

et al. 2022

Front. Physiol.

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Introduction: We compare the differences in the diagnostic results of S-thyroid, a computer-aided diagnosis (CAD) software, based on two mutually perpendicular planes.Methods: Initially, 149 thyroid nodules confirmed by surgical pathology were enrolled in our study. CAD in our study was based on the ACR TI-RADS lexicon. t test, rank-sum test, and Chi-square test were used. The interclass correlation coefficient and Cohen’s kappa were used to explore the correlation between CAD features. Receiver operating characteristic was plotted for different combinations of CAD features.Results: The patient’s age, transverse diameter, longitudinal diameter, shape, margin, echogenicity, echogenic foci, composition, TI-RADS classification, and risk probability of nodules in the transverse and longitudinal planes were related to thyroid cancer (p < 0.05). The AUC (95%CI) of TI-RADS classification in the transverse plane of CAD is better than that of the longitudinal plane [0.90 (0.84–0.95) vs. 0.83 (0.77–0.90), p = 0.04]. The AUC (95%CI) of risk probability of nodules in the transverse planes shows no difference from that in the longitudinal plane statistically [0.90 (0.85–0.95) vs. 0.88 (0.82–0.94), p = 0.52]. The AUC (95% CI), specificity, sensitivity, and accuracy [TI-RADS classification (transverse plane) + TI-RADS classification (longitudinal plane) + risk (transverse plane) + risk (longitudinal plane)] are 0.93 (0.89–0.97), 86.15%, 90.48%, and 88.59%, respectively.Conclusion: The diagnosis of thyroid cancer in the CAD transverse plane was superior to that in the CAD longitudinal plane when using the TI-RADS classification, but there was no difference in the diagnosis between the two planes when using risk. However, the combination of CAD transverse and longitudinal planes had the best diagnostic ability.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

S-Thyroid Computer-Aided Diagnosis Ultrasound System of Thyroid Nodules: Correlation Between Transverse and Longitudinal Planes

Yang

Chen

et al. 2022

Front. Physiol.

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“…Besides the neural network that was used in our study to classify thyroid nodules, several other approaches using computer-aided diagnosis systems (CAD) have been evaluated. Wei et al [ 25 ] compared the diagnostic value of S-Detect, a CAD system used to differentiate benign and malignant thyroid nodules by radiologists with different levels of experience. They reported that S-Detect had an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 77.0, 91.3, 65.2, 68.3, and 90.1%, respectively.…”

Section: Discussionmentioning

confidence: 99%

Advanced thyroid carcinomas: neural network analysis of ultrasonographic characteristics

et al. 2021

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Background Ultrasound is the first-line imaging modality for detection and classification of thyroid nodules. Certain characteristics observable by ultrasound have recently been identified that may indicate malignancy. This retrospective cohort study was conducted to test the hypothesis that advanced thyroid carcinomas show distinctive clinical and sonographic characteristics. Using a neural network model as proof of concept, nine clinical/sonographic features served as input. Methods All 96 study enrollees had histologically confirmed thyroid carcinomas, categorized (n = 32, each) as follows: group 1, advanced carcinoma (ADV) marked by local invasion or distant metastasis; group 2, non-advanced papillary carcinoma (PTC); or group 3, non-advanced follicular carcinoma (FTC). Preoperative ultrasound profiles were obtained via standardized protocols. The neural network had nine input neurons and one hidden layer. Results Mean age and the number of male patients in group 1 were significantly higher compared with groups 2 (p = 0.005) or 3 (p < 0.001). On ultrasound, tumors of larger volume and irregular shape were observed significantly more often in group 1 compared with groups 2 (p < 0.001) or 3 (p ≤ 0.01). Network accuracy in discriminating advanced vs. non-advanced tumors was 84.4% (95% confidence interval [CI]: 75.5–91), with positive and negative predictive values of 87.1% (95% CI: 70.2–96.4) and 92.3% (95% CI: 83.0–97.5), respectively. Conclusions Our study has shown some evidence that advanced thyroid tumors demonstrate distinctive clinical and sonographic characteristics. Further prospective investigations with larger numbers of patients and multicenter design should be carried out to show whether a neural network incorporating these features may be an asset, helping to classify malignancies of the thyroid gland.

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“…On this basis, we further reviewed the integrity of the content and data of the paper, and then excluded 6 papers. In the current meta-analysis, a total of 17 studies were used [7][8][9][10][13][14][15][16][17][18][19][20][21][22][23][24][25]. In Fig 1 of this paper, the selection method and main steps of the article are described in detail.…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Diagnostic accuracy of S-Detect in distinguishing benign and malignant thyroid nodules: A meta-analysis

Zhong

Wang

2022

PLoS ONE

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Objectives In this meta-analysis study, the main objective was to determine the accuracy of S-detect in effectively distinguishing malignant thyroid nodules from benign thyroid nodules. Methods We searched the PubMed, Cochrane Library, and CBM databases from inception to August 1, 2021. Meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 softwares. We calculated summary statistics for sensitivity (Sen), specificity (Spe), positive and negative likelihood ratio (LR+/LR−), diagnostic odds ratio(DOR), and receiver operating characteristic (SROC) curves. Cochran’s Q-statistic and I2 test were used to evaluate potential heterogeneity between studies. A sensitivity analysis was performed to evaluate the influence of single studies on the overall estimate. We also performed meta-regression analyses to investigate the potential sources of heterogeneity. Results In this study, a total of 17 studies meeting the requirements of the standard were used. The number of benign and malignant nodules analyzed and evaluated in this paper was 1595 and 1118 respectively. This paper mainly completes the required histological confirmation through s-detect. The pooled Sen and pooled Spe were 0.87 and 0.74, respectively, (95%CI = 0.84–0.89) and (95%CI = 0.66–0.81). Furthermore, the pooled LR+ and negative LR− were determined to be 3.37 (95%CI = 2.53–4.50) and 0.18 (95%CI = 0.15–0.21), respectively. The experimental results showed that the pooled DOR of thyroid nodules was 18.83 (95% CI = 13.21–26.84). In addition, area under SROC curve was determined to be 0.89 (SE = 0.0124). It should be pointed out that there is no evidence of bias (i.e. t = 0.25, P = 0.80). Conclusions Through this meta-analysis, it can be seen that the accuracy of s-detect is relatively high for the effective distinction between malignant thyroid nodules and benign thyroid nodules.

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

Cited by 21 publications

References 46 publications

S-Thyroid Computer-Aided Diagnosis Ultrasound System of Thyroid Nodules: Correlation Between Transverse and Longitudinal Planes

S-Thyroid Computer-Aided Diagnosis Ultrasound System of Thyroid Nodules: Correlation Between Transverse and Longitudinal Planes

Advanced thyroid carcinomas: neural network analysis of ultrasonographic characteristics

Diagnostic accuracy of S-Detect in distinguishing benign and malignant thyroid nodules: A meta-analysis

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