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

Comparison of artificial intelligence, elastic imaging, and the thyroid imaging reporting and data system in the differential diagnosis of suspicious nodules

Background

Ultrasound is widely used for detecting thyroid nodules in clinical practice. This retrospective study aimed to assess the diagnostic efficacy of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS), S-Detect, and elastography of the carotid artery for suspicious thyroid nodules and to determine the complementary value of artificial intelligence and elastography.

Methods

Between January 2021 and November 2021, 101 consecutive patients with 138 thyroid nodules were enrolled in The First Hospital of China Medical University. All nodules were evaluated using ACR-TIRADS categories (TR), S-Detect, and elastography, and then the diagnostic performance of the different methods and the combined assessment were compared. The inclusion criteria were the following: (I) TR3, TR4, and TR5 nodules, which were defined as "suspicious nodules"; (II) patients who had surgical or cytopathological results after ultrasound examination; and (III) voluntary enrollment in this study. Meanwhile, the exclusion criteria were the following: (I) TR1 and TR2 nodules, (II) patients who had undergone fine-needle aspiration before ultrasound examination, and (III) inconclusive cytologic findings.

Results

A total of 71 patients (12 men and 59 women) with 94 suspicious thyroid nodules (42 benign nodules and 52 malignant nodules) were finally included in this study. S-Detect had a significantly better sensitivity than did ACR-TIRADS [S-Detect: 98.1%, 95% confidence interval (CI): 89.7-100.0%; ACR-TIRADS: 84.6%, 95% CI: 71.9-93.1%; P=0.036], but its specificity was much lower (S-Detect: 19.0%; 95% CI: 8.6-34.1%; ACR-TIRADS: 40.5%, 95% CI: 25.6-56.7%; P=0.032). The accuracy was not significantly different between S-Detect (62.8%; 95% CI: 52.2-72.5%) and ACR-TIRADS (64.9%; 95% CI: 54.4-74.5%) (P=0.761). The elasticity contrast index (ECI) was not definitively useful in identifying suspicious thyroid nodules (P=0.592). Compared with the use of ACR-TIRADS and S-Detect alone, the specificity (45.2%; 95% CI: 29.8-61.3%), positive predictive value (65.2%; 95% CI: 52.4-76.5%), accuracy (66.0%; 95% CI: 55.5-75.4%), and the area under the receiver operating characteristic curve (0.640; 95% CI: 0.534-0.736) of their combination were higher but not significantly so.

Conclusions

At present, S-Detect cannot replace manual diagnosis, and the value of elastography of the carotid artery in diagnosing suspected thyroid nodules remains unclear.

Diagnostic efficiency among Eu-/C-/ACR-TIRADS and S-Detect for thyroid nodules: a systematic review and network meta-analysis

Background

The performance in evaluating thyroid nodules on ultrasound varies across different risk stratification systems, leading to inconsistency and uncertainty regarding diagnostic sensitivity, specificity, and accuracy.

Objective

Comparing diagnostic performance of detecting thyroid cancer among distinct ultrasound risk stratification systems proposed in the last five years.

Evidence acquisition

Systematic search was conducted on PubMed, EMBASE, and Web of Science databases to find relevant research up to December 8, 2022, whose study contents contained elucidation of diagnostic performance of any one of the above ultrasound risk stratification systems (European Thyroid Imaging Reporting and Data System[Eu-TIRADS]; American College of Radiology TIRADS [ACR TIRADS]; Chinese version of TIRADS [C-TIRADS]; Computer-aided diagnosis system based on deep learning [S-Detect]). Based on golden diagnostic standard in histopathology and cytology, single meta-analysis was performed to obtain the optimal cut-off value for each system, and then network meta-analysis was conducted on the best risk stratification category in each system.

Evidence synthesis

This network meta-analysis included 88 studies with a total of 59,304 nodules. The most accurate risk category thresholds were TR5 for Eu-TIRADS, TR5 for ACR TIRADS, TR4b and above for C-TIRADS, and possible malignancy for S-Detect. At the best thresholds, sensitivity of these systems ranged from 68% to 82% and specificity ranged from 71% to 81%. It identified the highest sensitivity for C-TIRADS TR4b and the highest specificity for ACR TIRADS TR5. However, sensitivity for ACR TIRADS TR5 was the lowest. The diagnostic odds ratio (DOR) and area under curve (AUC) were ranked first in C-TIRADS.

Conclusion

Among four ultrasound risk stratification options, this systemic review preliminarily proved that C-TIRADS possessed favorable diagnostic performance for thyroid nodules.

Systematic review registration

https://www.crd.york.ac.uk/prospero, CRD42022382818.

Ultrasonography-based radiomics and computer-aided diagnosis in thyroid nodule management: performance comparison and clinical strategy optimization

Objectives

To compare ultrasonography (US) feature-based radiomics and computer-aided diagnosis (CAD) models for predicting malignancy in thyroid nodules, and to evaluate their utility for thyroid nodule management.

Methods

This prospective study included 262 thyroid nodules obtained between January 2022 and June 2022. All nodules previously underwent standardized US image acquisition, and the nature of the nodules was confirmed by the pathological results. The CAD model exploited two vertical US images of the thyroid nodule to differentiate the lesions. The least absolute shrinkage and operator algorithm (LASSO) was applied to choose radiomics features with excellent predictive properties for building a radiomics model. Ultimately, the area under the receiver operating characteristic curve (AUC) and calibration curves were assessed to compare diagnostic performance between the models. DeLong's test was used to analyze the difference between groups. Both models were used to revise the American College of Radiology Thyroid Imaging Reporting and Data Systems (ACR TI-RADS) to provide biopsy recommendations, and their performance was compared with the original recommendations.

Results

Of the 262 thyroid nodules, 157 were malignant, and the remaining 105 were benign. The diagnostic performance of radiomics, CAD, and ACR TI-RADS models had an AUC of 0.915 (95% confidence interval (CI): 0.881-0.947), 0.814 (95% CI: 0.766-0.863), and 0.849 (95% CI: 0.804-0.894), respectively. DeLong's test showed a statistically significant between the AUC values of models (p < 0.05). Calibration curves showed good agreement in each model. When both models were applied to revise the ACR TI-RADS, our recommendations significantly improved the performance. The revised recommendations based on radiomics and CAD showed an increased sensitivity, accuracy, positive predictive value, and negative predictive value, and decreased unnecessary fine-needle aspiration rates. Furthermore, the radiomics model's improvement scale was more pronounced (33.3-16.7% vs. 33.3-9.7%).

Conclusion

The radiomics strategy and CAD system showed good diagnostic performance for discriminating thyroid nodules and could be used to optimize the ACR TI-RADS recommendation, which successfully reduces unnecessary biopsies, especially in the radiomics model.

Diagnostic Performance of Various Ultrasound Risk Stratification Systems for Benign and Malignant Thyroid Nodules: A Meta-Analysis

BACKGROUND

To evaluate the diagnostic performance of ultrasound risk-stratification systems for the discrimination of benign and malignant thyroid nodules and to determine the optimal cutoff values of individual risk-stratification systems.

METHODS

PubMed, Embase, SCOPUS, Web of Science, and Cochrane library databases were searched up to August 2022. Sensitivity and specificity data were collected along with the characteristics of each study related to ultrasound risk stratification systems.

RESULTS

Sixty-seven studies involving 76,512 thyroid nodules were included in this research. The sensitivity, specificity, diagnostic odds ratios, and area under the curves by K-TIRADS (4), ACR-TIRADS (TR5), ATA (high suspicion), EU-TIRADS (5), and Kwak-TIRADS (4b) for malignancy risk stratification of thyroid nodules were 92.5%, 63.5%, 69.8%, 70.6%, and 95.8%, respectively; 62.8%, 89.6%, 87.2%, 83.9%, and 63.8%, respectively; 20.7111, 16.8442, 15.7398, 12.2986, and 38.0578, respectively; and 0.792, 0.882, 0.859, 0.843, and 0.929, respectively.

CONCLUSION

All ultrasound-based risk-stratification systems had good diagnostic performance. Although this study determined the best cutoff values in individual risk-stratification systems based on statistical assessment, clinicians could adjust or alter cutoff values based on the clinical purpose of the ultrasound and the reciprocal changes in sensitivity and specificity.

Comparison of S-Detect and thyroid imaging reporting and data system classifications in the diagnosis of cytologically indeterminate thyroid nodules

PURPOSE

The aim of this study was to investigate the value of S-Detect for predicting the malignant risk of cytologically indeterminate thyroid nodules (CITNs).

METHODS

The preoperative prediction of 159 CITNs (Bethesda III, IV and V) were performed using S-Detect, Thyroid Imaging Reporting and Data System of American College of Radiology (ACR TI-RADS) and Chinese TI-RADS (C-TIRADS). First, Linear-by-Linear Association test and chi-square test were used to analyze the malignant risk of CITNs. McNemar's test and receiver operating characteristic curve were used to compare the diagnostic efficacy of S-Detect and the two TI-RADS classifications for CITNs. In addition, the McNemar's test was used to compare the diagnostic accuracy of the above three methods for different pathological types of nodules.

RESULTS

The maximum diameter of the benign nodules was significantly larger than that of malignant nodules [0.88(0.57-1.42) vs 0.57(0.46-0.81), P=0.002]. The risk of malignant CITNs in Bethesda system and the two TI-RADS classifications increased with grade (all P for trend<0.001). In all the enrolled CITNs, the diagnostic results of S-Detect were significantly different from those of ACR TI-RADS and C-TIRADS, respectively (P=0.021 and P=0.007). The sensitivity and accuracy of S-Detect [95.9%(90.1%-98.5%) and 88.1%(81.7%-92.5%)] were higher than those of ACR TI-RADS [87.6%(80.1%-92.7%) and 81.8%(74.7%-87.3%)] (P=0.006 and P=0.021) and C-TIRADS [84.3%(76.3%-90.0%) and 78.6%(71.3%-84.5%)] (P=0.001 and P=0.001). Moreover, the negative predictive value and the area under curve value of S-Detect [82.8% (63.5%-93.5%) and 0.795%(0.724%-0.855%)] was higher than that of C-TIRADS [54.8%(38.8%-69.8%) and 0.724%(0.648%-0.792%] (P=0.024 and P=0.035). However, the specificity and positive predictive value of S-Detect were similar to those of ACR TI-RADS (P=1.000 and P=0.154) and C-TIRADS (P=1.000 and P=0.072). There was no significant difference in all the evaluated indicators between ACR TI-RADS and C-TIRADS (all P>0.05). The diagnostic accuracy of S-Detect (97.4%) for papillary thyroid carcinoma (PTC) was higher than that of ACR TI-RADS (90.4%) and C-TIRADS (87.8%) (P=0.021 and P=0.003).

CONCLUSION

The diagnostic performance of S-Detect in differentiating CITNs was similar to ACR TI-RADS and superior to C-TIRADS, especially for PTC.

The Diagnostic Value of Artificial Intelligence Ultrasound S-Detect Technology for Thyroid Nodules

This study aimed to evaluate the consistency of ultrasound TI-RADS classification used by sonographers with different ultrasound diagnosis experience in the diagnosis of thyroid nodules and the diagnostic value of using artificial intelligence ultrasound S-Detect technology in the differentiation of benign and malignant thyroid lesions. 100 patients who underwent ultrasound examination of thyroid masses in our hospital from June 2019 to June 2021 and were further punctured or operated on were included in the study. Pathological results were used as the gold standard to evaluate ultrasound S-Detect technology and the value of TI-RADS classification and the combined application of the two in diagnosing benign and malignant thyroid TI-RADS 4 types of nodules, and the consistency of judgments of doctors of different ages is assessed by a Kappa value. There were 128 nodules in 100 patients, 51 benign nodules, and 77 malignant nodules. For senior physicians, the sensitivity of diagnosis using TI-RADS classification combined with ultrasound S-Detect technology is 93.5%, specificity is 94.1%, and accuracy is 93.8%; for middle-aged physicians using TI-RADS classification combined with ultrasound S-Detect technology for diagnosis, the sensitivity is 89.6%, specificity is 92.2%, and accuracy is 90.6%; for junior doctors, the sensitivity of diagnosis using TI-RADS classification combined with ultrasound S-Detect technology is 83.1%, specificity is 88.2%, and accuracy is 85.1%. Regardless of seniority, the combined application of artificial intelligence ultrasound S-Detect technology and TI-RADS classification can improve the diagnostic ability of sonographers for thyroid nodules and at the same time improve the consistency of judgment among physicians, and this is especially important for radiologists.

Ultrasonic S-Detect mode for the evaluation of thyroid nodules: A meta-analysis

OBJECTIVES

This meta-analysis aimed to evaluate the value of ultrasonic S-Detect mode for the evaluation of thyroid nodules.

METHODS

We searched PubMed, Cochrane Library, and Chinese biomedical databases from inception to August 31, 2021. Meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 software. We calculated the summary statistics for sensitivity (Sen), specificity (Spe), summary receiver operating characteristic curve, and the area under the curve, and compared the area under the curve between ultrasonic S-Detect mode and thyroid imaging report and data system (TI-RADS) for the diagnosis of thyroid nodules. As a systematic review summarizing the results of previous studies, this study does not need the informed consent of patients or the approval of the ethics review committee.

RESULTS

Fifteen studies that met all inclusion criteria were included in this meta-analysis. A total of 924 thyroid malignant nodules and 1228 thyroid benign nodules were assessed. All thyroid nodules were histologically confirmed after examination. The pooled Sen and Spe of TI-RADS were 0.89 (95% confidence interval [CI] = 0.85-0.91) and 0.85 (95% CI = 0.78-0.90), respectively; the pooled Sen and Spe of S-Detect were 0.88 (95% CI = 0.85-0.90) and 0.73 (95% CI = 0.63-0.81), respectively. The areas under the summary receiver operating characteristic curve of TI-RADS and S-Detect were 0.9370 (standard error [SE] = 0.0110) and 0.9128 (SE = 0.0147), respectively, between which there was no significant difference (Z = 1.318; SE = 0.0184; P = .1875). We found no evidence of publication bias (t = 0.36, P = .72).

CONCLUSIONS

Our meta-analysis indicates that ultrasonic S-Detect mode may have high diagnostic accuracy and may have certain clinical application value, especially for young doctors.

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

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.

The European Institute of Oncology Thyroid Imaging Reporting and Data System for Classification of Thyroid Nodules: A Prospective Study

Background: To evaluate the performance, quality and effectiveness of “IEO-TIRADS” in assigning a TI-RADS score to thyroid nodules (TN) when compared with “EU-TIRADS” and the US risk score calculated with the S-Detect software (“S-Detect”). The primary objective is the evaluation of diagnostic accuracy (DA) by “IEO-TIRADS”, “S-Detect” and “EU-TIRADS”, and the secondary objective is to evaluate the diagnostic performances of the scores, using the histological report as the gold standard. Methods: A radiologist collected all three scores of the TNs detected and determined the risk of malignancy. The results of all the scores were compared with the histological specimens. The sensitivity (SE), specificity (SP), and diagnostic accuracy (DA), with their 95% confidence interval (95% CI), were calculated for each method. Results: 140 TNs were observed in 93 patients and classified according to all three scores. “IEO-TIRADS” has an SE of 73.6%, an SP of 59.2% and a DA of 68.6%. “EU-TIRADS” has an SE of 90.1%, an SP of 32.7% and a DA of 70.0%. “S-Detect” has an SE of 67.0%, an SP of 69.4% and a DA of 67.9%. Conclusion: “IEO-TIRADS” has a similar diagnostic performance to “S-Detect” and “EU-TIRADS”. Providing a comparable DA with other reporting systems, IEO-TIRADS holds the potential of being applied in clinical practice.

Artificial Intelligence (AI) Tools for Thyroid Nodules on Ultrasound, From the AJR Special Series on AI Applications

Artificial intelligence (AI) methods for evaluating thyroid nodules on ultrasound have been widely described in the literature, with reported performance of AI tools matching or in some instances surpassing radiologists. As these data have accumulated, products for classification and risk stratification of thyroid nodules on ultrasound have become commercially available. This article reviews FDA-approved products currently on the market, with a focus on product features, reported performance, and considerations for implementation. The products perform risk stratification primarily using the Thyroid Imaging Reporting and Data System (TI-RADS), though may provide additional prediction tools independent of TI-RADS. Key issues in implementation include integration with radiologist interpretation, impact on workflow and efficiency, and performance monitoring. AI applications beyond nodule classification, including report construction and incidental findings follow-up, are also described. Anticipated future directions of research and development in AI tools for thyroid nodules are highlighted.

Editorial on the Special Issue "Novel Methods of Diagnostics of Thyroid and Parathyroid Lesions"

Thyroid nodular disease is one of the most frequent endocrine diseases [...].

A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules

Purpose

The fully automatic AI-Sonic computer-aided design (CAD) system was employed for the detection and diagnosis of benign and malignant thyroid nodules. The aim of this study was to investigate the efficiency of the AI-Sonic CAD system with the use of a deep learning algorithm to improve the diagnostic accuracy of ultrasound-guided fine-needle aspiration (FNA).

Methods

A total of 138 thyroid nodules were collected from 124 patients and diagnosed by an expert, a novice, and the Thyroid Imaging Reporting and Data System (TI-RADS). Diagnostic efficiency and feasibility were compared among the expert, novice, and CAD system. The application of the CAD system to enhance the diagnostic efficiency of novices was assessed. Moreover, with the experience of the expert as the gold standard, the values of features detected by the CAD system were also analyzed. The efficiency of FNA was compared among the expert, novice, and CAD system to determine whether the CAD system is helpful for the management of FNA.

Result

In total, 56 malignant and 82 benign thyroid nodules were collected from the 124 patients (mean age, 46.4 ± 12.1 years; range, 12–70 years). The diagnostic area under the curve of the CAD system, expert, and novice were 0.919, 0.891, and 0.877, respectively (p < 0.05). In regard to feature detection, there was no significant differences in the margin and composition between the benign and malignant nodules (p > 0.05), while echogenicity and the existence of echogenic foci were of great significance (p < 0.05). For the recommendation of FNA, the results showed that the CAD system had better performance than the expert and novice (p < 0.05).

Conclusions

Precise diagnosis and recommendation of FNA are continuing hot topics for thyroid nodules. The CAD system based on deep learning had better accuracy and feasibility for the diagnosis of thyroid nodules, and was useful to avoid unnecessary FNA. The CAD system is potentially an effective auxiliary approach for diagnosis and asymptomatic screening, especially in developing areas.

An integrated AI model to improve diagnostic accuracy of ultrasound and output known risk features in suspicious thyroid nodules

OBJECTIVES

From the viewpoint of ultrasound (US) physicians, an ideal thyroid US computer-assisted diagnostic (CAD) system for thyroid cancer should perform well in suspicious thyroid nodules with atypical risk features and be able to output explainable results. This study aims to develop an explainable US CAD model for suspicious thyroid nodules.

METHODS

A total of 2992 solid or almost-solid thyroid nodules were analyzed retrospectively. All nodules had pathological results (1070 malignancies and 1992 benignities) confirmed by ultrasound-guided fine-needle aspiration cytology and histopathology after thyroidectomy. A deep learning model (ResNet50) and a multiple risk features learning ensemble model (XGBoost) were used to train the US images of 2794 thyroid nodules. Then, an integrated AI model was generated by combining both models. The diagnostic accuracies of the three AI models (ResNet50, XGBoost, and the integrated model) were predicted in a testing set including 198 thyroid nodules and compared to the diagnostic efficacy of five ultrasonographers.

RESULTS

The accuracy of the integrated model was 76.77%, while the mean accuracy of the ultrasonographers was 68.38%. Of the risk features, microcalcifications showed the highest contribution to the diagnosis of malignant nodules.

CONCLUSIONS

The integrated AI model in our study can improve the diagnostic accuracy of suspicious thyroid nodules and output the known risk features simultaneously, thus aiding in training young ultrasonographers by linking the explainable results to their clinical experience and advancing the acceptance of AI diagnosis for thyroid cancer in clinical practice.

KEY POINTS

• We developed an artificial intelligence (AI) diagnosis model based on both deep learning and multiple risk feature ensemble learning methods. • The AI diagnosis model showed higher diagnostic accuracy for suspicious thyroid nodules than ultrasonographers. • The AI diagnosis model showed partial explainability by outputting the known risk features, thus aiding young ultrasonic doctors in increasing the diagnostic level for thyroid cancer.

Assessment of perinodular stiffness in differentiating malignant from benign thyroid nodules

OBJECTIVE

We evaluated the diagnostic accuracy of perinodular stiffness, four risk stratification systems (RSSs) (KWAK-TIRADS, ACR-TIRADS, EU-TIRADS, and C-TIRADS), and the combination of perinodular stiffness and the four RSSs in differentiating malignant from benign thyroid nodules (TNs).

METHODS

A total of 788 TNs in 726 patients were examined with conventional ultrasound (US) examination and sound touch elastography (STE). All TNs were classified by each of the four RSSs. The stiffness inside (E) the TNs was measured by STE. The stiffness of the 2.0-mm perinodular region (Eshell) was measured with the Shell measurement function of STE. The diagnostic performances of four RSSs, the E values, and the Eshell values were evaluated. All TNs were further divided into subgroups based on size (≤ 10 mm group and > 10 mm group).

RESULTS

Ninety-six TNs were classified as benign and 692 as malignant. Among the single-method approaches, ACR-TIRADS showed the highest AUC (0.77) for differentiating malignant from benign TNs for all TNs included. Eshell showed the highest AUC (0.75) in differentiating malignant from benign TNs for TNs with sizes ≤ 10 mm, and there were no significant differences in AUC among all single methods for diagnosis of TNs with sizes > 10 mm (P > 0.05). The combination of C-TIRADS and Eshell/E yielded the highest AUC for all TNs (0.83) and for TNs with size ≤ 10 mm (0.85) compared with other combinations.

CONCLUSIONS

Eshell/E combined with conventional US improves the diagnostic accuracy in TNs and may reduce unnecessary fine-needle aspiration.

The value of the Demetics ultrasound-assisted diagnosis system in the differential diagnosis of benign from malignant thyroid nodules and analysis of the influencing factors

OBJECTIVES

To evaluate the value of Demetics and to explore whether Demetics can help radiologists with varying years of experience in the differential diagnosis of benign from malignant thyroid nodules.

METHODS

The clinical application value of Demetics was assessed by comparing the diagnostic accuracy of radiologists before and after applying Demetics. This retrospective analysis included 284 thyroid nodules that underwent pathological examinations. Two different combined methods were applied. Using method 1: the original TI-RADS classification was forcibly upgraded or downgraded by one level when Demetics classified the thyroid nodules as malignant or benign. Using method 2: the TI-RADS and benign or malignant classification of the thyroid nodules were flexibly adjusted after the physician learned the Demetics' results.

RESULTS

Demetics exhibited a higher sensitivity than did junior radiologist 1 (p_D1 = 0.029) and was similar in sensitivity to the two senior radiologists. Demetics had a higher AUC than both junior radiologists (p_D1 = 0.042, p_D2 = 0.038) and an AUC similar to that of the senior radiologists. The sensitivity (p = 0.035) and AUC (p = 0.031) of junior radiologist 1 and the specificity (p < 0.001) and AUC (p = 0.026) of junior radiologist 2 improved with combined method 1. The AUC of junior radiologist 2 improved with combined method 2 (p = 0.045). The factors influencing the diagnostic results of Demetics include sonographic signs (echogenicity and echogenic foci), contrast of the image, and nodule size.

CONCLUSION

Demetics exhibited high sensitivity and accuracy in the differential diagnosis of benign from malignant thyroid nodules. Demetics could improve the diagnostic accuracy of junior radiologists.

KEY POINTS

• Demetics exhibited a high sensitivity and accuracy in the differential diagnosis of benign from malignant thyroid nodules. • Demetics could improve the diagnostic accuracy of junior radiologists in the differential diagnosis of benign from malignant thyroid nodules. • Factors influencing the diagnostic results of Demetics include the sonographic signs (echogenicity and echogenic foci), contrast of the image, and nodule size.