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Background: The management of cytologically indeterminate thyroid nodules is challenging for clinicians. This study aimed to compare the diagnostic performance of the Korean Thyroid Imaging Reporting and Data Systems (K-TIRADS) with that of the American College of Radiology (ACR)-TIRADS for predicting the malignancy risk of indeterminate thyroid nodules.Methods: Thyroid nodules diagnosed by fine-needle aspiration (FNA) followed by surgery or core needle biopsy at a single referral hospital were enrolled.Results: Among 200 thyroid nodules, 78 (39.0%) nodules were classified as indeterminate by FNA (Bethesda category III, IV, and V), and 114 (57.0%) nodules were finally diagnosed as malignancy by surgery or core needle biopsy. The area under the curve (AUC) was higher for FNA than for either TIRADS system in all nodules, while all three methods showed similar AUCs for indeterminate nodules. However, for Bethesda category III nodules, applying K-TIRADS 5 significantly increased the risk of malignancy compared to a cytological examination alone (50.0% vs. 26.5%, P=0.028), whereas applying ACR-TIRADS did not lead to a change.Conclusion: K-TIRADS and ACR-TIRADS showed similar diagnostic performance in assessing indeterminate thyroid nodules, and K-TIRADS had beneficial effects for malignancy prediction in Bethesda category III nodules.

To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules ≥ 1 cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680–0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657–0.768 and 0.652 for AUS, 0.469–0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k = 0.543), substantial (k = 0.652), and moderate (k = 0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.

Ultrasonography (US) is the primary diagnostic tool for thyroid nodules, while the accuracy is operator-dependent. It is widely used not only by radiologists but also by physicians with different levels of experience. The aim of this study was to investigate whether US with computer-aided diagnosis (CAD) has assisting roles to physicians in the diagnosis of thyroid nodules. 451 thyroid nodules evaluated by fine-needle aspiration cytology following surgery were included. 300 (66.5%) of them were diagnosed as malignancy. Physicians with US experience less than 1 year (inexperienced, n = 10), or more than 5 years (experienced, n = 3) reviewed the US images of thyroid nodules with or without CAD assistance. The diagnostic performance of CAD was comparable to that of the experienced group, and better than those of the inexperienced group. The AUC of the CAD for conventional PTC was higher than that for FTC and follicular variant PTC (0.925 vs. 0.499), independent of tumor size. CAD assistance significantly improved diagnostic performance in the inexperienced group, but not in the experienced groups. In conclusion, the CAD system showed good performance in the diagnosis of conventional PTC. CAD assistance improved the diagnostic performance of less experienced physicians in US, especially in diagnosis of conventional PTC.

Ultrasonography (US) is the primary diagnostic tool for thyroid nodules, while the accuracy is operator-dependent. It is widely used not only by radiologists but also by physicians with different levels of experience. The aim of this study was to investigate whether US with computer-aided diagnosis (CAD) has assisting roles to physicians in the diagnosis of thyroid nodules. 451 thyroid nodules evaluated by fine-needle aspiration cytology following surgery were included. 300 (66.5%) of them were diagnosed as malignancy. Physicians with US experience less than 1 year (inexperienced, n = 10), or more than 5 years (experienced, n = 3) reviewed the US images of thyroid nodules with or without CAD assistance. The diagnostic performance of CAD was comparable to that of the experienced group, and better than those of the inexperienced group. The AUC of the CAD for conventional PTC was higher than that for FTC and follicular variant PTC (0.925 vs. 0.499), independent of tumor size. CAD assistance significantly improved diagnostic performance in the inexperienced group, but not in the experienced groups. In conclusion, the CAD system showed good performance in the diagnosis of conventional PTC. CAD assistance improved the diagnostic performance of less experienced physicians in US, especially in diagnosis of conventional PTC.

We assessed the association of insulin resistance as indicated by the homeostatic model assessment of insulin resistance (HOMA-IR) with inflammatory molecules, lipopolysaccharide-binding protein (LBP), high sensitivity C-reactive protein (hs-CRP), Tumor necrosis factor-α (TNF-α), and Interleukin-6 (IL-6) in urban young adolescents.Seventy-six adolescents (36 subjects with HOMA-IR ≥ 2.6 and 40 subjects with HOMA-IR 2.6) were included in the study. We assessed anthropometric and laboratory measures, such as BMI, blood pressure, insulin sensitivity, liver enzymes, and lipid profiles along with the aforementioned inflammatory biomarkers. The diagnostic accuracy of LBP, hs-CRP, TNF-α, and IL-6 for insulin resistance was evaluated by using the receiver operating characteristic (ROC) curve analysis.The mean age of the study subjects was 12.0 [12.0-13.0] y. Circulating LBP plasma concentration and hs-CRP were significantly increased in subjects with HOMA-IR ≥ 2.6 when compared with those with HOMA-IR 2.6 (P .0001). There was no difference in TNF-α or IL-6 concentrations between groups. Comparisons based on the area under the ROC curve for LBP, hs-CRP, TNF-α, and IL-6 with regard to insulin resistance (HOMA-IR ≥ 2.6) were 0.8384 (95% CI: 0.7380 to 0.9388), 0.7907 (95% CI: 0.6701 to 0.9113), 0.6207 (95% CI: 0.4770 to 0.7643), and 0.5763 (95% CI: 0.4285 to 0.7241), respectively.Among LBP, hs-CRP, TNF-α, and IL-6, plasma LBP has the greatest diagnostic accuracy for insulin resistance in young adolescents. Prospective studies are warranted to delineate the value of LBP in the prediction of insulin resistance.

Objectives: To diagnose thyroid cancer, ultrasonography is a primary tool, but diagnostic accuracy varies according to the proficiency of clinicians. The aim of this study was to compare diagnostic performance between deep convolutional neural network (CNN) models and endocrinologist with various experiences. Methods: Patients who underwent fine needle aspiration at endocrinology department in Seoul National University Hospital, between April 2014 and June 2019, were reviewed. Among them, thyroid nodules which were pathologically confirmed by surgery and maximal diameter greater than 1cm were included. Ultrasonography images of thyroid nodules were reviewed by 13 endocrinologists with various experiences: 0 month (E0, n=8), 1 year (E1, n=2), and >5 years (E5, n=3). Results: Of total 451 thyroid nodules, 66.5% was diagnosed as cancer and 83.7% was papillary thyroid cancer (PTC). Sensitivity and specificity of CNN were 85.3% and 63.6%, respectively, and its area under the curve (AUC) was 0.855. Compared to CNN, mean accuracy of E0 group was significantly lower (Accuracy 68.7% vs 78.0%, P