Combining Image Similarity and Predictive Artificial Intelligence Models to Decrease Subjectivity in Thyroid Nodule Diagnosis and Improve Malignancy Prediction.

OBJECTIVES: To evaluate the efficacy of combining predictive artificial intelligence (AI) and image similarity model to risk stratify thyroid nodules, using retrospective external validation study. METHODS: Two datasets were used to determine efficacy of the AI application. One was Stanford dataset ultrasound images of 192 nodules between April 2017 and May 2018 and the second was private practice consisting of 118 thyroid nodule images between January 2018 and December 2023. The nodules had definitive diagnosis by cytology or surgical pathology. The AI application was used to predict the diagnosis and American College of Radiology Thyroid Imaging and Data System (ACR TI-RADS) score. RESULTS: In the Stanford dataset, the AI application predicted malignancies with sensitivity of 1.0 and specificity of 0.55. Positive predictive value (PPV) was 0.18 and negative predictive value (NPV) was 1.0. The Area Under the Curve - Receiver Operating Characteristic was 0.78. ACR TI-RADS based clinical recommendation had a polychoric correlation of 0.67. In the private dataset, the AI application predicted malignancies with sensitivity of 0.91 and specificity of 0.95. PPV was 0.8 and NPV was 0.98. The area under the curve - receiver operating characteristic was 0.93 and accuracy was 0.94. ACR TI-RADS based score had a polychoric correlation of 0.94. CONCLUSION: The AI application showed good performance for sensitivity and NPV between the two datasets and demonstrated potential for 61.5% reduction in the need for fine needle aspiration and strong correlation to ACR TI-RADS. However, PPV was variable between the datasets possibly from variability in image selection and prevalence of malignancy. If implemented widely and consistently among various clinical settings, this could lead to decreased patient burden associated with an invasive procedure and possibly to decreased health care spending.

Accuracy of ultrasound-guided fine-needle aspiration cytology in evaluation of thyroid nodules using different ultrasonographic and cytological features.

BACKGROUND: Fine-needle aspiration cytology (FNAC) is a reliable method for preoperative evaluation of thyroid nodules particularly if ultrasound-guided (USG-FNAC). The main purpose of this study is to evaluate the efficacy of USG-FNAC and its accuracy. METHODS: We retrospectively studied 212 thyroidectomy cases with preoperative ultrasonography and FNAC data during the period 2015-2022 using TI-RADS for final ultrasound diagnosis and Bethesda system for cytological diagnosis. RESULTS: The studied cases were 200 females and 12 males. Thyroid cancer was more prevalent under 20 years old (78.5%). Papillary thyroid carcinoma comprises 84% of all cancer cases. Significant ultrasound features (p-value <0.05) favour malignancy were hypoechogenicity (66%), mixed echogenicity (84%), irregular border (61%), microcalcification (68%) and rim halo (63.6%). Malignancy was found in 21% of TI-RADS-2, 65% of TI-RADS-4 and 100% of TI-RADS-5. There is a significant difference between different categories of Bethesda system. All cases in Cat-VI were malignant (100%). Malignancy was also found in 81% of Cat-V, 20% of Cat-IV, 33% of Cat-III, 16% of Cat-II and 43% of Cat-I. Cytological features consistent with malignancy were as follows: grooving (94%), nuclear irregularities (89%), nuclear pseudoinclusion (89%) and little colloid (82%). In our study, USG-FNAC sensitivity was 83%, specificity 85%, PPV 85%, NPV 83% and accuracy 84%. CONCLUSION: Ultrasound features in favour of malignancy in thyroid nodules are hypoechoic or complex echogenicity, irregular border, punctuate calcification and presence of rim halo. Cytological features in favour of malignancy are grooving, nuclear irregularities, nuclear pseudoinclusion and little or absent colloid.

Comparative evaluation of shear wave elastography elasticity values in thyroid nodules with cytology results and TI-RADS scoring in differentiation of benign-malignant nodules.

PURPOSE: The aim of this prospective study was to investigate the diagnostic performance of shear wave elastography (SWE) in differentiating benign and malignant thyroid nodules and their correlation with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS). METHODS: This prospective study included 370 thyroid nodules in 308 patients aged 18-70 years. All the patients underwent B-mode ultrasound (US), Doppler examination, and SWE and were given an ACR TI-RADS risk score before fine needle aspiration biopsy (FNAB) and/or surgery. The correlation between SWE parameters and ACR TI-RADS categories was investigated statistically and compared with histopathologic results. Additionally, the diagnostic performance of SWE was evaluated to distinguish malignant and benign thyroid nodules. RESULTS: One hundred and thirty-five of the 370 thyroid nodules were malignant, and 235 nodules were benign. The mean shear wave velocity (SWV) value of the malignant nodules (3.70 ± 0.98 m/s) was statistically higher than that of the benign nodules (2.70 ± 0.37 m/s). The best cutoff value of the mean SWV for differentiating benign and malignant nodules was found to be 2.94 m/s (sensitivity 90.4%, specificity 89.9%, positive predictive value 81.3%, negative predictive value 94.1%, p < 0.001). The average score of the nodules according to the ACR TI-RADS was 3.57 ± 1.83 in benign nodules and 7.38 ± 2.69 in malignant nodules (p ≤ 0.001). CONCLUSION: This study showed that combining SWE and TI-RADS improves the specificity of TI-RADS alone in differentiating benign and malignant nodules.

Study on diagnosing thyroid nodules of ACR TI-RADS 4-5 with multimodal ultrasound radiomics technology.

BACKGROUND: Explore the feasibility of using the multimodal ultrasound (US) radiomics technology to diagnose American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) 4-5 thyroid nodules. METHOD: This study prospectively collected the clinical characteristics, conventional, and US elastography images of 100 patients diagnosed with ACR TI-RADS 4-5 nodules from May 2022 to 2023. Independent risk factors for malignant thyroid nodules were extracted and screened using methods such as the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model, and a multimodal US radiomics combined diagnostic model was established. Using a multifactorial LR analysis and a Rad-score rating, the predictive performance was validated and evaluated, and the final threshold range was determined to assess the clinical net benefit of the model. RESULTS: In the training set, the US radiomics combined predictive model area under curve (AUC = 0.928) had higher diagnostic performance compared with clinical characteristics (AUC = 0.779), conventional US (AUC = 0.794), and US elastography model (AUC = 0.852). In the validation set, the multimodal US radiomics combined diagnostic model (AUC = 0.829) also had higher diagnostic performance compared with clinical characteristics (AUC = 0.799), conventional US (AUC = 0.802), and US elastography model (AUC = 0.718). CONCLUSION: Multi-modal US radiomics technology can effectively diagnose thyroid nodules of ACR TI-RADS 4-5, and the combination of radiomics signature and conventional US features can further improve the diagnostic performance.

Implementation strategies to support ultrasound thyroid nodule risk stratification: A systematic review.

BACKGROUND: Ultrasound risk stratification can improve the care of patients with thyroid nodules by providing a structured and systematic approach for the evaluation of thyroid nodule features and thyroid cancer risk. The optimal strategies to support implementation of high quality thyroid nodule risk stratification are unknown. This study seeks to summarise strategies used to support implementation of thyroid nodule ultrasound risk stratification in practice and their effects on implementation and service outcomes. METHODS: This is a systematic review of studies evaluating implementation strategies published between January 2000 and June 2022 that were identified on Ovid MEDLINE, Ovid EMBASE, Ovid Cochrane, Scopus, or Web of Science. Screening of eligible studies, data collection and assessment for risk of bias was completed independently and in duplicate. Implementation strategies and their effects on implementation and service outcomes were evaluated and summarised. RESULTS: We identified 2666 potentially eligible studies of which 8 were included. Most implementation strategies were directed towards radiologists. Common strategies to support the implementation of thyroid nodule risk stratification included: tools to standardise thyroid ultrasound reports, education on thyroid nodule risk stratification and use of templates/forms for reporting, and reminders at the point of care. System based strategies, local consensus or audit were less commonly described. Overall, the use of these strategies supported the implementation process of thyroid nodule risk stratification with variable effects on service outcomes. CONCLUSIONS: Implementation of thyroid nodule risk stratification can be supported by development of standardised reporting templates, education of users on risk stratification and reminders of use at the point of care. Additional studies evaluating the value of implementation strategies in different contexts are urgently needed.

Nomogram to differentiate benign and malignant thyroid nodules in the American College of Radiology Thyroid Imaging Reporting and Data System level 5.

OBJECTIVES: To develop and validate a nomogram for differentiating benign and malignant thyroid nodules of American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) level 5 (TR5) and improving the performance of the guideline. METHODS: From May 2018 to December 2019, 640 patients with TR5 nodules were retrospectively included in the primary cohort. Univariate and multivariable analyses were performed to determine the risk factors for thyroid cancer. A nomogram was established on the basis of multivariable analyses; the performance of the nomogram was evaluated with respect to discrimination, calibration, and clinical usefulness. The nomogram model was also compared to the ACR score model. External validation was performed and the independent validation cohort contained 201 patients from April 2021 to January 2022. RESULTS: Multivariable analyses showed that age, tumour location, multifocality, concomitant Hashimoto's disease, neck lymph node status reported by ultrasound (US) and ACR score were the independent risk factors for thyroid cancer (all p < .05). The nomogram showed good discrimination, with an area under the curve (AUC) of 0.786 (95% confidence interval [CI]: 0.742-0.830) and 0.712 (95% CI: 0.615-0.809) in the primary cohort and external validation cohort, respectively. Decision curve analysis demonstrated the clinical usefulness of the model. Compared to the ACR score model, the nomogram showed higher AUC (0.786 vs. 0.626, p < .001) and specificity (0.783 vs. 0.391). CONCLUSIONS: The presented nomogram model, based on age, tumour features and ACR score, can differentiate benign and malignant thyroid nodules in TR5 and had a high specificity.

Thyroid Nodules on Ultrasound in Children and Young Adults: Comparison of Diagnostic Performance of Radiologists' Impressions, ACR TI-RADS, and a Deep Learning Algorithm.

BACKGROUND. In current clinical practice, thyroid nodules in children are generally evaluated on the basis of radiologists' overall impressions of ultrasound images. OBJECTIVE. The purpose of this article is to compare the diagnostic performance of radiologists' overall impression, the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS), and a deep learning algorithm in differentiating benign and malignant thyroid nodules on ultrasound in children and young adults. METHODS. This retrospective study included 139 patients (median age 17.5 years; 119 female patients, 20 male patients) evaluated from January 1, 2004, to September 18, 2020, who were 21 years old and younger with a thyroid nodule on ultrasound with definitive pathologic results from fine-needle aspiration and/or surgical excision to serve as the reference standard. A single nodule per patient was selected, and one transverse and one longitudinal image each of the nodules were extracted for further evaluation. Three radiologists independently characterized nodules on the basis of their overall impression (benign vs malignant) and ACR TI-RADS. A previously developed deep learning algorithm determined for each nodule a likelihood of malignancy, which was used to derive a risk level. Sensitivities and specificities for malignancy were calculated. Agreement was assessed using Cohen kappa coefficients. RESULTS. For radiologists' overall impression, sensitivity ranged from 32.1% to 75.0% (mean, 58.3%; 95% CI, 49.2-67.3%), and specificity ranged from 63.8% to 93.9% (mean, 79.9%; 95% CI, 73.8-85.7%). For ACR TI-RADS, sensitivity ranged from 82.1% to 87.5% (mean, 85.1%; 95% CI, 77.3-92.1%), and specificity ranged from 47.0% to 54.2% (mean, 50.6%; 95% CI, 41.4-59.8%). The deep learning algorithm had a sensitivity of 87.5% (95% CI, 78.3-95.5%) and specificity of 36.1% (95% CI, 25.6-46.8%). Interobserver agreement among pairwise combinations of readers, expressed as kappa, for overall impression was 0.227-0.472 and for ACR TI-RADS was 0.597-0.643. CONCLUSION. Both ACR TI-RADS and the deep learning algorithm had higher sensitivity albeit lower specificity compared with overall impressions. The deep learning algorithm had similar sensitivity but lower specificity than ACR TI-RADS. Interobserver agreement was higher for ACR TI-RADS than for overall impressions. CLINICAL IMPACT. ACR TI-RADS and the deep learning algorithm may serve as potential alternative strategies for guiding decisions to perform fine-needle aspiration of thyroid nodules in children.

Predicting papillary thyroid carcinoma cervical lymph node metastases: an algorithm using the American College of Radiology Thyroid Imaging, Reporting and Data System.

BACKGROUND: It is important to predict lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) preoperatively; however, the relationship between the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) score and cervical LNM remains unclear. PURPOSE: To evaluate the association between the ACR TI-RADS score and cervical LNM in patients with PTC. MATERIAL AND METHODS: This retrospective study consisted of 474 patients with 548 PTCs. Cervical LNM including central LNM (CLNM) and lateral LNM (LLNM) were confirmed by pathology. Univariate and multivariate analyses were performed to investigate the risk factors of CLNM and LLNM. RESULTS: Multivariate logistic regression analyses indicated that younger age and multifocality were risk factors for CLNM in PTCs with TR5. In addition, younger age, larger tumor size, and Hashimoto's thyroiditis (HT) were risk factors for LLNM in PTCs ≥ 10 mm with TR5. In PTCs with TR4, ACR TI-RADS scores 5-6 conferred risks for LNM. In PTCs ≥ 10 mm with TR5, ACR TI-RADS scores ≥9 were risk factors for LLNM. CONCLUSION: A higher ACR TI-RADS score is a predictor for cervical LNM in PTCs with TR4 and PTCs ≥ 10 mm with TR5.

Thyroid Nodule Margin Assessment Using ACR TI-RADS: Adding Points for Macrolobulation Impairs Performance.

OBJECTIVE: We evaluated the performance of ACR TI-RADS when points for lobulated margins are applied only when the margins meet a quantified measure of margin microlobulation and not applied when nodules only demonstrate macrolobulation. METHODS: We retrospectively reviewed ultrasound and pathology records (May 01, 2018 to July 31, 2020) to find all thyroid nodules at one institution characterized as having lobulated margins using the ACR TI-RADS lexicon and subsequently undergoing fine needle aspiration (FNA). Nodule margins were evaluated to note the presence or absence of microlobulation, quantitatively defined as a protrusion with a base <2.5 mm in length. The impact to detection of malignant nodules and avoidance of benign FNA when margin points for lobulation were added only when microlobulated was analyzed. RESULTS: 58 of 516 thyroid nodules undergoing US-guided FNA were classified as lobulated, comprising the study population. 21 (36.2%) had microlobulated margins, with 12 of the 21 (57.1%) being malignant. Comparatively, of the 37 nodules showing only macrolobulated margins without microlobulation, only 2 (5.4%) were malignant (P < .0001). For 53 nodules ≥10 mm, 15 (28.3%) benign nodules would not have met size criteria for FNA had points for margins not been applied when only showing macrolobulation, whereas all 10 malignant nodules would still have been sampled. CONCLUSION: Adding two points to the ACR TI-RADS score for lobulated thyroid nodules should only apply when microlobulations are present.

Proportion of Malignancy and Evaluation of Sonographic Features of Thyroid Nodules Classified as Highly Suspicious Using ACR TI-RADS Criteria.

OBJECTIVES: The reported malignancy rate of highly suspicious thyroid nodules based on the ACR TI-RADS criteria (TI-RADS category 5 [TR5]) varies widely. The objective of our study was to determine the rate of malignancy of TR5 nodules at our institution. We also aimed to determine the predictive values of individual sonographic features, as well as the correlation of total points assigned to a nodule and rate of malignancy. METHODS: Our single-institution retrospective study evaluated 450 TR5 nodules that had cytology results available, in 399 patients over a 1-year period. Sonographic features and total TI-RADS points were determined by the interpreting radiologist. Statistical analyses included logistic regression models to find factors associated with increased odds of malignancy, and computing sensitivity, specificity, positive and negative predictive values of various individual sonographic features. RESULTS: Of the 450 nodules, 95 (21.1%, 95% exact confidence interval 17.4-25.2%) were malignant. Each additional TI-RADS point increased the odds of malignancy (adjusted odds ratio 1.35, 95% confidence interval 1.13-1.60, P < .001). "Very hypoechoic" was the sonographic feature with the highest specificity and positive predictive value for malignancy (95.5 and 44.8%, respectively), while "punctate echogenic foci" had the lowest positive predictive value (20.0%). CONCLUSIONS: The rate of malignancy of TR5 nodules at our institution was 21.1%, which is lower than other malignancy rates reported in the literature. The total number of points assigned on the basis of the TI-RADS criteria was positively associated with malignancy, which indicates that TR5 should be viewed as a spectrum of risk.

Explainable Automated TI-RADS Evaluation of Thyroid Nodules.

A thyroid nodule, a common abnormal growth within the thyroid gland, is often identified through ultrasound imaging of the neck. These growths may be solid- or fluid-filled, and their treatment is influenced by factors such as size and location. The Thyroid Imaging Reporting and Data System (TI-RADS) is a classification method that categorizes thyroid nodules into risk levels based on features such as size, echogenicity, margin, shape, and calcification. It guides clinicians in deciding whether a biopsy or other further evaluation is needed. Machine learning (ML) can complement TI-RADS classification, thereby improving the detection of malignant tumors. When combined with expert rules (TI-RADS) and explanations, ML models may uncover elements that TI-RADS misses, especially when TI-RADS training data are scarce. In this paper, we present an automated system for classifying thyroid nodules according to TI-RADS and assessing malignancy effectively. We use ResNet-101 and DenseNet-201 models to classify thyroid nodules according to TI-RADS and malignancy. By analyzing the models' last layer using the Grad-CAM algorithm, we demonstrate that these models can identify risk areas and detect nodule features relevant to the TI-RADS score. By integrating Grad-CAM results with feature probability calculations, we provide a precise heat map, visualizing specific features within the nodule and potentially assisting doctors in their assessments. Our experiments show that the utilization of ResNet-101 and DenseNet-201 models, in conjunction with Grad-CAM visualization analysis, improves TI-RADS classification accuracy by up to 10%. This enhancement, achieved through iterative analysis and re-training, underscores the potential of machine learning in advancing thyroid nodule diagnosis, offering a promising direction for further exploration and clinical application.

Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation.

Thyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet 600,000 FNAs are still performed annually, with a PPV of 5-7% (up to 30%). Artificial intelligence (AI) systems have the capacity to improve diagnostic accuracy and workflow efficiency when integrated into clinical decision pathways. Previous studies have evaluated AI systems against physicians, whereas we aim to compare the benefits of incorporating AI into their final diagnostic decision. This work analyzed the potential for artificial intelligence (AI)-based decision support systems to improve physician accuracy, variability, and efficiency. The decision support system (DSS) assessed was Koios DS, which provides automated sonographic nodule descriptor predictions and a direct cancer risk assessment aligned to ACR TI-RADS. The study was conducted retrospectively between (08/2020) and (10/2020). The set of cases used included 650 patients (21% male, 79% female) of age 53 ± 15. Fifteen physicians assessed each of the cases in the set, both unassisted and aided by the DSS. The order of the reading condition was randomized, and reading blocks were separated by a period of 4 weeks. The system's impact on reader accuracy was measured by comparing the area under the ROC curve (AUC), sensitivity, and specificity of readers with and without the DSS with FNA as ground truth. The impact on reader variability was evaluated using Pearson's correlation coefficient. The impact on efficiency was determined by comparing the average time per read. There was a statistically significant increase in average AUC of 0.083 [0.066, 0.099] and an increase in sensitivity and specificity of 8.4% [5.4%, 11.3%] and 14% [12.5%, 15.5%], respectively, when aided by Koios DS. The average time per case decreased by 23.6% (p = 0.00017), and the observed Pearson's correlation coefficient increased from r = 0.622 to r = 0.876 when aided by Koios DS. These results indicate that providing physicians with automated clinical decision support significantly improved diagnostic accuracy, as measured by AUC, sensitivity, and specificity, and reduced inter-reader variability and interpretation times.

Risk stratification of indeterminate thyroid nodules using ultrasound and machine learning algorithms.

BACKGROUND: Indeterminate thyroid nodules (Bethesda III) are challenging to characterize without diagnostic surgery. Auxiliary strategies including molecular analysis, machine learning models, and ultrasound grading with Thyroid Imaging, Reporting and Data System (TI-RADS) can help to triage accordingly, but further refinement is needed to prevent unnecessary surgeries and increase positive predictive values. DESIGN: Retrospective review of 88 patients with Bethesda III nodules who had diagnostic surgery with final pathological diagnosis. MEASUREMENTS: Each nodule was retrospectively scored through TI-RADS. Two deep learning models were tested, one previously developed and trained on another data set, mainly containing determinate cases and then validated on our data set while the other one trained and tested on our data set (indeterminate cases). RESULTS: The mean TI-RADS score was 3 for benign and 4 for malignant nodules (p = .0022). Radiological high risk (TI-RADS 4,5) and low risk (TI-RADS 2,3) categories were established. The PPV for the high radiological risk category in those with >10 mm nodules was 85% (CI: 70%-93%). The NPV for low radiological risk in patients >60 years (mean age was 100% (CI: 83%-100%). The area under the curve (AUC) value of our novel classifier was 0.75 (CI: 0.62-0.84) and differed significantly from the chance-level (p < .00001). CONCLUSIONS: Novel radiomic and radiologic strategies can be employed to assist with preoperative diagnosis of indeterminate thyroid nodules.

Comparison of diagnostic accuracy and utility of artificial intelligence-optimized ACR TI-RADS and original ACR TI-RADS: a multi-center validation study based on 2061 thyroid nodules.

OBJECTIVE: To determine if artificial intelligence-based modification of the Thyroid Imaging Reporting Data System (TI-RADS) would be better than the current American College of Radiology (ACR) TI-RADS for risk stratification of thyroid nodules. METHODS: A total of 2061 thyroid nodules (in 1859 patients) sampled with fine-needle aspiration or operation were retrospectively analyzed between January 2017 and July 2020. Two radiologists blinded to the pathologic diagnosis evaluated nodule features in five ultrasound categories and assigned TI-RADS scores by both ACR TI-RADS and AI TI-RADS. Inter-rater agreement was assessed by asking another two radiologists to score a set of 100 nodules independently. The reference standard was postoperative pathological or cytopathological diagnosis according to the Bethesda system. Inter-rater agreement was determined using intraclass correlation coefficient (ICC). RESULTS: AI TI-RADS assigned lower TI-RADS risk levels than ACR TI-RADS (p < 0.001) and had larger area under receiver operating characteristic curve (0.762 vs. 0.679, p < 0.001). The sensitivities of ACR TI-RADS and AI TI-RADS were similar (86.7% vs. 82.2%, p = 0.052), but specificity was higher with AI TI-RADS (70.2% vs. 49.2%, p < 0.001). AI TI-RADS downgraded 743 (48.63%) benign nodules, indicating that 328 (42.3% of 776 biopsied nodules) unnecessary fine-needle aspirations (FNA) could have been avoided. Inter-rater agreement was better with AI TI-RADS than with ACR TI-RADS (ICC, 0.808 vs. 0.861, p < 0.001). CONCLUSION: AI TI-RADS can achieve meaningful reduction in the number of benign thyroid nodules recommended for biopsy and significantly improve specificity despite a slight decrease in sensitivity. KEY POINTS: • AI TI-RADS assigned lower TI-RADS risk levels than ACR TI-RADS, showing similar sensitivity but higher specificity. • Half of the benign nodules can be downgraded of which 42.3% of biopsy nodules avoided unnecessary fine-needle aspiration (FNA). • AI TI-RADS had a better overall inter-rater agreement.

Clinical diagnostic value of American College of Radiology thyroid imaging report and data system in different kinds of thyroid nodules.

BACKGROUND: To evaluate the diagnostic value of American College of Radiology (ACR) score and ACR Thyroid Imaging Report and Data System (TI-RADS) for benign nodules, medullary thyroid carcinoma (MTC) and papillary thyroid carcinoma (PTC) through comparing with Kwak TI-RADS. METHODS: Five hundred nine patients diagnosed with PTC, MTC or benign thyroid nodules were included and classified into the benign thyroid nodules group (n = 264), the PTC group (n = 189) and the MTC group (n = 56). The area under the curve (AUC) values were analyzed and the receiver operator characteristic (ROC) curves were drawn to compare the diagnostic efficiencies of ACR score, ACR TI-RADS and KWAK TI-RADS on benign thyroid nodules, MTC and PTC. RESULTS: The AUC values of ACR score, ACR TI-RADS and Kwak TI-RADS for distinguishing malignant nodules from benign nodules were 0.914 (95%CI: 0.886-0.937), 0.871 (95%CI: 0.839-0.899) and 0.885 (95%CI: 0.854-0.911), respectively. In distinguishing of patients with MTC from PTC, the AUC values of ACR score, ACR TI-RADS and Kwak TI-RADS were 0.650 (95%CI: 0.565-0.734), 0.596 (95%CI: 0.527-0.664), and 0.613 (95%CI: 0.545-0.681), respectively. The AUC values of ACR score, ACR TI-RADS and Kwak TI-RADS for the discrimination of patients with MTC, PTC or benign nodules from patients without MTC, PTC or benign nodules were 0.899 (95%CI: 0.882-0.915), 0.865 (95%CI: 0.846-0.885), and 0.873 (95%CI: 0.854-0.893), respectively. CONCLUSION: The ACR score performed the best, followed ex aequo by the ACR and Kwak TI-RADS in discriminating patients with malignant nodules from benign nodules and patients with MTC from PTC.

Consistency of Thyroid Imaging Reporting and Data System Reporting in Community-Based Imaging Centers Versus a Large Tertiary Hospital.

OBJECTIVE: In our country, thyroid nodules are sonographically evaluated in health maintenance organization (HMO) imaging centers, and patients are referred to tertiary hospitals for ultrasound-guided fine-needle aspiration (FNA) biopsy when indicated. We evaluated the concordance in Thyroid Imaging Reporting and Data System (TI-RADS) classification reporting between these sites. METHODS: We conducted a retrospective cohort study reviewing the sonographic features of thyroid nodules evaluated both at the HMO and a large tertiary center between January 2018 and December 2019. The primary outcome was concordance between the TI-RADS classification at both sites. Additional endpoints included correlation of TI-RADS to the Bethesda category following FNA and correlation of TI-RADS with malignancy on final pathology at each site. RESULTS: The records of 336 patients with 370 nodules were reviewed. The level of concordance was poor (19.8%), with 277 (74.8%) nodules demonstrating higher TI-RADS and 20 (5.4%) lower TI-RADS at the HMO compared to the hospital (P < .001; weighted κ = 0.120). FNA results were available for 236 (63.8%) nodules. The Bethesda category strongly correlated with the hospital TI-RADS (P < .001), yet not with HMO TI-RADS (P = .123). In the surgically removed 57 nodules, a strong correlation was identified between the malignancy on final pathology and TI-RADS documented at the hospital (P < .001), yet not at the HMO (P = .259). CONCLUSIONS: There is poor agreement between TI-RADS classification on ultrasound performed in the HMO compared to a tertiary hospital. The hospital's TI-RADS strongly correlated with the Bethesda category and the final risk of malignancy, unlike the HMO.

Prediction of central compartment nodal metastases in papillary thyroid cancer using TI-RADS score, blood flow, and multifocality.

BACKGROUND: The relationship between the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) and the risk of lymph node metastases in papillary thyroid cancer (PTC) could improve the detection rate of lymph node metastases in thyroid cancer and provide a scientific basis for clinical diagnosis. PURPOSE: To evaluate the risk of lymph node metastases of PTC associated with the score from ACR TI-RADS adjusted for other correlative factors. MATERIAL AND METHODS: A total of 560 patients with pathologically confirmed PTC were included in the study and classified into a metastases group and a non-metastases group. Clinical and pathological manifestations of the patients were collected. RESULTS: The median TI-RADS score was 13 (p25-p75 = 11-14) among the patients with lymph node metastases, higher than those without metastases 9 (8-10) (P < 0.001). Multiple logistic regression indicated that TI-RADS score (odds ratio [OR] = 2.204), male sex (OR = 2.376), multifocality (OR = 4.170), and rich blood flow (OR = 3.656) were risk factors for lymph node metastases in patients with thyroid carcinoma. Some related factors such as TI-RADS score, age(<45years old), male, multifocality and rich blood flow were related to lymph node metastases in the central area of the neck which could provide therapeutic strategy for further treatment. CONCLUSION: it is not just the TI-RADS score but also multifocality, blood flow, and sex that influence the prediction of the risk of PTC central lymph node metastases.

Ultrasonic Characterization of Primary Squamous Cell Carcinoma of the Thyroid.

OBJECTIVES: To identify the ultrasonographic characteristics of primary squamous cell carcinoma of the thyroid (PSCCT), and to assess the value of the 2015 American Thyroid Association (ATA) guideline and 2017 American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS) in the evaluation of this disease. METHODS: Eight patients with 9 PSCCTs over a 20-year study period were enrolled. Ultrasonic characteristics including nodule echogenicity, composition, shape, margin, calcification, size, vascularity, and cervical lymphadenopathy were reviewed. All nodules were then evaluated by 2017 ACR TI-RADS and 2015 ATA guidelines. RESULTS: The average size of PSCCTs was 3.87 ± 1.41 cm. All PSCCTs were hypoechoic or very hypoechoic, solid nodules with intranodular vascularity. The average resistive index (RI) was 0.84 ± 0.18. Near half of PSCCTs (44.4%) demonstrated extrathyroidal extension. Taller-than-wide signs and cervical lymphadenopathy were observed in 33.3% of PSCCTs, and microcalcification was observed in 11.1% of them. All PSCCTs were classified as high suspicion patterns by 2015 ATA and recommended for fine-needle aspiration (FNA). Six PSCCTs (66.7%) were classified as grade 5 by 2017 ACR TI-RADS, while the remaining were grade 4. 88.9% of PSCCTs were recommended for FNA based on 2017 ACR TI-RADS. CONCLUSION: PSCCT has certain ultrasonic features, including relatively large, hypoechoic, or very hypoechoic solid nodules with intranodular vascularity and extrathyroidal extension. Both 2015 ATA and 2017 ACR TI-RADS could identify PSCCT as suspicious for malignancy.

Multimodal ultrasound imaging: A method to improve the accuracy of diagnosing thyroid TI-RADS 4 nodules.

Thyroid nodule is a common and frequently occurring disease in the neck in recent years, and ultrasound has become the preferred imaging diagnosis method for thyroid nodule due to its advantages of noninvasive, nonradiation, real-time, and repeatable. The thyroid imaging, reporting and data system (TI-RADS) classification standard scores suspicious nodules that are difficult to determine benign and malignant as grade 4, and further pathological puncture is recommended clinically, which may lead to a large number of unnecessary biopsies and operations. Including conventional ultrasound, ACR TI-RADS, shear wave elastography, super microvascular imaging, contrast enhanced ultrasound, "firefly," artificial intelligence, and multimodal ultrasound imaging used in combination. In order to identify the most clinically significant malignant tumors when reducing invasive operations. This article reviews the application and research progress of multimodal ultrasound imaging in the diagnosis of TI-RADS 4 thyroid nodules.

Use of Web-Based Calculator for the Implementation of ACR TI-RADS Risk-Stratification System.

In this article, we demonstrate the use of a software-based radiologist reporting tool for the implementation of American College of Radiology Thyroid Imaging, Reporting and Data System thyroid nodule risk-stratification. The technical details are described with emphasis on addressing the information security and patient privacy issues while allowing it to integrate with the electronic health record and radiology reporting dictation software. Its practical implementation is assessed in a quality improvement project in which guideline adherence and recommendation congruence were measured pre and post implementation. The descriptions of our solution and the release of the open-sourced codes may be helpful in future implementation of similar web-based calculators.