Risk Stratification Of Thyroid Nodules Research Articles

Sonographic risk stratification of FDG-avid thyroid nodules using the Thyroid Imaging Reporting and Data System.

The increasing usage of positron emission tomography/computed tomography (PET/CT) for detection and monitoring of malignancy has led to an increase in incidental detection of thyroid nodules. Nodules that demonstrate increased avidity for 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) have been shown to carry a high incidence of malignancy and warrant further investigation. At present, there has been limited research on the risk stratification of FDG-avid thyroid incidentalomas. Thus, this study aims to evaluate the efficacy of the ACR TIRADS classification in the risk stratification of such nodules. Data were collected retrospectively for FDG-avid thyroid incidentalomas over a 10-year period. Nodules were characterised using the TIRADS classification and, subsequently, underwent fine-needle aspirate cytology. Cytological findings were classified using the Bethesda reporting system. Non-diagnostic samples (Bethesda class I) were excluded. The remaining samples were divided into two groups: benign (Bethesda class II) or suspicious for malignancy/malignant (Bethesda class III or above). Thirty-six percent of low-risk nodules and 45% of high-risk nodules were malignant, respectively (P = 0.516). The sensitivity and specificity of TIRADS for detection of malignant nodules were 56% and 54%, respectively. There were no malignant TIRADS 1 or 2 nodules. The absence of any suspicious sonographic features had a 1.0 negative predictive value. FDG-avid nodules classified as TIRADS 1 or 2 or have no suspicious ultrasound features have a 0% incidence of malignancy and thus may not require further assessment with fine-needle aspirate cytology (FNA) when detected incidentally. FDG-avid nodules that are TIRADS 3 or above should undergo FNA regardless of size due to the high risk of malignancy and poor sensitivity of the TIRADS classification system.

Meaning of ACR-TIRADS recommendation in favor of follow-up rather than FNAC in thyroid nodules.

Thyroid Imaging Reporting and Data Systems (TIRADSs) have been largely diffused for their high accuracy in risk stratification of thyroid nodules (TNs) and their selection for fine-needle aspiration cytology (FNAC). The most popular TIRADSs are ACR-, EU-, and K-TIRADS, with some discrepancies each other. One major difference is that ACR-TIRADS includes a recommendation in favor of follow-up in TNs having a major diameter insufficient to indicate FNAC. The present study aimed to explore prevalence and significance of this recommendation. EU- and K-TIRADS were used as comparator. A retrospective series of thyroidectomies was searched according to a pre-defined protocol. The study period was 2019-2023. Preoperative ultrasound images were reviewed by radiologists blinded of clinical data. Matching of TIRADS and histology was performed later. Histology was the gold standard. The study series included 39 TNs classified as category 3, 4, or 5 and assessed for follow-up according to ACR-TIRADS. The overall cancer frequency was 25.6%, being 13% in category 3, 20% in category 4, and 83.3% in category 5. The category assessment according to ACR-, EU-, and K-TIRADS was not significantly different. EU-TIRADS indicated FNAC in 10 TNs of which two cancers and eight benign lesions. K-TIRADS recommended FNAC in 32 TNs of which seven cancers and 25 benign lesions. TNs assessed for follow-up according to ACR-TIRADS are cancer in one-fourth of cases. EU- and, especially, K-TIRADS allow us to select for FNAC cancers, with the burden of non-negligible frequency of unnecessary FNACs.

Comprehensive Review of the Imaging Recommendations for Diagnosis, Staging, and Management of Thyroid Carcinoma.

Thyroid cancer is the most common head and neck cancer (HNC) in the world. In this article, we comprehensively cover baseline, posttreatment, and follow-up imaging recommendations for thyroid carcinomas along with the eighth edition of the tumor, node, metastasis (TNM) staging system proposed by the American Joint Committee on Cancer (AJCC) and the Union for International Cancer Control (UICC). We include characterization and risk stratification of thyroid nodules on ultrasound (US) proposed by various international bodies. Management guidelines (depending upon the type of thyroid carcinoma) based on the international consensus recommendations (mainly by the American Thyroid Association) are also extensively covered in this article, including the role of a radioiodine scan. The management of recurrent disease is also briefly elucidated in this article. In addition, we cover the risk factors and etiopathogenesis of thyroid carcinoma along with the non-imaging diagnostic workup essential for thyroid carcinoma management, including the significance of genetic mutations. US is the diagnostic imaging modality of choice, with US-guided fine needle aspiration (FNA) being the procedure of choice for tissue diagnosis. The roles of computed tomography (CT), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography/CT (FDG-PET/CT) in thyroid carcinoma staging are also specified. Through this article, we aim to provide a comprehensive reference guide for the radiologists and the clinicians in the pursuit of optimal care for patients with thyroid carcinoma.

Stepwise analysis of thyroid diagnostic modalities with genomic imprinting detection.

The current preoperative malignancy risk evaluation for thyroid nodules involves stepwise diagnostic modalities including ultrasonography, thyroid function serology and fine-needle aspiration (FNA) cytopathology, respectively. We aimed to substantiate the stepwise contributions of each diagnostic step and additionally investigate the diagnostic significance of quantitative chromogenic imprinted gene in-situ hybridization (QCIGISH)-an adjunctive molecular test based on epigenetic imprinting alterations. A total of 114 cytopathologically-diagnosed and histopathologically-confirmed thyroid nodules with complete ultrasonographic and serological examination records were evaluated using QCIGISH in the study. Logistic regression models for thyroid malignancy prediction were developed with the stepwise addition of each diagnostic modality and the contribution of each step evaluated in terms of discrimination performance and goodness-of-fit. From the baseline model using ultrasonography [area under the receiver operating characteristics curve (AUROC): 0.79; 95% confidence interval (CI): 0.71-0.86], significant improvements in thyroid malignancy discrimination were observed with the stepwise addition of thyroid function serology (AUROC: 0.82; 95% CI: 0.74-0.90; P=0.23) and FNA cytopathology (AUROC: 0.88; 95% CI: 0.81-0.94; P=0.02), respectively. The inclusion of QCIGISH as an adjunctive molecular test further advanced the preceding model's diagnostic performance (AUROC: 0.95; 95% CI: 0.91-1.00, P=0.007). Our study demonstrated the significant stepwise diagnostic contributions of standard clinical assessments in the malignancy risk stratification of thyroid nodules. However, the addition of molecular imprinting detection further enabled a more accurate and definitive preoperative evaluation especially for morphologically indeterminate thyroid nodules and cases with potentially discordant results among standard modalities.

Enhancing Thyroid Nodule Risk Stratification: Integrating Artificial Intelligence and Polygenic Risk Scores

Enhancing Thyroid Nodule Risk Stratification: Integrating Artificial Intelligence and Polygenic Risk Scores

Clinical Evaluation of an Artificial Intelligence-Based Decision Support System for the Diagnosis and American College of Radiology Thyroid Imaging Reporting and Data System Classification of Thyroid Nodules.

Background: This study aimed to evaluate the clinical impact of an artificial intelligence (AI)-based decision support system (DSS), Koios DS, on the analysis of ultrasound imaging and suspicious characteristics for thyroid nodule risk stratification. Methods: A retrospective ultrasound study was conducted on all thyroid nodules with histological findings from June 2021 to December 2022 in a thyroid nodule clinic. The diagnostic performance of ultrasound imaging was evaluated by six readers on the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) before and after the use of the AI-based DSS and by AI itself. Results: A total of 172 patients (83.1% women) with a mean age of 52.3 ± 15.3 years were evaluated. The mean maximum nodular diameter was 2.9 ± 1.2 cm, with 11.0% being differentiated thyroid carcinomas. Among the nodules initially classified as ACR TI-RADS 3 and 4, AI reclassified 81.4% and 24.5% into lower risk categories, respectively. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the readers and the AI-based DSS versus histological diagnosis. There was an increase in the area under the ROC curve (AUROC) after the use of AI (0.776 vs. 0.817, p < 0.001). The AI-based DSS improved the mean sensitivity (Sens) (82.3% vs. 86.5%) and specificity (Spe) (38.3% vs. 54.8%), produced a high negative predictive value (94.5% vs. 96.4%), and increased the positive predictive value (PPV) (14.0% vs. 16.1%) and diagnostic precision (43.0% vs. 49.3%). Based on the ACR TI-RADS score, there was significant improvement in interobserver agreement after the use of AI (r = 0.741 for ultrasound imaging alone vs. 0.981 for ultrasound imaging and the AI-based DSS, p < 0.001). Conclusions: The use of an AI-based DSS was associated with overall improvement in the diagnostic efficacy of ultrasound imaging, based on the AUROC, as well as an increase in Sens, Spe, negative and PPVs, and diagnostic accuracy. There was also a reduction in interobserver variability and an increase in the degree of concordance with the use of AI. AI reclassified more than half of the nodules with intermediate ACR TI-RADS scores into lower risk categories.

Comparative analysis of the diagnostic efficiency of the four thyroid imaging reporting and data systems

The purpose of this study was to comparative analyze the diagnostic efficiency of Thyroid Imaging Reporting and Data System proposed by Kwak, American College of Radiology, the European Thyroid Association and the Chinese Medical Association, respectively (Kwak TI-RADS, ACR TI-RADS, EU-TIRADS and C-TIRADS, respectively) for malignant risk stratification of thyroid nodules. A total of 1142 thyroid nodules confirmed by cytological or surgical pathologies were retrospectively classified according to Kwak TI-RADS, ACR TI-RADS, EU-TIRADS and C-TIRADS. The area under the receiver operating characteristic curve (AUC), the diagnostic sensitivity, specificity, Youden index, and accuracy of each TI-RADS for nodules was calculated and compared. The diagnostic sensitivity of ACR TI-RADS was 85.5%, which was lowest (all P < 0.05). The diagnostic specificity of ACR TI-RADS was higher than that of Kwak TI-RADS and EU-TIRADS (all P < 0.05), but there was no significant difference compared with that of C-TIRADS (x2 = 2.335, P = 0.126). The diagnostic accuracies of the four methods were more than 85%, and there were no significant differences (all P > 0.05). The AUC of C-TIRADS was highest among the four methods (all P < 0.05). The four methods have high diagnostic efficiency in malignant risk stratification of thyroid nodules, and the AUC of C-TIRADS was higher than that of the other three methods.

Localization and Risk Stratification of Thyroid Nodules in Ultrasound Images Through Deep Learning

ObjectiveDeep learning algorithms have commonly been used for the differential diagnosis between benign and malignant thyroid nodules. In this study, we aimed to develop an integrated system that combines deep learning model and clinical standard thyroid imaging reporting and data system (TI-RADS) for the simultaneous segmentation and risk stratification of thyroid nodules. Methods304 ultrasound images from two independent sites with TI-RADS 4 thyroid nodules were collected. Edge-connection and Criminisi algorithm was used to remove manually-induced markers in ultrasound images. An integrated system based on TI-RADS and mask region-based convolution neural network (Mask R-CNN) was proposed to stratify subclasses of TI-RADS 4 thyroid nodules and to segment thyroid nodules in the ultrasound images. Accuracy and precision-recall curve were used to evaluate stratification performance, dice similarity coefficient (DSC) between the segmentation of Mask R-CNN and the radiologist's contour was used to evaluate segmentation performance of the model. ResultsThe combined approach could significantly enhance the performance of the proposed integrated system. Overall stratification accuracy of TI-RADS 4 thyroid nodules, mean average precision, and mean DSC of the proposed model in the independent test set were 90.79%, 0.8579, and 0.83, respectively. Specifically, stratification accuracy for TI-RADS 4a, 4b and 4c thyroid nodules were 95.83%, 84.21%, and 77.78%. ConclusionAn integrated system combining TI-RADS and deep learning model was developed. The system can provide clinicians with not only diagnostic assistance from TI-RADS, but also accurate segmentation of thyroid nodules, which improves the applicability of the system in clinical practice.

Improved cancer risk stratification of isoechoic thyroid nodules to reduce unnecessary biopsies using quantitative ultrasound.

Gray-scale ultrasound (US) is the standard-of-care for evaluating thyroid nodules (TNs). However, the performance is better for the identification of hypoechoic malignant TNs (such as classic papillary thyroid cancer) than isoechoic malignant TNs. Quantitative ultrasound (QUS) utilizes information from raw ultrasonic radiofrequency (RF) echo signal to assess properties of tissue microarchitecture. The purpose of this study is to determine if QUS can improve the cancer risk stratification of isoechoic TNs. Patients scheduled for TN fine needle biopsy (FNB) were recruited from the Thyroid Health Clinic at Boston Medical Center. B-mode US and RF data (to generate QUS parameters) were collected in 274 TNs (163 isoechoic, 111 hypoechoic). A linear combination of QUS parameters (CQP) was trained and tested for isoechoic [CQP(i)] and hypoechoic [CQP(h)] TNs separately and compared with the performance of conventional B-mode US risk stratification systems. CQP(i) produced an ROC AUC value of 0.937+/- 0.043 compared to a value of 0.717 +/- 0.145 (p >0.05) for the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) and 0.589 +/- 0.173 (p >0.05) for the American Thyroid Association (ATA) risk stratification system. In this study, CQP(i) avoids unnecessary FNBs in 73% of TNs compared to 55.8% and 11.8% when using ACR TI-RADS and ATA classification system. This data supports that a unique QUS-based classifier may be superior to conventional US stratification systems to evaluate isoechoic TNs for cancer and should be explored further in larger studies.

Comparison of the diagnostic performance of three ultrasound thyroid nodule risk stratification systems for follicular thyroid neoplasm: K-TIRADS, ACR -TIRADS and C-TIRADS.

To explore the diagnostic performance of the currently used ultrasound-based thyroid nodule risk stratification systems (K-TIRADS, ACR -TIRADS, and C-TIRADS) in differentiating follicular thyroid adenoma (FTA) from follicular thyroid carcinoma (FTC). Clinical data and preoperative ultrasonographic images of 269 follicular thyroid neoplasms were retrospectively analyzed. All of them were detected by Color Doppler ultrasound instruments equipped with high-frequency liner array probes (e.g. Toshiba Apoli500 with L5-14MHZ; Philips IU22 with L5-12MHZ; GE LOGIQ E9 with L9-12MHZ and MyLab Class C with L9-14MHZ). The diagnostic performance of three TIRADS classifications for differentiating FTA from FTC was evaluated by drawing the receiver operating characteristic (ROC) curves and calculating the cut-off values. Of the 269 follicular neoplasms (mean size, 3.67±1.53 cm), 209 were FTAs (mean size, 3.56±1.38 cm) and 60 were FTCs (mean size, 4.07±1.93 cm). There were significant differences in ultrasound features such as margins, calcifications, and vascularity of thyroid nodules between the FTA and FTC groups (P < 0.05). According to the ROC curve comparison analysis, the diagnostic cut-off values of K-TIRADS, ACR-TIRADS, and C-TIRADS for identifying FTA and FTC were K-TR4, ACR-TR4, and C-TR4B, respectively, and the areas under the curves were 0.676, 0.728, and 0.719, respectively. The difference between ACR-TIRADS and K-TIRADS classification was statistically significant (P = 0.0241), whereas the differences between ACR-TIRADS and C-TIRADS classification and between K-TIRADS and C-TIRADS classification were not statistically significant (P > 0.05). The three TIRADS classifications were not conducive to distinguishing FTA from FTC. It is necessary to develop a novel malignant risk stratification system specifically for the identification of follicular thyroid neoplasms.

Risk stratification of thyroid nodules: Assessing the suitability of ChatGPT for text-based analysis

PurposeAccurate risk stratification of thyroid nodules is essential for optimal patient management. This study aimed to assess the suitability of ChatGPT for risk stratification of thyroid nodules using a text-based evaluation. MethodsA dataset was compiled comprising 50 anonymized clinical reports and associated risk assessments for thyroid nodules. The Chat Generative Pre-trained Transformer (ChatGPT) was used to classify sonographic patterns in accordance with the Thyroid Imaging Reporting and Data System (TI-RADS). The model's performance was assessed using various criteria, including sensitivity, specificity, and accuracy. A comparative analysis was conducted, evaluating the model against investigator-based risk stratification as well as histology. ResultsWith an overall agreement rate of 42 % in comparison with examiner-based evaluation (TI-RADS 1–5), the results show that ChatGPT has moderate potential for predicting the risk of malignancy in thyroid nodules using text-based reports. The chatbot model achieved a sensitivity of 86.7 %, a specificity of 10.7 %, and an overall accuracy of 68 % when distinguishing between low-risk (TI-RADS 2 and 3) and high-risk (TI-RADS 4 and 5) categories. Interrater reliability was calculated with a Cohen's kappa of 0.686. ConclusionThis study highlights the potential of ChatGPT in assisting clinicians with risk stratification of thyroid nodules. The results suggest that ChatGPT can facilitate personalized treatment decisions, although the agreement rate is still low. Further research and validation studies are necessary to establish the clinical applicability and generalizability of ChatGPT in routine practice. The integration of ChatGPT into clinical workflows has the potential to enhance thyroid nodule risk assessment and improve patient care.

OR32-02 Thyroid Cancer Polygenic Risk Score Improves Risk Stratification Of Thyroid Nodules When Added To Ultrasound Imaging

Abstract Disclosure: N. Pozdeyev: None. M. Dighe: None. M. Barrio: None. C. Raeburn: None. H.A. Smith: None. M. Fisher: None. S. Chavan: None. N. Rafaels: None. J. Shortt: None. M. Leu: None. T. Clark: None. C. Marshall: None. B.R. Haugen: None. D. Subramanian: None. K. Crooks: None. C. Gignoux: None. T.A. Cohen: None. Purpose. Evaluating thyroid nodules to rule out malignancy is a common clinical task. Image-based risk stratification schemas rely on the presence of high-risk thyroid nodule sonographic features and, therefore, are less suitable for the diagnosis of malignant thyroid nodules that have a benign appearance on the ultrasound. To mitigate the deficiency of thyroid nodule evaluation relying solely on the sonographic characteristics, we used thyroid cancer polygenic risk score (PRS) to complement deep learning analysis of ultrasound images. Methods. A supervised deep learning classifier of thyroid nodules was trained on 32,545 thyroid US images from 621 nodules and tested on an independent set of 232 nodules from patients genotyped on the Illumina's MEGAEX platform. The deep-learning thyroid nodule classifier was developed by fine-tuning a BiT-M ResNet-50x1 convolutional neural network (CNN) pre-trained on the ImageNet-21k dataset. A polygenic risk score (PRS) was calculated using thyroid cancer genome-wide association meta-analysis summary statistics from the Global Biobank Meta-analysis Initiative. The thyroid cancer PRS was defined as a weighted sum of five alleles with the strongest association with thyroid cancer. CNN predictions and PRS were combined into a meta-classifier using logistic regression with or without genetic ancestry and demographic covariates. Results. The CNN classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.83 on the out-of-sample test set of 232 thyroid nodules. The CNN classifier incorrectly classified thyroid nodules without suspicious sonographic characteristics belonging to difficult-to-diagnose subtypes such as follicular thyroid cancer and follicular variant of papillary thyroid cancer. Combining predictions from the CNN classifier with PRS into a cross-validated classifier improved AUC to 0.868 (DeLong test, p = 0.05). Incorporating genetic ancestry in the form of five genetic principal components further improved AUC of the benign vs. malignant CNN + PRS thyroid nodule classifier to 0.885 (p = 0.007). Finally, when age, sex, and nodule dimensions were considered, the AUC of the meta-classifier increased to 0.915 (p = 2.3e-4). The meta-classifier including predictions from CNN, PRS, and genetic principal components showed a sensitivity of 0.95, specificity of 0.61, NPV of 0.97, and PPV of 0.5. This performance was superior to that of the clinical Thyroid Imaging Reporting and Data System (TI-RADS) as reported by radiologists in ultrasound reports. Conclusions. For the first time, we showed PRS provides an orthogonal thyroid cancer risk assessment complementary to the ultrasound image-based thyroid nodule risk evaluation. This proof-of-concept study opens an opportunity for developing next-generation schemas for thyroid nodule evaluation incorporating clinical, imaging, and genetic data. Presentation: Sunday, June 18, 2023

Concordance of the ACR TI-RADS Classification With Bethesda Scoring and Histopathology Risk Stratification of Thyroid Nodules

Although most thyroid nodules are benign, 10% to 15% of them harbor cancer. Thyroid ultrasonography is useful for risk stratification of nodules, and American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) classification provides recommendations for fine-needle aspiration cytology (FNAC) based on objective ultrasonographic features of these nodules. To validate the concordance of ACR TI-RADS classification with Bethesda classification and histopathology. This retrospective cohort study was performed to evaluate the concordance of ACR TI-RADS classification with Bethesda classification and histopathology and was conducted in Singapore General Hospital Outpatient Otolaryngology clinic in March 2021 to May 2021. Data analysis was performed in May 2021. Results were deemed concordant when ACR TI-RADS recommendations aligned with Bethesda scores. Conversely, results were classified as nonconcordant with Bethesda scores and/or histopathology results when nodules that were recommended for FNAC yielded benign results or nodules that were not recommended for FNAC yielded malignant results. A total of 446 patients (370 women [83%]; mean [range] age, 60 [24-89] years) who underwent ultrasonography of the thyroid and ultrasonography-guided thyroid FNACs were identified. A total of 492 of 630 nodules (78.1%) were benign on FNAC (Bethesda II). Score 3 ACR TI-RADS nodules yielded the highest negative predictive values: 94.6% (95% CI, 92.9%-95.9%; P < .001) compared with Bethesda scoring and 100.0% (95% CI, 15.8%-100.0%; P = .003) compared with histopathology. Score 4 or 5 ACR TI-RADS nodules yielded positive predictive values of 2.8% and 16.2%, respectively, compared with Bethesda scoring and 6.1% and 66.7%, respectively, compared with histopathology. Small (<1.5 cm) ACR TI-RADS nodules of scores of 4 and 5 that were not recommended for FNAC yielded a malignant risk of 5.7% and 25.0% on Bethesda 5 and 6, respectively. On surgical excision, 5 of 46 (10.9%) ACR TI-RADS 4 nodules and 15 of 21 (71.4%) of ACR TI-RADS 5 nodules were confirmed to be malignant. Among nodules initially not recommended for FNAC, histopathology-proven cancer was found in 4 of 13 (30.7%) and 3 of 6 (50.0%) of nodules, respectively. These findings suggest that ACR TI-RADS score 3 nodules have a low risk of cancer and should be considered for FNAC only if nodules are 2.5 cm or larger. Patients with small (<1.5 cm) ACR TI-RADS 4 and 5 nodules should be appropriately counseled for FNAC to exclude cancer.

Role of superb microvascular imaging in thyroid nodule risk stratification

Role of superb microvascular imaging in thyroid nodule risk stratification

Computer-interpretable guidelines: electronic tools to enhance the utility of thyroid nodule clinical practice guidelines and risk stratification tools.

Clinicians seeking guidance for evaluating and managing thyroid nodules currently have several resources. The principal ones are narrative clinical guidelines and clinical risk calculators. This paper will review the strengths and weaknesses of both. The paper will introduce a concept of computer interpretable guideline, a novel way of transforming narrative guidelines in to a clinical decision support tool that can provide patient specific recommendations at the point of care. The paper then describes an experience of developing an interactive web based computer interpretable guideline for thyroid nodule management, called Thyroid Nodule Management App (TNAPP). The advantages of this approach and the potential barriers for widespread adaptation are discussed.

Comparison of the C-TIRADS, ACR-TIRADS, and ATA guidelines in malignancy risk stratification of thyroid nodules.

To compare the diagnostic performance in determining the malignancy of thyroid nodules and the fine needle aspiration (FNA) recommendations of the guidelines set forth by the Superficial Organ and Vascular Ultrasound Group of the Society of Ultrasound in Medicine of the Chinese Medical Association in 2020 [2020 Chinese Thyroid Imaging Reporting and Data System (C-TIRADS)], the American College of Radiology in 2017 (2017 ACR-TIRADS) and the American Thyroid Association in 2015 (2015 ATA guidelines). From January 2021 to December 2021, 1,228 thyroid nodules with definitive postoperative histopathology and ultrasound (US) examination within 3 months before surgery in Shantou Central Hospital were enrolled in this study. We collected the data in 2022. The participants formed a consecutive series. The clinical and US features of the nodules were retrospectively reviewed and categorized according to the 2020 C-TIRADS, the 2017 ACR-TIRADS and the 2015 ATA guidelines. The diagnostic performance and unnecessary FNA rates of the three guidelines were calculated. The 2017 ACR-TIRADS had the highest diagnostic performance [area under the receiver operating characteristic curve (AUROC) 0.938], followed by the 2020 C-TIRADS (AUROC 0.933) and the 2015 ATA guidelines (AUROC 0.928). The ATA guidelines had the highest specificity (93.38%), accuracy (92.10%) and positive predictive value (PPV) (80.56%) among the three guidelines. There were no significant differences in the sensitivity and negative predictive value (NPV) among the three guidelines. The sensitivity, specificity, PPV, NPV and accuracy of the FNA recommendations based on the C-TIRADS were 84.25%, 58.76%, 38.92%, 92.28% and 64.82%, respectively, which were higher than those of the ACR-TIRADS (57.53%, 42.94%, 23.93%, 76.43% and 46.42%, respectively) and the ATA guidelines (62.67%, 13.25%, 18.39%, 53.22% and 25.00%, respectively). Compared with the ACR-TIRADS (76.07%) and the ATA guidelines (81.61%), the C-TIRADS showed advantages in the unnecessary FNA rate (61.08%), especially in nodules larger than 20 mm. The 2020 C-TIRADS, the 2017 ACR-TIRADS and the 2015 ATA guidelines can effectively predict the malignancy risk of thyroid nodules. Compared with the 2017 ACR-TIRADS and the 2015 ATA guidelines, the 2020 C-TIRADS may offer a meaningful reduction in FNA recommendations with the highest efficacy in distinguishing thyroid carcinoma.

Diagnostic performance of ACR-TIRADS ultrasound lexicon in risk stratification of thyroid nodules

Diagnostic performance of ACR-TIRADS ultrasound lexicon in risk stratification of thyroid nodules

Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.

Background: The use of artificial intelligence (AI) in health care has grown exponentially with the promise of facilitating biomedical research and enhancing diagnosis, treatment, monitoring, disease prevention, and health care delivery. We aim to examine the current state, limitations, and future directions of AI in thyroidology. Summary: AI has been explored in thyroidology since the 1990s, and currently, there is an increasing interest in applying AI to improve the care of patients with thyroid nodules (TNODs), thyroid cancer, and functional or autoimmune thyroid disease. These applications aim to automate processes, improve the accuracy and consistency of diagnosis, personalize treatment, decrease the burden for health care professionals, improve access to specialized care in areas lacking expertise, deepen the understanding of subtle pathophysiologic patterns, and accelerate the learning curve of less experienced clinicians. There are promising results for many of these applications. Yet, most are in the validation or early clinical evaluation stages. Only a few are currently adopted for risk stratification of TNODs by ultrasound and determination of the malignant nature of indeterminate TNODs by molecular testing. Challenges of the currently available AI applications include the lack of prospective and multicenter validations and utility studies, small and low diversity of training data sets, differences in data sources, lack of explainability, unclear clinical impact, inadequate stakeholder engagement, and inability to use outside of the research setting, which might limit the value of their future adoption. Conclusions: AI has the potential to improve many aspects of thyroidology; however, addressing the limitations affecting the suitability of AI interventions in thyroidology is a prerequisite to ensure that AI provides added value for patients with thyroid disease.

Multimodality US versus Thyroid Imaging Reporting and Data System Criteria in Recommending Fine-Needle Aspiration of Thyroid Nodules.

Background Current guidelines recommend the use of conventional US for risk stratification and management of thyroid nodules. However, fine-needle aspiration (FNA) is often recommended in benign nodules. Purpose To compare the diagnostic performance of multimodality US (including conventional US, strain elastography, and contrast-enhanced US [CEUS]) with the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) in the recommendation of FNA for thyroid nodules to reduce unnecessary biopsies. Materials and Methods In this prospective study, 445 consecutive participants with thyroid nodules from nine tertiary referral hospitals were recruited between October 2020 and May 2021. With univariable and multivariable logistic regression, the prediction models incorporating sonographic features, evaluated with interobserver agreement, were constructed and internally validated with bootstrap resampling technique. In addition, discrimination, calibration, and decision curve analysis were performed. Results A total of 434 thyroid nodules confirmed at pathologic analysis (259 malignant thyroid nodules) in 434 participants (mean age, 45 years ± 12 [SD]; 307 female participants) were included. Four multivariable models incorporated participant age, nodule features at US (proportion of cystic components, echogenicity, margin, shape, punctate echogenic foci), elastography features (stiffness), and CEUS features (blood volume). In recommending FNA in thyroid nodules, the highest area under the receiver operating characteristic curve (AUC) was 0.85 (95% CI: 0.81, 0.89) for the multimodality US model, and the lowest AUC was 0.63 (95% CI: 0.59, 0.68) for TI-RADS (P < .001). At the 50% risk threshold, 31% (95% CI: 26, 38) of FNA procedures could be avoided with multimodality US compared with 15% (95% CI: 12, 19) with TI-RADS (P < .001). Conclusion Multimodality US had better performance in recommending FNA to avoid unnecessary biopsies than the TI-RADS. Clinical trial registration no. NCT04574258 © RSNA, 2023 Supplemental material is available for this article.

AIUM Practice Parameter for the Performance and Interpretation of Diagnostic Ultrasound of the Thyroid and Extracranial Head and Neck.

AIUM Practice Parameter for the Performance and Interpretation of Diagnostic Ultrasound of the Thyroid and Extracranial Head and Neck.