The incidental thyroid nodule.

Incidental thyroid nodules that are found on an imaging study performed for reasons other than thyroid pathology represent a common scenario encountered by health care providers. The initial workup for these nodules comprises a thorough history and physical examination, thyroid function tests, a dedicated thyroid ultrasound, and fine-needle aspiration of any suspicious lesions. Management ranges from observation and reassurance to surgical resection and depends on the cytologic diagnosis. In cases of cytologically indeterminate or discordant nodules, surgical excision (lobectomy) offers a definitive diagnosis, although molecular testing or a reasonable period of observation may be useful as less invasive adjuncts. CA Cancer J Clin 2018;68:97-105. © 2018 American Cancer Society.

Contemporary Management of Thyroid Nodules.

Thyroid nodules are common in the general population, with higher prevalence in women and with advancing age. Approximately 5% of thyroid nodules are malignant; the majority of this subset represents papillary thyroid cancer. Ultrasonography is the standard technique to assess the underlying thyroid parenchyma, characterize the features of thyroid nodules, and evaluate for abnormal cervical lymphadenopathy. Various risk stratification systems exist to categorize the risk of malignancy based on the ultrasound appearance of a thyroid nodule. Nodules are selected for fine-needle aspiration biopsy on the basis of ultrasound features, size, and high-risk clinical history. Cytology results are classified by the Bethesda system into six categories ranging from benign to malignant. When cytology is indeterminate, molecular testing can further risk-stratify patients for observation or surgery. Surveillance is indicated for nodules with benign cytology, indeterminate cytology with reassuring molecular testing, or non-biopsied nodules without a benign sonographic appearance.

Thyroid cancer.

The past 5-10 years have brought in a new era in the care of patients with thyroid cancer, with the introduction of transformative diagnostic and management options. Several international ultrasound-based thyroid nodule risk stratification systems have been developed with the goal of reducing unnecessary biopsies. Less invasive alternatives to surgery for low-risk thyroid cancer, such as active surveillance and minimally invasive interventions, are being explored. New systemic therapies are now available for patients with advanced thyroid cancer. However, in the setting of these advances, disparities exist in the diagnosis and management of thyroid cancer. As new management options are becoming available for thyroid cancer, it is essential to support population-based studies and randomised clinical trials that will inform evidence-based clinical practice guidelines on the management of thyroid cancer, and to include diverse patient populations in research to better understand and subsequently address existing barriers to equitable thyroid cancer care.

Collaborative Enhancement of Consistency and Accuracy in US Diagnosis of Thyroid Nodules Using Large Language Models.

Background Large language models (LLMs) hold substantial promise for medical imaging interpretation. However, there is a lack of studies on their feasibility in handling reasoning questions associated with medical diagnosis. Purpose To investigate the viability of leveraging three publicly available LLMs to enhance consistency and diagnostic accuracy in medical imaging based on standardized reporting, with pathology as the reference standard. Materials and Methods US images of thyroid nodules with pathologic results were retrospectively collected from a tertiary referral hospital between July 2022 and December 2022 and used to evaluate malignancy diagnoses generated by three LLMs-OpenAI's ChatGPT 3.5, ChatGPT 4.0, and Google's Bard. Inter- and intra-LLM agreement of diagnosis were evaluated. Then, diagnostic performance, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), was evaluated and compared for the LLMs and three interactive approaches: human reader combined with LLMs, image-to-text model combined with LLMs, and an end-to-end convolutional neural network model. Results A total of 1161 US images of thyroid nodules (498 benign, 663 malignant) from 725 patients (mean age, 42.2 years ± 14.1 [SD]; 516 women) were evaluated. ChatGPT 4.0 and Bard displayed substantial to almost perfect intra-LLM agreement (κ range, 0.65-0.86 [95% CI: 0.64, 0.86]), while ChatGPT 3.5 showed fair to substantial agreement (κ range, 0.36-0.68 [95% CI: 0.36, 0.68]). ChatGPT 4.0 had an accuracy of 78%-86% (95% CI: 76%, 88%) and sensitivity of 86%-95% (95% CI: 83%, 96%), compared with 74%-86% (95% CI: 71%, 88%) and 74%-91% (95% CI: 71%, 93%), respectively, for Bard. Moreover, with ChatGPT 4.0, the image-to-text-LLM strategy exhibited an AUC (0.83 [95% CI: 0.80, 0.85]) and accuracy (84% [95% CI: 82%, 86%]) comparable to those of the human-LLM interaction strategy with two senior readers and one junior reader and exceeding those of the human-LLM interaction strategy with one junior reader. Conclusion LLMs, particularly integrated with image-to-text approaches, show potential in enhancing diagnostic medical imaging. ChatGPT 4.0 was optimal for consistency and diagnostic accuracy when compared with Bard and ChatGPT 3.5. © RSNA, 2024 Supplemental material is available for this article.

The clinical value of artificial intelligence in assisting junior radiologists in thyroid ultrasound: a multicenter prospective study from real clinical practice.

BACKGROUND: This study is to propose a clinically applicable 2-echelon (2e) diagnostic criteria for the analysis of thyroid nodules such that low-risk nodules are screened off while only suspicious or indeterminate ones are further examined by histopathology, and to explore whether artificial intelligence (AI) can provide precise assistance for clinical decision-making in the real-world prospective scenario. METHODS: In this prospective study, we enrolled 1036 patients with a total of 2296 thyroid nodules from three medical centers. The diagnostic performance of the AI system, radiologists with different levels of experience, and AI-assisted radiologists with different levels of experience in diagnosing thyroid nodules were evaluated against our proposed 2e diagnostic criteria, with the first being an arbitration committee consisting of 3 senior specialists and the second being cyto- or histopathology. RESULTS: According to the 2e diagnostic criteria, 1543 nodules were classified by the arbitration committee, and the benign and malignant nature of 753 nodules was determined by pathological examinations. Taking pathological results as the evaluation standard, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the AI systems were 0.826, 0.815, 0.821, and 0.821. For those cases where diagnosis by the Arbitration Committee were taken as the evaluation standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.946, 0.966, 0.964, and 0.956. Taking the global 2e diagnostic criteria as the gold standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.868, 0.934, 0.917, and 0.901, respectively. Under different criteria, AI was comparable to the diagnostic performance of senior radiologists and outperformed junior radiologists (all P < 0.05). Furthermore, AI assistance significantly improved the performance of junior radiologists in the diagnosis of thyroid nodules, and their diagnostic performance was comparable to that of senior radiologists when pathological results were taken as the gold standard (all p > 0.05). CONCLUSIONS: The proposed 2e diagnostic criteria are consistent with real-world clinical evaluations and affirm the applicability of the AI system. Under the 2e criteria, the diagnostic performance of the AI system is comparable to that of senior radiologists and significantly improves the diagnostic capabilities of junior radiologists. This has the potential to reduce unnecessary invasive diagnostic procedures in real-world clinical practice.

Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules: a prospective cohort study.

BACKGROUND: Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy. METHODS: This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes. RESULTS: The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies. CONCLUSIONS: This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.

Pathology and new insights in thyroid neoplasms in the 2022 WHO classification.

PURPOSE OF REVIEW: The assessment of thyroid nodules is a common clinical problem, linked to the high incidence of thyroid nodules in the population and the low incidence of aggressive thyroid carcinoma. The screening is therefore one of the strengths of our patient care. Recently, the 2023 Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) and 2022 WHO classification of thyroid neoplasms have been released based on the definition of new entities and the growing impact of molecular testing. The aim of this review is to analyze how these upgrades can help us in the daily routine practice diagnosis of thyroid cancer. RECENT FINDINGS: Our review is focused on the most frequent thyroid tumors derived from thyroid follicular cell. Fine needle aspiration (FNA) is the gold standard for the screening of thyroid nodules with very high levels of sensitivity and specificity. These sensitivity and specificity are improved by molecular testing, which refines the risk of malignancy. The 2023 TBSRTC integrates molecular data and the upgrades integrated in the 2022 WHO classification such as the 'low-risk neoplasms' and the 'high-grade follicular-cells derived carcinoma'. The morphological examination remains crucial since the capsular and/or vascular invasion are key features of malignancy in the follicular thyroid neoplasms. Low-risk neoplasms represent a clinical challenge since no specific guidelines are available. Challenges remain regarding oncocytic thyroid lesions, which are not associated with specific diagnostic molecular biomarkers. Molecular testing can help not only in deciphering the prognosis but also in the targeted therapeutic strategy. SUMMARY: While molecular testing has succeeded to substantially improve the pre and postoperative diagnosis and risk stratification of thyroid tumors, the morphological examination is still central in the daily routine diagnosis of thyroid pathology. Future is the integrated diagnosis of clinical, morphological, molecular and epigenetic features with the help of artificial intelligence algorithms.

Radioiodine versus radiofrequency ablation to treat autonomously functioning thyroid nodules: a systematic review and comparative meta-analysis.

PURPOSE: Radioiodine (RAI) is a well-established first-line therapy for autonomously functioning thyroid nodules (AFTN). Radiofrequency ablation (RFA) is a minimally invasive procedure that has been proposed as an alternative treatment option for hyperthyroidism caused by AFTN. Although RFA has been shown to be useful for reducing nodule volume and improving TSH levels in AFTN, no comprehensive comparative clinical studies have been proposed to evaluate the overall response to RFA treatment. The aim of this comparative systematic review and meta-analysis was to evaluate the response of RAI and RFA treatments in AFTN. METHODS: A systematic search strategy was applied in PubMed, Web of Science, Scopus, Cochrane Library, and ClinicalTrials.gov until July 2023 without time or language restrictions. Studies investigating the response to RAI and/or RFA treatment in AFTN patients 6 and/or 12 months after treatment were included. The risk of bias was assessed based on the study design. Random-effect models were used for the meta-analysis. RESULTS: Twenty-three articles (28 reports) met the inclusion criteria and were included in the study. Overall, RAI therapy was found to have a significantly higher treatment response (94%) than RFA (59%), although the volume of AFTNs was reduced to a similar extent. In the direct comparison (n = 3 studies), RFA showed a higher risk of non-response than RAI (RR, 1.24; 95% CI, 0.94-1.63; z = 1.55; p = 0.12). CONCLUSIONS: Our results demonstrate the superiority of RAI over RFA in terms of success rates and safety profile and confirm RAI as the first choice for the treatment of AFTNs.

Molecular testing for thyroid nodules: Where are we now?

Approximately 25% of the fine needle aspiration samples (FNAB) of thyroid nodules are classified as "indeterminate samples", that means, Bethesda III and IV categories. Until the last decade, most of these cases underwent diagnostic surgery, although only a minority (13-34%) confirmed malignancy postoperatively. In view of this, with the objective of improving the preoperative diagnosis in these cases, the molecular tests emerged, which are validated from the diagnostic point of view, presenting good performance, with good diagnostic accuracy, being able to avoid diagnostic surgeries. With the advancement of knowledge of the role of each of the mutations and gene rearrangements in thyroid oncogenesis, molecular markers have left to play only a diagnostic role and have been gaining more and more space both in defining the prognostic role of the tumor, as well as in the indication of target therapy. Thus, the objective of this review is to show how to use the tool of molecular tests, now commercially available in the world, in the management of indeterminate cytological nodules, assessing the pre-test malignancy risk of the nodule, through clinical, ultrasonographic and cytological characteristics, and decide on the benefit of molecular testing for each patient. In addition, to discuss its new and promising prognostic and therapeutic role in thyroid cancer.

Thyroid ultrasound and its ancillary techniques.

Ultrasound (US) of the thyroid has been used as a diagnostic tool since the late 1960s. US is the most important imaging tool for diagnosing thyroid disease. In the majority of cases a correct diagnosis can already be made in synopsis of the sonographic together with clinical findings and basal thyroid hormone parameters. However, the characterization of thyroid nodules by US remains challenging. The introduction of Thyroid Imaging Reporting and Data Systems (TIRADSs) has improved diagnostic accuracy of thyroid cancer significantly. Newer techniques such as elastography, superb microvascular imaging (SMI), contrast enhanced ultrasound (CEUS) and multiparametric ultrasound (MPUS) expand diagnostic options and tools further. In addition, the use of artificial intelligence (AI) is a promising tool to improve and simplify diagnostics of thyroid nodules and there is evidence that AI can exceed the performance of humans. Combining different US techniques with the introduction of new software, the use of AI, FNB as well as molecular markers might pave the way for a completely new area of diagnostic accuracy in thyroid disease. Finally, interventional ultrasound using US-guided thermal ablation (TA) procedures are increasingly proposed as therapy options for benign as well as malignant thyroid diseases.

Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review.

BACKGROUND: In the last years growing evidences on the role of radiomics and machine learning (ML) applied to different nuclear medicine imaging modalities for the assessment of thyroid diseases are starting to emerge. The aim of this systematic review was therefore to analyze the diagnostic performances of these technologies in this setting. METHODS: A wide literature search of the PubMed/MEDLINE, Scopus and Web of Science databases was made in order to find relevant published articles about the role of radiomics or ML on nuclear medicine imaging for the evaluation of different thyroid diseases. RESULTS: Seventeen studies were included in the systematic review. Radiomics and ML were applied for assessment of thyroid incidentalomas at 18 F-FDG PET, evaluation of cytologically indeterminate thyroid nodules, assessment of thyroid cancer and classification of thyroid diseases using nuclear medicine techniques. CONCLUSION: Despite some intrinsic limitations of radiomics and ML may have affect the results of this review, these technologies seem to have a promising role in the assessment of thyroid diseases. Validation of preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.

New insights in thyroid diagnosis and treatment.

The prevalence of thyroid disease continues to rise. As a consequence, the research in the thyroid field has significantly increased over time. Thus, clinicians, and endocrinologists first, have to be aware of the important continuous progress achieved, in particular of thyroid cancer, to better manage their patients. This themed issue, titled "New Insights in Thyroid Diagnosis and Treatment," delves deep into contemporary hot topics in thyroid field. These papers included in the present issue are focused on several aspects in this area, such as imaging, molecular analysis, machine learning and radiomics, nuclear medicine, clinical, and laboratory. Seven papers centers around thyroid cancer. Three papers review imaging modalities for thyroid nodule/cancer assessment. Two papers report a comprehensive review of metabolic issues involving thyroid gland. Finally, a large overview about genetics of Graves' disease is reported in another study. Clinicians will find this issue very interesting.

Protect the recurrent laryngeal nerves in US-guided microwave ablation of thyroid nodules at Zuckerkandl tubercle: a pilot study.

BACKGROUND: To evaluate the safety and efficacy of US-guided microwave ablation in patients with thyroid nodules at Zuckerkandl tubercle. METHODS: 103 consecutive patients with thyroid nodules at Zuckerkandl tubercle (ZTTN) were enrolled in this study from November 2017 to August 2021. Prior to the surgery or US-guided microwave ablation (MWA), preoperative ultrasound visualization of the recurrent laryngeal nerve (RLN) and ZTTN was performed, the size and the position relationship between them were observed. Patients were followed up at 1, 3, 6, and 12 months after MWA and the volume reduction rates (VRR) of the thyroid nodules were analyzed. RESULTS: All patients successfully had the RLN and ZTTN detected using ultrasound before surgery or ablation with a detection rate of 100%. For the 103 patients, the majority of ZTTN grades were categorized as grade 2, with the distance from the farthest outside of ZTTN to the outer edge of thyroid ranging between 6.0 and 10.0 mm. The position relationship between ZTTN and RLN was predominantly type A in 98 cases, with type D observed in 5 cases. After MWA, the median nodule volume had significantly decreased from 4.61 (2.34, 8.70) ml to 0.42 (0.15, 1.41) ml and the VRR achieved 84.36 ± 13.87% at 12 months. No nodules regrew throughout the 12-month follow-up period. Of the 11 patients experienced hoarseness due to RLN entrapment before ablation, 7 recovered immediately after separation of the RLN and ZTTN during MWA, 2 recovered after one week, and the other 2 recovered after two months. CONCLUSIONS: The RLN is closely related to ZTTN and mainly located at the back of ZTTN. The RLN can be separated from ZTTN by hydrodissection during MWA. US-guided MWA is a safe and effective treatment for ZTTN.

Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules.

BACKGROUND: The low specificity of Thyroid Imaging Reporting and Data System (TI-RADS) for preoperative benign-malignant diagnosis leads to a large number of unnecessary biopsies. This study developed and validated a predictive model based on MRI morphological features to improve the specificity. METHODS: A retrospective analysis was conducted on 825 thyroid nodules pathologically confirmed postoperatively. Univariate and multivariate logistic regression were used to obtain ß coefficients, construct predictive models and nomogram incorporating MRI morphological features in the training cohort, and validated in the validation cohort. The discrimination, calibration, and decision curve analysis of the nomogram were performed. The diagnosis efficacy, area under the curve (AUC) and net reclassification index (NRI) were calculated and compared with TI-RADS. RESULTS: 572 thyroid nodules were included (training cohort: n = 397, validation cohort: n = 175). Age, low signal intensity on T2WI, restricted diffusion, reversed halo sign in delay phase, cystic degeneration and wash-out pattern were independent predictors of malignancy. The nomogram demonstrated good discrimination and calibration both in the training cohort (AUC = 0.972) and the validation cohort (AUC = 0.968). The accuracy, sensitivity, specificity, PPV, NPV and AUC of MRI-based prediction were 94.4%, 96.0%, 93.4%, 89.9%, 96.5% and 0.947, respectively. The MRI-based prediction model exhibited enhanced accuracy (NRI>0) in comparison to TI-RADSs. CONCLUSIONS: The prediction model for diagnosis of benign and malignant thyroid nodules demonstrated a more notable diagnostic efficacy than TI-RADS. Compared with the TI-RADSs, predictive model had better specificity along with a high sensitivity and can reduce overdiagnosis and unnecessary biopsies.

Imprinted gene detection effectively improves the diagnostic accuracy for papillary thyroid carcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the most frequent histological type of thyroid carcinoma. Although an increasing number of diagnostic methods have recently been developed, the diagnosis of a few nodules is still unsatisfactory. Therefore, the present study aimed to develop and validate a comprehensive prediction model to optimize the diagnosis of PTC. METHODS: A total of 152 thyroid nodules that were evaluated by postoperative pathological examination were included in the development and validation cohorts recruited from two centres between August 2019 and February 2022. Patient data, including general information, cytopathology, imprinted gene detection, and ultrasound features, were obtained to establish a prediction model for PTC. Multivariate logistic regression analysis with a bidirectional elimination approach was performed to identify the predictors and develop the model. RESULTS: A comprehensive prediction model with predictors, such as component, microcalcification, imprinted gene detection, and cytopathology, was developed. The area under the curve (AUC), sensitivity, specificity, and accuracy of the developed model were 0.98, 97.0%, 89.5%, and 94.4%, respectively. The prediction model also showed satisfactory performance in both internal and external validations. Moreover, the novel method (imprinted gene detection) was demonstrated to play a role in improving the diagnosis of PTC. CONCLUSION: The present study developed and validated a comprehensive prediction model for PTC, and a visualized nomogram based on the prediction model was provided for clinical application. The prediction model with imprinted gene detection effectively improves the diagnosis of PTCs that are undetermined by the current means.

Relationship Between Urinary Iodine Concentration and the Prevalence of Thyroid Nodules and Subclinical Hypothyroidism.

The aim of the study was to investigate the iodine intake in the resident population in Xi'an and analyze the relationship between iodine nutritional status and the prevalence of subclinical hypothyroidism and thyroid nodules (TNs). A total of 2507 people were enrolled in Xi'an. Venous serum thyroid stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TgAb), urinary iodine concentration (UIC), and thyroid ultrasonography were collected. Patients with abnormal TSH were checked for free thyroxine (FT4) and triiodothyronine (FT3). Adults in Xi'an had median UICs of 220.80 µg/L and 178.56 µg/l, respectively. A sum of 16.78% of people had subclinical hypothyroidism. Both iodine excess and iodine deficit increased the frequency of subclinical hypothyroidism. The lowest was around 15.09% in females with urine iodine levels between 200 and 299 µg/l. With a rate of 10.69%, the lowest prevalence range for males was 100-199 µg/l. In Xi'an, 11.37% of people have TNs. In comparison to other UIC categories, TN occurrences were higher in females (18.5%) and males (12%) when UIC were below 100 µg/l. In conclusion, iodine intake was sufficient in the Xi'an area, while the adults' UIC remains slightly higher than the criteria. Iodine excess or deficiency can lead to an increase in the prevalence of subclinical hypothyroidism. Patients with iodine deficiency are more likely to develop TNs.

US of thyroid nodules: can AI-assisted diagnostic system compete with fine needle aspiration?

OBJECTIVES: Artificial intelligence (AI) systems can diagnose thyroid nodules with similar or better performance than radiologists. Little is known about how this performance compares with that achieved through fine needle aspiration (FNA). This study aims to compare the diagnostic yields of FNA cytopathology alone and combined with BRAFV600E mutation analysis and an AI diagnostic system. METHODS: The ultrasound images of 637 thyroid nodules were collected in three hospitals. The diagnostic efficacies of an AI diagnostic system, FNA-based cytopathology, and BRAFV600E mutation analysis were evaluated in terms of sensitivity, specificity, accuracy, and the κ coefficient with respect to the gold standard, defined by postsurgical pathology and consistent benign outcomes from two combined FNA and mutation analysis examinations performed with a half-year interval. RESULTS: The malignancy threshold for the AI system was selected according to the Youden index from a retrospective cohort of 346 nodules and then applied to a prospective cohort of 291 nodules. The combination of FNA cytopathology according to the Bethesda criteria and BRAFV600E mutation analysis showed no significant difference from the AI system in terms of accuracy for either cohort in our multicenter study. In addition, for 45 included indeterminate Bethesda category III and IV nodules, the accuracy, sensitivity, and specificity of the AI system were 84.44%, 95.45%, and 73.91%, respectively. CONCLUSIONS: The AI diagnostic system showed similar diagnostic performance to FNA cytopathology combined with BRAFV600E mutation analysis. Given its advantages in terms of operability, time efficiency, non-invasiveness, and the wide availability of ultrasonography, it provides a new alternative for thyroid nodule diagnosis. CLINICAL RELEVANCE STATEMENT: Thyroid ultrasonic artificial intelligence shows statistically equivalent performance for thyroid nodule diagnosis to FNA cytopathology combined with BRAFV600E mutation analysis. It can be widely applied in hospitals and clinics to assist radiologists in thyroid nodule screening and is expected to reduce the need for relatively invasive FNA biopsies. KEY POINTS: • In a retrospective cohort of 346 nodules, the evaluated artificial intelligence (AI) system did not significantly differ from fine needle aspiration (FNA) cytopathology alone and combined with gene mutation analysis in accuracy. • In a prospective multicenter cohort of 291 nodules, the accuracy of the AI diagnostic system was not significantly different from that of FNA cytopathology either alone or combined with gene mutation analysis. • For 45 indeterminate Bethesda category III and IV nodules, the AI system did not perform significantly differently from BRAFV600E mutation analysis.

Volume reduction rate of radiofrequency ablation in ≤ 2 cm Bethesda IV thyroid nodules.

OBJECTIVE: This prospective observational study aimed to evaluate the efficacy of radiofrequency ablation (RFA) in treating ≤ 2 cm thyroid nodules with Bethesda IV cytology and C-TIRADS 4A categorization. Additionally, the factors influencing the completed absorption of ablation (CAA) were examined. METHODS: A total of 62 cases with 62 nodules underwent ultrasound-guided RFA and were included in the study. The volume reduction rate (VRR), CAA, and incomplete absorption of ablation (IAA) were assessed at the 1st, 3rd, 6th, and subsequent 6-month follow-ups. Clinical and ultrasound features were compared between the CAA and IAA groups at the 12th month follow-up. RESULTS: The average VRR at the 1st, 3rd, 6th, 12th month, and last follow-up were -88.6%, 16.0%, 59.7%, 82.0%, and 98.2%, respectively. More than half of the nodules achieved a 90% VRR after 1 year of RFA, with 88.7% demonstrating CAA at the end of the study (follow-up duration of 14 to 63 months). Nodules with grade 3 vascularity and those associated with chronic thyroiditis showed delayed CAA at the 12th month follow-up (p = 0.036 and 0.003, respectively). CONCLUSION: RFA is an effective technique for treating ≤ 2 cm thyroid nodules with Bethesda IV cytology and C-TIRADS 4A categorization. Nodules with grade 3 blood supply and patients with chronic thyroiditis exhibited an impact on the completed absorption following RFA. CLINICAL RELEVANCE STATEMENT: Our study has shown that radiofrequency ablation is an effective treatment for ≤ 2 cm thyroid nodules classified as Bethesda IV cytology. However, we identified that high vascularity of the nodule and chronic thyroiditis are adverse factors affecting the completed absorption of the ablation. KEY POINTS: •Radiofrequency ablation (RFA) is an effective technique for treatment of ≤ 2 cm Bethesda IV category thyroid nodules. •Higher blood supply and chronic thyroiditis influence the completed absorption after RFA.

Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.

OBJECTIVES: This study aimed to propose a deep learning (DL)-based framework for identifying the composition of thyroid nodules and assessing their malignancy risk. METHODS: We conducted a retrospective multicenter study using ultrasound images from four hospitals. Convolutional neural network (CNN) models were constructed to classify ultrasound images of thyroid nodules into solid and non-solid, as well as benign and malignant. A total of 11,201 images of 6784 nodules were used for training, validation, and testing. The area under the receiver-operating characteristic curve (AUC) was employed as the primary evaluation index. RESULTS: The models had AUCs higher than 0.91 in the benign and malignant grading of solid thyroid nodules, with the Inception-ResNet AUC being the highest at 0.94. In the test set, the best algorithm for identifying benign and malignant thyroid nodules had a sensitivity of 0.88, and a specificity of 0.86. In the human vs. DL test set, the best algorithm had a sensitivity of 0.93, and a specificity of 0.86. The Inception-ResNet model performed better than the senior physicians (p < 0.001). The sensitivity and specificity of the optimal model based on the external test set were 0.90 and 0.75, respectively. CONCLUSIONS: This research demonstrates that CNNs can assist thyroid nodule diagnosis and reduce the rate of unnecessary fine-needle aspiration (FNA). CLINICAL RELEVANCE STATEMENT: High-resolution ultrasound has led to increased detection of thyroid nodules. This results in unnecessary fine-needle aspiration and anxiety for patients whose nodules are benign. Deep learning can solve these problems to some extent. KEY POINTS: • Thyroid solid nodules have a high probability of malignancy. • Our models can improve the differentiation between benign and malignant solid thyroid nodules. • The differential performance of one model was superior to that of senior radiologists. Applying this could reduce the rate of unnecessary fine-needle aspiration of solid thyroid nodules.

Efficacy of microwave ablation in the treatment of large benign thyroid nodules: a multi-center study.

OBJECTIVE: To evaluate the efficacy, safety, and improvement of symptoms by ultrasound-guided microwave ablation (MWA) for patients with large benign thyroid nodules (BTNs). METHODS: Eighty-seven patients with 87 BTNs (≥ 4 cm) treated with MWA between April 2015 and March 2021 were enrolled in this retrospective multicenter study, with clinical and ultrasound examinations performed at the 1st, 3rd, 6th, and 12th months. A multivariable linear mixed effects model was employed to explore the alterations in volume and volume reduction ratio (VRR), as well as the potential factors associated with VRR. RESULTS: The mean age of the 87 patients was 45.69 ± 14.21 years (range 18-76 years), and the ratio of men to women was 1:4.8. The mean volumes were much decreased at the 12th month after ablation compared to the initial volumes (p < .001). The mean VRR was 76.09% at the 12th month. The technique efficacy (VRR > 50%) was 90.80% at the 12th month. A multivariate analysis revealed that VRR was related to the initial volume (p = .015), annular flow (p = .010), and nodule composition (p = .024). The mean symptomatic score decreased from 4.40 ± 0.28 to 0.26 ± 0.06 at the 12th month (p < .001). At the same time, the mean cosmetic score decreased from 3.22 ± 0.10 to 1.31 ± 0.08 (p < .001). CONCLUSION: MWA could serve as a safe and effective therapy for large BTNs, significantly reducing the volume of BTNs and significantly improving compressive symptoms and appearance problems. CLINICAL RELEVANCE STATEMENT: Microwave ablation could serve as a safe and effective therapy for large benign thyroid nodules, leading to significant volume reduction and satisfied symptom and cosmetic alleviation period. KEY POINTS: • This multicenter study investigated the feasibility and safety of microwave ablation for large benign thyroid nodules. • After ablation, the nodule volume was significantly reduced, and patients' symptoms and appearance problems were significantly improved. • Microwave ablation is feasible for large benign thyroid nodules and has been a supplement treatment.