[Expert consensus on ultrasound guided thyroid and neck lymph node puncture (2023 edition)].

Thyroid nodule and cervical lymph node biopsy is the main clinical method for evaluating the condition and determining the follow-up treatment plan. The literature on thyroid nodule puncture predominantly focuses on thyroid fine needle puncture, and there are limited systematic articles on coarse needle aspiration for thyroid-related diseases and needle biopsy of thyroid-related cervical lymph node diseases. However, this shortage of articles does not reflect the diagnostic value of coarse needle aspiration in thyroid biopsy and cervical lymph node-related diseases. Currently, different departments of many hospitals in China are conducting or planning to perform needle biopsy of thyroid and cervical lymph node-related diseases to improve the standardization and safety of related operations. Standardization is needed for the indications, contraindications, perioperative period, postoperative complications management, puncture specimen processing, and related genetic analysis of thyroid and cervical lymph node puncture. For this purpose, Interventional Ultrasound Committee of Chinese College of Interventionalists organized a panel of domestic experts in the field of thyroid diseases to discuss and formulate a consensus. Based on the latest research progress, combined with the clinical realities in China, this Expert Consensus on Ultrasound Guided Thyroid and Neck Lymph Node Puncture (2023 edition) is released.

Diagnostic value of combined ultrasound contrast and elastography for differentiating benign and malignant thyroid nodules: a meta-analysis.

The diagnostic value of contrast-enhanced ultrasound combined with ultrasound elastography for benign and malignant thyroid nodules is still controversial, so we used meta-analysis to seek controversial answers. The PubMed, OVID, and CNKI databases were searched according to the inclusion and exclusion criteria. The literature was selected from the establishment of each database to February 2024. The QUADAS-2 tool assessed diagnostic test accuracy. SROC curves and Spearman's correlation coefficient were made by Review Manager 5.4 software to assess the presence of threshold effects in the literature. Meta-Disc1.4 software was used for Cochrane-Q and χ2 tests, which be used to evaluate heterogeneity, with P-values and I2 indicating heterogeneity levels. The appropriate effect model was selected based on the results of the heterogeneity test. Stata18.0 software was used to evaluate publication bias. The diagnostic accuracy of contrast-enhanced ultrasound combined with ultrasound elastography for benign and malignant thyroid nodules was evaluated by calculating the combined sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, DOR, and area under the SROC curve. A total of 31 studies included 3811 patients with 4718 nodules were analyzed. There is no heterogeneity caused by the threshold effect, but there is significant non-threshold heterogeneity. Combined diagnostic metrics were: sensitivity = 0.93, specificity = 0.91, DOR = 168.41, positive likelihood ratio = 10.60, and negative likelihood ratio = 0.07. The SROC curve area was 0.97. Contrast-enhanced ultrasound and elastography show high diagnostic accuracy for thyroid nodules, offering a solid foundation for early diagnosis and treatment.Trial registration. CRD42024509462.

Differential diagnosis of thyroid nodules using heterogeneity quantification software on ultrasound images: correlation with the Bethesda system and surgical pathology.

Ultrasonography (US)-guided fine-needle aspiration cytology (FNAC) is the primary modality for evaluating thyroid nodules. However, in cases of atypia of undetermined significance (AUS) or follicular lesion of undetermined significance (FLUS), supplemental tests are necessary for a definitive diagnosis. Accordingly, we aimed to develop a non-invasive quantification software using the heterogeneity scores of thyroid nodules. This cross-sectional study retrospectively enrolled 188 patients who were categorized into four groups according to their diagnostic classification in the Bethesda system and surgical pathology [II-benign (B) (n = 24); III-B (n = 52); III-malignant (M) (n = 54); V/VI-M (n = 58)]. Heterogeneity scores were derived using an image pixel-based heterogeneity index, utilized as a coefficient of variation (CV) value, and analyzed across all US images. Differences in heterogeneity scores were compared using one-way analysis of variance with Tukey's test. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristic (AUROC) curve. The results of this study indicated significant differences in mean heterogeneity scores between benign and malignant thyroid nodules, except in the comparison between III-M and V/VI-M nodules. Among malignant nodules, the Bethesda classification was not observed to be associated with mean heterogeneity scores. Moreover, there was a positive correlation between heterogeneity scores and the combined diagnostic category, which was based on the Bethesda system and surgical cytology grades (R = 0.639, p < 0.001). AUROC for heterogeneity scores showed the highest diagnostic performance (0.818; cut-off: 30.22% CV value) for differentiating the benign group (normal/II-B/III-B) from the malignant group (III-M/V&VI-M), with a diagnostic accuracy of 72.5% (161/122). Quantitative heterogeneity measurement of US images is a valuable non-invasive diagnostic tool for predicting the likelihood of malignancy in thyroid nodules, including AUS or FLUS.

A fully autonomous robotic ultrasound system for thyroid scanning.

The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nod- ules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets. The orientation of the ultrasound probe is adjusted dynamically via Bayesian optimization. Experimental results on human participants demonstrated that this system can perform high-quality ultrasound scans, close to manual scans obtained by clinicians. Additionally, it has the potential to detect thyroid nodules and provide data on nodule characteristics for American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) calculation.

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography.

Introduction: Ultrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes. However, there is a lack of clinical guidelines for the judicious use of thyroid ultrasonography in routine screening. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to leverage the ML approach in assessing the risk of thyroid nodules based on common clinical features. Methods: Data were sourced from a Chinese cohort undergoing routine physical examinations including thyroid ultrasonography between 2013 and 2023. Models were established to predict the 3-year risk of thyroid nodules based on patients' baseline characteristics and laboratory tests. Four ML algorithms, including logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine, were trained and tested using fivefold cross-validation. The importance of each feature was measured by the permutation score. A nomogram was established to facilitate risk assessment in the clinical settings. Results: The final dataset comprised 4,386 eligible subjects. Thyroid nodules were detected in 54.8% (n=2,404) individuals within the 3-year observation period. All ML models significantly outperformed the baseline regression model, successfully predicting the occurrence of thyroid nodules in approximately two-thirds of individuals. Age, high-density lipoprotein, fasting blood glucose and creatinine levels exhibited the highest impact on the outcome in these models. The nomogram showed consistency and validity, providing greater net benefits for clinical decision-making than other strategies. Conclusion: This study demonstrates the viability of an ML-based approach in predicting the occurrence of thyroid nodules. The findings highlight the potential of ML models in identifying high-risk individuals for personalized screening, thereby guiding the judicious use of ultrasound in this context.

Assessing the relative effectiveness of various ultrasound-guided ablation techniques for treating benign thyroid nodules: A systematic review and network meta-analysis.

BACKGROUND: Benign thyroid nodules (BTNs) represent a prevalent clinical challenge globally, with various ultrasound-guided ablation techniques developed for their management. Despite the availability of these methods, a comprehensive evaluation to identify the most effective technique remains absent. This study endeavors to bridge this knowledge gap through a network meta-analysis (NMA), aiming to enhance the understanding of the comparative effectiveness of different ultrasound-guided ablation methods in treating BTNs. METHODS: We comprehensively searched PubMed, Embase, Cochrane, Web of Science, Ovid, SCOPUS, and ProQuest for studies involving 16 ablation methods, control groups, and head-to-head trials. NMA was utilized to evaluate methods based on the percentage change in nodule volume, symptom score, and cosmetic score. This study is registered in INPLASY (registration number 202260061). RESULTS: Among 35 eligible studies involving 5655 patients, NMA indicated that RFA2 (radiofrequency ablation, 2 sessions) exhibited the best outcomes at 6 months for percentage change in BTN volume (SUCRA value 74.6), closely followed by RFA (SUCRA value 73.7). At 12 months, RFA was identified as the most effective (SUCRA value 81.3). Subgroup analysis showed RFA2 as the most effective for solid nodule volume reduction at 6 months (SUCRA value 75.6), and polidocanol ablation for cystic nodules (SUCRA value 66.5). CONCLUSION: Various ablation methods are effective in treating BTNs, with RFA showing notable advantages. RFA with 2 sessions is particularly optimal for solid BTNs, while polidocanol ablation stands out for cystic nodules.

Thyroid abnormalities identified on CT screening for lung cancer.

PURPOSE: Because incidental thyroid nodules (ITNs) are common extrapulmonary findings in low-dose computed tomography (LDCT) scans for lung cancer screening, we aimed to investigate the frequency of ITNs on LDCT scans separately on baseline and annual repeat scans, the frequency of malignancy among the ITNs, and any association with demographic, clinical, CT characteristics. METHODS: Retrospective case series of all 2309 participants having baseline and annual repeat screening in an Early Lung and Cardiac Action Program (MS-ELCAP) LDCT lung screening program from January 2010 to December 2016 was performed. Frequency of ITNs in baseline and annual repeat rounds were determined. Multivariable regression analysis was performed to identify significant predictors. RESULTS: Dominant ITNs were seen in 2.5 % of 2309 participants on baseline and in 0.15 % of participants among 4792 annual repeat LDCTs. The low incidence of new ITNs suggests slow growth as it would take approximately an average of 16.8 years for a new ITN to be detected on annual rounds of screening. Newly detected ITNs on annual repeat LDCT were all smaller than 15 mm. Regression analysis showed that the increasing of age, coronary artery calcifications score and breast density grade were significant predictors for females having an ITN. No significant predictors were found for ITNs in males. CONCLUSION: ITNs are detected at LDCT however, no malignancy was found. Certain predictors for ITNs in females have been identified including breast density, which may point towards a common causal pathway.

Diagnostic Accuracy of Ultrasound and Fine-Needle Aspiration Cytology in Thyroid Malignancy.

Introduction: Thyroid nodule incidence is increasing due to the widespread application of ultrasonography. Fine-needle aspiration cytology is widely applied for the detection of malignancies. The aim of this study was to evaluate the predictive value of ultrasonography in thyroid cancer. Methods: This retrospective study included patients that underwent total thyroidectomy for benign thyroid disease or well-differentiated thyroid carcinoma from January 2017 to December 2022. The study population was divided into groups: the well-differentiated thyroid cancer group and the control group with benign histopathological reports. Results: In total, 192 patients were enrolled in our study; 159 patients were included in the well-differentiated thyroid cancer group and 33 patients in the control group. Statistical analysis demonstrated that ultrasonographic findings such as microcalcifications (90.4%), hypoechogenicity (89.3%), irregular margins (92.2%) and taller-than-wide shape (90.5%) were correlated to malignancy (p < 0.001). Uni- and multivariate analysis revealed that both US score (OR: 2.177; p < 0.001) and Bethesda System (OR: 1.875; p = 0.002) could predict malignancies. In terms of diagnostic accuracy, the US score displayed higher sensitivity (64.2% vs. 33.3%) and better negative predictive value (34.5% vs. 24.4%) than the Bethesda score, while both scoring systems displayed comparable specificities (90.9% vs. 100%) and positive predictive values (97.1% vs. 100%). Discussion: The malignant potential of thyroid nodules is a crucial subject, leading the decision for surgery. Ultrasonography and fine-needle aspiration cytology are pivotal examinations in the diagnostic process, with ultrasonography demonstrating better negative predictive value.

Super-resolution based Nodule Localization in Thyroid Ultrasound Images through Deep Learning.

BACKGROUND: Currently, it is difficult to find a solution to the inverse inappropriate problem, which involves restoring a high-resolution image from a lowresolution image contained within a single image. In nature photography, one can capture a wide variety of objects and textures, each with its own characteristics, most notably the high-frequency component. These qualities can be distinguished from each other by looking at the pictures. OBJECTIVE: The goal is to develop an automated approach to identify thyroid nodules on ultrasound images. The aim of this research is to accurately differentiate thyroid nodules using Deep Learning Technique and to evaluate the effectiveness of different localization techniques. METHODS: The method used in this research is to reconstruct a single super-resolution image based on segmentation and classification. The poor-quality ultrasound image is divided into several parts, and the best applicable classification is chosen for each component. Pairs of high- and lowresolution images belonging to the same class are found and used to figure out which image is high-resolution for each segment. Deep learning technology, specifically the Adam classifier, is used to identify carcinoid tumors within thyroid nodules. Measures, such as localization accuracy, sensitivity, specificity, dice loss, ROC, and area under the curve (AUC), are used to evaluate the effectiveness of the techniques. RESULTS: The results of the proposed method are superior, both statistically and qualitatively, compared to other methods that are considered one of the latest and best technologies. The developed automated approach shows promising results in accurately identifying thyroid nodules on ultrasound images. CONCLUSION: The research demonstrates the development of an automated approach to identify thyroid nodules within ultrasound images using super-resolution single-image reconstruction and deep learning technology. The results indicate that the proposed method is superior to the latest and best techniques in terms of accuracy and quality. This research contributes to the advancement of medical imaging and holds the potential to improve the diagnosis and treatment of thyroid nodules.

Diagnostic Efficiency of ACR-TIRADS Score for Differentiating Benign and Malignant Thyroid Nodules of Various Pathological Types.

BACKGROUND Thyroid nodule prevalence reaches 65% in the general population. Hence, appropriate ultrasonic examination is key in disease monitoring and management. We investigated the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) score for diagnosis of benign and malignant thyroid nodules and pathological types. MATERIAL AND METHODS A retrospective study was conducted. According to ultrasound images, ultrasonic characteristics of benign and malignant thyroid nodules and different pathological types were analyzed using ACR-TIRADS score, and diagnostic value was determined. AUCs were compared for tumor diagnosis and differentiation. RESULTS Overall, 1675 thyroid nodules from 1614 patients were included. AUC value of papillary thyroid carcinoma (PTC) diagnosed with ACR-TIRADS was highest (0.955 [95% CI=0.946-0.965]), while that of follicular thyroid carcinoma (FTC) was lowest (0.877 [95% CI=0.843-0.912]). FTC had the highest sensitivity (95.1%) and lowest specificity (64.8%). When the cut-off value was 5.5 points, accuracy of diagnosing PTC and anaplastic thyroid carcinoma (ATC) was highest, 80.5% and 78.7% respectively. Comparison of the multi-index prediction model constructed by multivariable logistic regression analysis and prediction model constructed by ACR-TIRADS score showed, when evaluating PTC and ATC, the multi-index model was better: AUCs of PTC were 0.966 vs 0.955, and AUCs of ATC were 0.982 vs 0.952, respectively, (P<0.05). CONCLUSIONS ACR-TIRADS score-based ultrasound examination of thyroid nodules aids diagnosis of benign and malignant thyroid nodules. TIRADS criteria favor diagnosis of PTC (and ATC) over FTC. ACR-TIRADS score can help clinicians diagnose thyroid nodules quickly and earlier, exhibits good clinical value, and can prevent missed diagnoses.

Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: A comprehensive study.

BACKGROUND AND OBJECTIVE: The thyroid gland, a key component of the endocrine system, is pivotal in regulating bodily functions. Thermography, a non-invasive imaging technique utilizing infrared cameras, has emerged as a diagnostic tool for thyroid-related conditions, offering advantages such as early detection and risk stratification. Artificial intelligence (AI) has demonstrated success in medical diagnostics, and its integration into thermal imaging analysis holds promise for improving diagnostic capabilities. This study aims to explore the potential of AI, specifically convolutional neural networks (CNNs), in enhancing the analysis of thyroid thermograms for the detection of nodules and abnormalities. METHODS: Artificial intelligence (AI) and machine learning techniques are integrated to enhance thyroid thermal image analysis. Specifically, a fusion of U-Net and VGG16, combined with feature engineering (FE), is proposed for accurate thyroid nodule segmentation. The novelty of this research lies in leveraging feature engineering in transfer learning for the segmentation of thyroid nodules, even in the presence of a limited dataset. RESULTS: The study presents results from four conducted studies, demonstrating the efficacy of this approach even with a limited dataset. It's observed that in study 4, using FE has led to a significant improvement in the value of the dice coefficient. Even for the small size of the masked region, incorporating radiomics with FE resulted in significant improvements in the segmentation dice coefficient. It's promising that one can achieve higher dice coefficients by employing different models and refining them. CONCLUSION: The findings here underscore the potential of AI for precise and efficient segmentation of thyroid nodules, paving the way for improved thyroid health assessment.

Thyroid US-guided FNA techniques: A prospective,randomized controlled study.

BACKGROUND: Thyroid nodules are common in the general population. Ultrasonography is the most efficient diagnostic approach to evaluate thyroid nodules. The US FNAC procedure can be performed using either the short axis (perpendicular), or a long axis (parallel) approach to visualize the needle as it is advanced toward the desired nodule. The main aim of this study was to compare the percentage of non-diagnostic results between the long and short axis approach. METHODS: A prospective study that included a randomized controlled trial and was divided into two arms-the short axis and the long axis-was conducted. A total of 245 thyroid nodules were collected through the fine needle aspiration cytology, performed with ultrasound, from march 2021 to march 2022. The patient's demographic information were collected and also nodules characteristics. RESULTS: Of 245 nodules sampled, 122 were sampled with the long axis method, while 123 with the short axis method. There is not significantly less non diagnostic approach with either method compared to the other (11.5 % vs 16.3 % respectively). DISCUSSION: Previous studies came to the conclusion that the long axis method yields fewer non diagnostic samples. This study evaluated the two FNA approaches which were proceeded by the same physician who is expert in both techniques. CONCLUSION: The US FNAC performed in the long axis approach will not produce more conclusive results and less non diagnostic results (Bethesda category 1) than the short axis approach one.

The application value of deep learning-based nomograms in benign-malignant discrimination of TI-RADS category 4 thyroid nodules.

Thyroid nodules are a common occurrence, and although most are non-cancerous, some can be malignant. The American College of Radiology has developed the Thyroid Imaging Reporting and Data System (TI-RADS) to standardize the interpretation and reporting of thyroid ultrasound results. Within TI-RADS, a category 4 designation signifies a thyroid nodule with an intermediate level of suspicion for malignancy. Accurate classification of these nodules is crucial for proper management, as it can potentially reduce unnecessary surgeries and improve patient outcomes. This study utilized deep learning techniques to effectively classify TI-RADS category 4 thyroid nodules as either benign or malignant. A total of 500 patients were included in the study and randomly divided into a training group (350 patients) and a test group (150 patients). The YOLOv3 model was constructed and evaluated using various metrics, achieving an 84% accuracy in the classification of TI-RADS category 4 thyroid nodules. Based on the predictions of the model, along with clinical and ultrasound data, a nomogram was developed. The performance of the nomogram was superior in both the training and testing groups. Furthermore, the calibration curve demonstrated good agreement between predicted probabilities and actual outcomes. Decision curve analysis further confirmed that the nomogram provided greater net benefits. Ultimately, the YOLOv3 model and nomogram successfully improved the accuracy of distinguishing between benign and malignant TI-RADS category 4 thyroid nodules, which is crucial for proper management and improved patient outcomes.

Predictive values of ultrasound characters associated with malignant thyroid nodules in Yaoundé: a cross-sectional study.

Introduction: most ultrasound criteria are defined in developed countries and commonly used in practice to assess the malignancy risk of thyroid nodules. This practice does not take into consideration some aspects of our context as delay of consultation and insufficient iodine intake. The objective of this study was to determine the predictive values of ultrasound characters associated with malignant thyroid nodules in our environment. Methods: we conducted a cross-sectional, prospective, and analytical study in three hospitals in Yaoundé over a six-month period in 2022. Our sample consisted of thyroid nodules with ultrasound, cytopathological, and histopathological data. The ultrasound characters and histology status of category III thyroid nodules and higher in Bethesda score were analysed in univariate and multivariate statistics to determine their predictive values. Results: eighty-nine nodules were obtained according to our inclusion criteria. The sex ratio was 0.46 and the average age of the patients was 46 years (IQR=42-59). The cancer prevalence in our sample was 22.47%. On ultrasound assessment, the characters associated to malignant histology (p<0.05) were nodules count, echogenicity, echostructure, presence or absence of microcalcifications, margins, and type of vascularization. Positive predictive values ranged from 26.15 to 57.14%, while negative predictive values ranged from 12.5 to 33.3%. Conclusion: taken alone, the ultrasound characters of suspected thyroid nodules have poor predictive values. There was a high variability in sensitivity but that was generally good (60-95%) while specificity was low. The prediction of malignant thyroid nodules is correlated with the association of at least two ultrasound criteria supported by clinical arguments.

Long-Term Follow-up of Patients With Autonomous Thyroid Nodules Treated With Radioiodine.

PURPOSE: This article aims to describe the presentation of Plummer disease and its evolution after radioiodine treatment and determine factors that may influence treatment efficacy. PATIENTS AND METHODS: The sample included retrospective medical records of 165 adult patients with toxic nodular goiter treated with radioiodine between 1997 and 2017, followed up at a single thyroid center. RESULTS: The efficacy of treatment with a single dose of radioiodine was higher than 90%. The mean radioiodine activity was 28.9 ± 3.4 mCi. The mean time between radioiodine performance and hyperthyroidism resolution was 3.6 ± 3.0 months, ranging from 1-12 months. After the first year, 33.9% of the patients were under hypothyroidism, 59.4% under euthyroidism, and 6.7% under hyperthyroidism. Among the nonresponders, the variables that showed statistical difference were the presence of multinodular goiter and the radioiodine activity (mean, 25.5 ± 6.5 mCi; median, 30 [15-30 mCi]). The cumulative rate of hypothyroidism was 48.9% over 20 years of follow-up. CONCLUSIONS: Radioiodine therapy is an effective and safe treatment. In Plummer disease, high rates of euthyroidism are expected after the radioiodine treatment. Therapeutic failure was observed mainly in patients with larger multinodular goiters treated with lower doses of radioiodine. The evolution to hypothyroidism was mostly observed in younger patients with larger and uninodular goiters.

Comparison of ultrasound-guided microwave ablation, laser ablation, and radiofrequency ablation for the treatment of elderly patients with benign thyroid nodules: A meta-analysis.

BACKGROUND: A new minimally invasive technique, ultrasound-guided thermal ablation has become one of the treatment methods for benign thyroid nodules. This study aims to evaluate the efficacy and safety of laser ablation (LA), radiofrequency ablation (RFA), and microwave ablation (MWA) in the treatment of elderly patients with benign thyroid nodules. METHODS: PubMed, Web of Science, and Cochrane Library were searched for qualified randomized controlled studies (RCTs) issued from establishing databases to March 2022. After screening and evaluating the article quality, the data on nodular volume reduction rate (VRR) and the incidence of complications after thermal ablation were extracted and analyzed by RevMan 5.3 and Stata l4.0. RESULTS: The meta-analysis included seven articles with 3055 participants. We found that LA, RFA, and MWA could markedly reduce the volume of benign thyroid nodules. LA was superior to RFA and MWA in reducing the volume of benign thyroid nodules in 6 months of follow-up (all P < 0.05). LA, RFA, and MWA can be safely implemented in patients with benign thyroid nodules. The incidence of significant complications after the RFA group was enhanced compared with that in the MWA (P < 0.05), and the incidence of secondary complications after RFA was slightly higher than that of LA (P < 0.05). CONCLUSION: LA, RFA, and MWA can markedly reduce the volume of benign thyroid nodules in elderly patients and can safely treat benign thyroid nodules.

Transforming free-text radiology reports into structured reports using ChatGPT: A study on thyroid ultrasonography.

PURPOSE: The importance of structured radiology reports has been fully recognized, as they facilitate efficient data extraction and promote collaboration among healthcare professionals. Our purpose is to assess the accuracy and reproducibility of ChatGPT, a large language model, in generating structured thyroid ultrasound reports. METHODS: This is a retrospective study that includes 184 nodules in 136 thyroid ultrasound reports from 136 patients. ChatGPT-3.5 and ChatGPT-4.0 were used to structure the reports based on ACR-TIRADS guidelines. Two radiologists evaluated the responses for quality, nodule categorization accuracy, and management recommendations. Each text was submitted twice to assess the consistency of the nodule classification and management recommendations. RESULTS: On 136 ultrasound reports from 136 patients (mean age, 52 years ± 12 [SD]; 61 male), ChatGPT-3.5 generated 202 satisfactory structured reports, while ChatGPT-4.0 only produced 69 satisfactory structured reports (74.3 % vs. 25.4 %, odds ratio (OR) = 8.490, 95 %CI: 5.775-12.481, p < 0.001). ChatGPT-4.0 outperformed ChatGPT-3.5 in categorizing thyroid nodules, with an accuracy of 69.3 % compared to 34.5 % (OR = 4.282, 95 %CI: 3.145-5.831, p < 0.001). ChatGPT-4.0 also provided more comprehensive or correct management recommendations than ChatGPT-3.5 (OR = 1.791, 95 %CI: 1.297-2.473, p < 0.001). Finally, ChatGPT-4.0 exhibits higher consistency in categorizing nodules compared to ChatGPT-3.5 (ICC = 0.732 vs. ICC = 0.429), and both exhibited moderate consistency in management recommendations (ICC = 0.549 vs ICC = 0.575). CONCLUSIONS: Our study demonstrates the potential of ChatGPT in transforming free-text thyroid ultrasound reports into structured formats. ChatGPT-3.5 excels in generating structured reports, while ChatGPT-4.0 shows superior accuracy in nodule categorization and management recommendations.

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.