UNASSIGNED: The detection rate of benign thyroid nodules is increasing every year, with some affected patients experiencing symptoms. Ultrasound-guided thermal ablation can reduce the volume of nodules to alleviate symptoms. As the degree and speed of lesion absorption vary greatly between individuals, an effective model to predict curative effect after ablation is lacking. This study aims to predict the efficacy of ultrasound-guided thermal ablation for benign thyroid nodules using machine learning and explain the characteristics affecting the nodule volume reduction ratio (VRR).
UNASSIGNED: Prospective study.
UNASSIGNED: The clinical and ultrasonic characteristics of patients who underwent ultrasound-guided thermal ablation of benign thyroid nodules at our hospital between January 2020 and January 2023 were recorded.
UNASSIGNED: Six machine learning models (logistic regression, support vector machine, decision tree, random forest, eXtreme Gradient Boosting [XGBoost], and Light Gradient Boosting Machine [LGBM]) were constructed to predict efficacy; the effectiveness of each model was evaluated, and the optimal model selected. SHapley Additive exPlanations (SHAP) was used to visualize the decision process of the optimal model and analyze the characteristics affecting the VRR.
UNASSIGNED: In total, 518 benign thyroid nodules were included: 356 in the satisfactory group (VRR ≥70% 1 year after operation) and 162 in the unsatisfactory group. The optimal XGBoost model predicted satisfactory efficacy with 78.9% accuracy, 88.8% precision, 79.8% recall rate, an F1 value of 0.84 F1, and an area under the curve of 0.86. The top five characteristics that affected VRRs were the proportion of solid components < 20%, initial nodule volume, blood flow score, peripheral blood flow pattern, and proportion of solid components 50-80%.
UNASSIGNED: The models, based on interpretable machine learning, predicted the VRR after thermal ablation for benign thyroid nodules, which provided a reference for preoperative treatment decisions.

Composed of over 1200 species of anaerobes and aerobes bacteria along with bacteriophages, viruses, and fungal species, the human gut microbiota (GM) is vital to health, including digestive equilibrium, immunologic, hormonal, and metabolic homeostasis. Micronutrients, usually refer to trace elements (copper, iodine, iron, selenium, zinc) and vitamins (A, C, D, E), interact with the GM to influence host immune metabolism. So far, microbiome studies have revealed an association between disturbances in the microbiota and various pathological disorders, such as anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis, anxiety, depression, early-onset cancers, type 1 diabetes (T1D) and type 2 diabetes (T2D). As common conditions, thyroid diseases, encompassing Graves\' disease (GD), Graves\' orbitopathy (GO), Hashimoto\'s thyroiditis (HT), benign nodules, and papillary thyroid cancer (TC), have negative impacts on the health of all populations. Following recent studies, GM might play an integral role in triggering diseases of the thyroid gland. Not only do environmental triggers and genetic predisposing background lead to auto-aggressive damage, involving cellular and humoral networks of the immune system, but the intestinal microbiota interacts with distant organs by signals that may be part of the bacteria themselves or their metabolites. The review aims to describe the current knowledge about the GM in the metabolism of thyroid hormones and the pathogenesis of thyroid diseases and its involvement in the appearance of benign nodules and papillary TC. We further focused on the reciprocal interaction between GM composition and the most used treatment drugs for thyroid disorders. However, the exact etiology has not yet been known. To elucidate more precisely the mechanism for GM involvement in the development of thyroid diseases, future work is needed.

UNASSIGNED: A preoperative diagnosis to distinguish malignant from benign thyroid nodules accurately and sensitively is urgently important. However, existing clinical methods cannot solve this problem satisfactorily. The aim of this study is to establish a simple, economic approach for preoperative diagnosis in eastern population.
UNASSIGNED: Our retrospective study included 86 patients with papillary thyroid cancer and 29 benign cases. The ITK-SNAP software was used to draw the outline of the area of interest (ROI), and Ultrosomics was used to extract radiomic features. Whole-transcriptome sequencing and bioinformatic analysis were used to identify candidate genes for thyroid nodule diagnosis. RT-qPCR was used to evaluate the expression levels of candidate genes. SVM diagnostic model was established based on the METLAB 2022 platform and LibSVM 3.2 language package.
UNASSIGNED: The radiomic model was first established. The accuracy is 73.0%, the sensitivity is 86.1%, the specificity is 17.6%, the PPV is 81.6%, and the NPV is 23.1%. Then, CLDN10, HMGA2, and LAMB3 were finally screened for model building. All three genes showed significant differential expressions between papillary thyroid cancer and normal tissue both in our cohort and TCGA cohort. The molecular model was established based on these genetic data and partial clinical information. The accuracy is 85.9%, the sensitivity is 86.1%, the specificity is 84.6%, the PPV is 96.9%, and the NPV is 52.4%. Considering that the above two models are not very effective, We integrated and optimized the two models to construct the final diagnostic model (C-thyroid model). In the training set, the accuracy is 96.7%, the sensitivity is 100%, the specificity is 93.8%, the PPV is 93.3%, and the NPV is 100%. In the validation set, the accuracy is 97.6%, the sensitivity remains 100%, the specificity is 84.6%, the PPV is 97.3%, and the NPV is 100%.
UNASSIGNED: A diagnostic panel is successfully established for eastern population through a simple, economic approach using only four genes and clinical data.

Ultrasound (US) is the primary tool for evaluating patients with thyroid nodules, and the risk of malignancy assessed is based on US features. These features help determine which patients require fine-needle aspiration (FNA) biopsy. Classification systems for US features have been developed to facilitate efficient interpretation, reporting, and communication of thyroid US findings. These systems have been validated by numerous studies and are reviewed in this article. Additionally, this overview provides a comprehensive description of the clinical and laboratory evaluation of patients with thyroid nodules, various imaging modalities, grayscale US features, color Doppler US, contrast-enhanced US (CEUS), US elastography, FNA biopsy assessment, and the recent introduction of molecular testing. The potential of artificial intelligence in thyroid US is also discussed.

Thyroid nodules in pediatric patients are less common than in adults but show a higher malignancy rate. Accordingly, the management of thyroid nodules in pediatric patients is more complex the younger the patient is, needing careful evaluation by physicians. In adult patients, specific ultrasound (US) features have been associated with an increased risk of malignancy (ROM) in thyroid nodules. Moreover, several US risk stratification systems (RSSs) combining the US features of the nodule were built to define the ROM. RSSs are developed for the adult population and their use has not been fully validated in pediatric patients. This study aimed to evaluate the available data about US features of thyroid nodules in pediatric patients and to provide a summary of the evidence regarding the performance of RSS in predicting malignancy. Moreover, insights into the management of thyroid nodules in pediatric patients will be provided.

UNASSIGNED: Emerging evidence suggests alterations in gut microbiota (GM) composition following thyroid nodules (TNs) development, yet the causal relationship remains unclear. Utilizing Mendelian Randomization (MR), this study aims to elucidate the causal dynamics between GM and TNs.
UNASSIGNED: Employing summary statistics from the MiBioGen consortium (n=18,340) and FinnGen consortium (1,634 TNs cases, 263,704 controls), we conducted univariable and multivariable MR analyses to explore the GM-TNs association. Techniques including inverse variance weighted, MR-Egger regression, weighted median, and MR-PRESSO were utilized for causal inference. Instrumental variable heterogeneity was assessed through Cochran\'s Q statistic and leave-one-out analysis. Reverse MR was applied for taxa showing significant forward MR associations, with multivariate adjustments for confounders.
UNASSIGNED: Our findings suggest that certain microbiota, identified as Ruminococcaceae_NK4A214_group (OR, 1.89; 95%CI, 0.47-7.64; p = 0.040), Senegalimassilia (OR, 1.72; 95%CI, 1.03-2.87; p =0.037), Lachnospiraceae (OR,0.64; 95%CI,0.41-0.99; p =0.045), exhibit a protective influence against TNs\' development, indicated by negative causal associations. In contrast, microbiota categorized as Desulfovibrionales (OR, 0.63; 95%CI, 0.41-0.95; p =0.028), Prevotella_7 (OR, 0.79; 95%CI, 0.63-1.00; p =0.049), Faecalibacterium (OR, 0.66; 95%CI, 0.44-1.00; p =0.050), Desulfovibrionaceae (OR, 0.55; 95%CI, 0.35-0.86; p =0.008), Deltaproteobacteria (OR, 0.65; 95%CI, 0.43-0.97; p =0.036) are have a positive correlation with with TNs, suggesting they may serve as risk factors. Reverse MR analyses did not establish significant causal links. After comprehensive adjustment for confounders, taxa Desulfovibrionales (Order), Desulfovibrionaceae (Family), Deltaproteobacteria (Class) remain implicated as potential contributors to TNs\' risk.
UNASSIGNED: This study substantiates a significant causal link between GM composition and TNs development, underscoring the thyroid-gut axis\'s relevance. The findings advocate for the integration of GM profiles in TNs\' prevention and management, offering a foundation for future research in this domain.

UNASSIGNED: The incidence rate of thyroid nodules has reached 65%, but only 5-15% of these modules are malignant. Therefore, accurately determining the benign and malignant nature of thyroid nodules can prevent unnecessary treatment. We aimed to develop a deep-learning (DL) radiomics model based on ultrasound (US), explore its diagnostic efficacy for benign and malignant thyroid nodules, and verify whether it improved the diagnostic level of physicians.
UNASSIGNED: We retrospectively included 1,076 thyroid nodules from 817 patients at three institutions. The radiomics and DL features of the US images were extracted and used to construct radiomics signature (Rad_sig) and deep-learning signature (DL_sig). A Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used for feature selection. Clinical US semantic signature (C_US_sig) was constructed based on clinical information and US semantic features. Next, a combined model was constructed based on the above three signatures in the form of a nomogram. The model was constructed using a development set (institution 1: 719 nodules), and the model was evaluated using two external validation sets (institution 2: 74 nodules, and institution 3: 283 nodules). The performance of the model was assessed using decision curve analysis (DCA) and calibration curves. Furthermore, the C_US_sigs of junior physicians, senior physicians, and expers were constructed. The DL radiomics model was used to assist the physicians with different levels of experience in the interpretation of thyroid nodules.
UNASSIGNED: In the development and validation sets, the combined model showed the highest performance, with areas under the curve (AUCs) of 0.947, 0.917, and 0.929, respectively. The DCA results showed that the comprehensive nomogram had the best clinical utility. The calibration curves indicated good calibration for all models. The AUCs for distinguishing between benign and malignant thyroid nodules by junior physicians, senior physicians, and experts were 0.714-0.752, 0.740-0.824, and 0.891-0.908, respectively; however, with the assistance of DL radiomics, the AUCs reached 0.858-0.923, 0.888-0.944, and 0.912-0.919, respectively.
UNASSIGNED: The nomogram based on DL radiomics had high diagnostic efficacy for thyroid nodules, and DL radiomics could assist physicians with different levels of experience to improve their diagnostic level.

UNASSIGNED: The contrasted-enhanced ultrasound thyroid imaging reporting and data system (CEUS TI-RADS) is the first international risk stratification system for thyroid nodules based on conventional ultrasound (US) and CEUS. This study aimed to evaluate the diagnostic efficacy of CEUS TI-RADS for benign and malignant thyroid nodules and to assess the related interobserver agreement.
UNASSIGNED: The study recruited 433 patients who underwent thyroid US and CEUS between January 2019 and June 2023 at the Affiliated Hospital of Guangdong Medical University. A retrospective analysis of 467 thyroid nodules confirmed by fine-needle aspiration (FNA) and/or surgery was performed. Further, a CEUS TI-RADS classification was assigned to each thyroid nodule based on the CEUS TI-RADS scoring criteria for the US and CEUS features of the nodule. The nodules were grouped based on their sizes as follows: size ≤1 cm, group A; size >1 and ≤4 cm, group B; and size >4 cm, group C. Multivariate logistic regression was used to analyze independent risk factors for malignant thyroid nodules. Pathological assessment was the reference standard for establishing the sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) of CEUS TI-RADS in diagnosing malignant thyroid nodules. The area under the curve (AUC) in the receiver operating characteristic (ROC) curve analysis was used to compare the diagnostic efficacy of the scoring system in predicting malignancy in three groups of nodules. The intragroup correlation coefficient (ICC) was adopted to assess the interobserver agreement of the CEUS TI-RADS score.
UNASSIGNED: Out of the 467 thyroid nodules, 262 were malignant and 205 were benign. Logistic regression analysis revealed that the independent risk factors for malignant thyroid nodules included punctate echogenic foci (P<0.001), taller-than-wide shape (P=0.015), extrathyroidal invasion (P=0.020), irregular margins/lobulation (P=0.036), hypoechoicity on US (P=0.038), and hypoenhancement on CEUS (P<0.001). The AUC for the CEUS TI-RADS in diagnosing malignant thyroid nodules was 0.898 for all nodules, 0.795 for group A, 0.949 for group B, and 0.801 for group C, with the optimal cutoff values of the CEUS TI-RADS being 5 points, 6 points, 5 points, and 5 points, respectively. Among these groups of nodules, group B had the highest AUC, with the SEN, SPE, ACC, PPV, and NPV for diagnosing malignant nodules being 95.9%, 88.1%, 92.8%, 92.6%, and 93.2%, respectively. The ICC of the CEUS TI-RADS classification between senior and junior physicians was 0.862 (P<0.001).
UNASSIGNED: In summary, CEUS TI-RADS demonstrated significant efficacy in distinguishing thyroid nodules. Nonetheless, there were variations in its capacity to detect malignant nodules across diverse sizes, and it demonstrate optimal performance in 1- to 4-cm nodules. These findings may serve as important insights for clinical diagnoses.

Benign thyroid nodules are significantly common and occur in 50-60% of the population. Therefore, differentiation from malignant nodes and the choice of treatment tactics in some cases of benign pathology remain relevant. Despite advances in the clinical evaluation of thyroid nodules, methodological challenges exist due to empirically simplistic understandings of the nodular process. Different opinions on the pathogenesis of thyroid nodules and the history of the formation of the idea of ​​the stages of nodules are considered. For the first time, based on natural principles and many years of ultrasound analysis of changes in benign thyroid nodules, three stages of the nodular process were identified: Development, Wasting and Scarring. The stage of exhaustion has three substages: Initial, Moderate and Significant Wasting. The principles of stage-by-stage changes in nodules are explained and their ultrasound signs are shown. The key principle of the stages of nodules is the ratio of the magnitudes of the processes of regeneration (proliferation) and destruction in the nodule. Separate stage changes may occur in node segments. In such cases, part of the segments may show signs of the Development stage, another part-Wasting, and the third part-Scarring. The different variants of thyroid nodules are explained in terms of stages. Practical recommendations for differentiating ultrasound signs of nodules associated with stages are proposed. Knowledge about the staged changes in thyroid nodules helps reduce the likelihood of diagnostic errors, better navigate the prognosis and choice of treatment tactics, and recommend preventive ultrasound examination of the thyroid.

BACKGROUND: Thyroid nodules pose a frequent clinical dilemma, requiring the use of precise and expedient diagnostic methods. The effectiveness of the Thyroid Imaging Reporting and Data System (TIRADS) in relation to histopathology, which is considered the standard method, continues to be a prominent area of investigation. TIRADS provides a systematic evaluation based on ultrasound imaging. The primary objective of this study was to evaluate the reliability of the Thyroid Imaging Reporting and Data System (TIRADS) in the assessment of thyroid nodules, in comparison with histopathological findings.
METHODS: A retrospective design was employed to analyze data obtained from a sample of 100 patients, ranging in age from 19 to 82 years. The main objective of the study was to assess ultrasonography (USG) findings using the Thyroid Imaging Reporting and Data System (TIRADS) as the major outcome measure. The secondary outcome measure was based on histological evaluations. Multiple statistical tests were utilized, such as linear regression and the kappa statistic.
RESULTS: The outcomes of this study indicate a significant association between TIRADS and histopathology results, particularly in the higher risk groups. The study findings indicate that the diagnostic value of TIRADS III, IV, and V is supported by the respective malignancy risks of 4.1%, 90%, and 100%.
CONCLUSIONS: The appropriate utilization of TIRADS can function as a dependable first method for evaluating thyroid nodules, although it is essential to supplement this approach with histological examinations in order to obtain a thorough understanding.