OBJECTIVE: This study aims to assess the diagnostic efficacy of Korean Thyroid imaging reporting and data system (K-TIRADS), S-Detect software and contrast-enhanced ultrasound (CEUS) when employed individually, as well as their combined application, for the evaluation of thyroid nodules, with the objective of identifying the optimal method for diagnosing thyroid nodules.
METHODS: Two hundred and sixty eight cases pathologically proven of thyroid nodules were retrospectively enrolled. Each nodule was classified according to K-TIRADS. S-Detect software was utilized for intelligent analysis. CEUS was employed to acquire contrast-enhanced features.
RESULTS: The area under curve (AUC) values for diagnosing benign and malignant thyroid nodules using K-TIRADS alone, S-Detect software alone, CEUS alone, the combined application of K-TIRADS and CEUS, the combined application of S-Detect software and CEUS were 0.668, 0.668, 0.719, 0.741, and 0.759, respectively (p < 0.001). The sensitivity rate of S-Detect software was 89.9% (p < 0.001). It was the highest of the five diagnostic methods above.
CONCLUSIONS: The utilization of S-Detect software can be served as a powerful tool for early screening. Notably, the combined utilization of S-Detect software with CEUS demonstrates superior diagnostic performance compared to employing K-TIRADS, S-Detect software, CEUS used individually, as well as the combined application of K-TIRADS with CEUS.

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.

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.

BACKGROUND: In recent years, the incidence of thyroid nodules has increased significantly. There are various ways to treat thyroid nodules, and ablation therapy is one of the important ways to treat thyroid nodules. However, there are many complications and deficiencies in the current ablation treatment of thyroid nodules, especially the incomplete ablation of thyroid cancer nodules, which limits the further application of ablation technology. In this paper, we report two cases of incomplete ablation of thyroid nodules, one of which underwent surgical treatment due to anxiety after ablation, and the postoperative pathology confirmed that there was still residual papillary thyroid carcinoma, and the other patient underwent an operation after ablation, but visited our medical institution again due to cervical lymph node metastasis in a short period of time, and after radical cervical lymph node dissection, pathology confirmed multiple cervical lymph node metastasis. Radionuclide therapy was performed after surgery, and two patients are currently receiving endocrine suppression therapy, and their condition is stable with no signs of recurrence.
CONCLUSIONS: The incomplete ablation of thyroid cancer nodules limits the development of ablation therapy, making ablation treatment a double-edged sword. Guidelines and expert consensus can guide their development, but they need to evolve with the times, and a multidisciplinary diagnostic team can help screen the most suitable patients. Only by using this technology more standardly, using the most appropriate technology, and treating the most suitable patients, can benefit more and more patients.

UNASSIGNED: To investigate the prevalence of thyroid nodules among medical staff and health check-up population in a Level-A hospital (Primary-level hospital) in Jinan City and analyze its influencing factors.
UNASSIGNED: A total of 5812 cases from the two groups were screened. t-test and χ2 tests were used to analyze the differences in the prevalence of thyroid nodules. Multivariate Logistic regression analysis was used to explore the influencing factors.
UNASSIGNED: The average age of medical staff was (36.20±9.11) years old, and the total prevalence was 48.5%. The average age of the healthcare population was (57.89±12.51) years old, and the total prevalence rate was 63.9%, with statistical significance between the two groups (P<0.001 for all). A stratified analysis of the two groups showed that the prevalence increased with age, and the prevalence among medical workers of all ages was higher than that of the health population younger than 50 years of age. Multivariate Logistic regression analysis showed that female sex (OR=1.646,95% CI: 1.315-2.060), older age (OR=1.384,95% CI: 1.265-1.514), and high BMI (OR = 1.199, 95% CI: 1.065-1.350) were risk factors for the disease among medical staff. In the health population, female sex (OR=0.799,95% CI: 0.644-0.992) and high TSH levels (OR = 0.918, 95% CI: 0.874-0.964) were protective factors, while older age (OR=1.634,95% CI: 1.525-1.751) was a risk factor.
UNASSIGNED: There are certain differences in the prevalence of thyroid nodules between the two groups. Age and occupation are important influencing factors. While age is uncontrollable, active regulation of emotional status caused by occupational factors has important clinical guiding significance for reducing the prevalence of thyroid nodules and reducing the social medical burden.

Thyroid nodules (TNs) have emerged as the most prevalent endocrine disorder in China. Fine-needle aspiration (FNA) remains the standard diagnostic method for assessing TN malignancy, although a majority of FNA results indicate benign conditions. Balancing diagnostic accuracy while mitigating overdiagnosis in patients with benign nodules poses a significant clinical challenge. Precise, noninvasive, and high-throughput screening methods for high-risk TN diagnosis are highly desired but remain less explored. Developing such approaches can improve the accuracy of noninvasive methods like ultrasound imaging and reduce overdiagnosis of benign nodule patients caused by invasive procedures. Herein, we investigate the application of gold-doped zirconium-based metal-organic framework (ZrMOF/Au) nanostructures for metabolic profiling of thyroid diseases. This approach enables the efficient extraction of urine metabolite fingerprints with high throughput, low background noise, and reproducibility. Utilizing partial least-squares discriminant analysis and four machine learning models, including neural network (NN), random forest (RF), logistic regression (LR), and support vector machine (SVM), we achieved an enhanced diagnostic accuracy (98.6%) for discriminating thyroid cancer (TC) from low-risk TNs by using a diagnostic panel. Through the analysis of metabolic differences, potential pathway changes between benign nodule and malignancy are identified. This work explores the potential of rapid thyroid disease screening using the ZrMOF/Au-assisted LDI-MS platform, providing a potential method for noninvasive screening of thyroid malignant tumors. Integrating this approach with imaging technologies such as ultrasound can enhance the reliability of noninvasive diagnostic methods for malignant tumor screening, helping to prevent unnecessary invasive procedures and reducing the risk of overdiagnosis and overtreatment in patients with benign nodules.