Combining CEUS and ultrasound parameters in thyroid nodule and cancer diagnosis: a TIRADS-based evaluation.

Contrast-enhanced ultrasonography (CEUS) has been established as a diagnostic tool for assessing microvascularization, essential for understanding angiogenesis in neoplastic development. AIM: This study assesses the effectiveness of CEUS as a supplementary tool to TIRADS in enhancing the ultrasound-based diagnosis of thyroid cancer. METHODS AND MATERIALS: Over one year, 157 nodules in 133 patients, with predominantly solid thyroid nodules, were examined using ultrasound and CEUS and underwent thyroidectomy, allowing for a comparison of ultrasound findings with pathological reports. RESULTS: Thyroid cancer was identified in 31.21% (49/157) of cases. Significant CEUS high-risk features included inhomogeneous enhancement, enhancement defects, and complete hypoenhancement (AUC 0.818, 0.767, 0.864 respectively). Nodules exhibiting any of these features were classified as high-risk in CEUS. The diagnostic performance of TIRADS improved when combined with CEUS, with AUC increasing from 0.707 to 0.840 and improved sensitivity. CONCLUSION: The integration of CEUS with TIRADS significantly enhances the diagnostic accuracy and specificity in identifying thyroid cancer. This combination proves to be a more effective method for risk stratification and diagnosis, highlighting the value of CEUS as an adjunctive tool in thyroid cancer evaluation.

[Diagnostic Value of Ultrasound for Thyroid Nodules With a Spoke-Wheel Blood Flow Pattern].

Objective To explore the diagnostic value of ultrasound for thyroid nodules with a spoke-wheel blood flow pattern.Methods The clinical data of the patients with thyroid nodules presenting a spoke-wheel blood flow pattern examined by ultrasound were collected,and the gray-scale ultrasound features of the nodules were recorded.The diagnostic performance of the Thyroid Imaging Reporting and Data System by American College of Radiology (ACR TI-RADS),Chinese Thyroid Imaging Reporting and Data System (C-TIRADS),and combined specific indicators for the thyroid nodules with a spoke-wheel blood flow pattern was evaluated by comparison with the pathological results,which was regarded as the gold standard.Results A total of 64 patients with thyroid nodules were finally included,including 47 patients with malignant nodules and 17 patients with benign nodules.In addition to the general ultrasound features,central scar mostly appeared in malignant nodules (χ2=5.968,P=0.015),while central coarse calcification was more common in benign nodules (χ2=10.899,P=0.001).After the combination of central scar and central gross calcification,the diagnostic performance of ACR TI-RADS and C-TIRADS was improved (both P<0.001).Conclusions When the thyroid nodule shows a spoke-wheel blood flow pattern,one should be cautious of the possibility of malignancy.Combining central scar and central coarse calcification can improve the accuracy of ultrasonic diagnosis.

Utilizing machine learning for early screening of thyroid nodules: a dual-center cross-sectional study in China.

Background: Thyroid nodules, increasingly prevalent globally, pose a risk of malignant transformation. Early screening is crucial for management, yet current models focus mainly on ultrasound features. This study explores machine learning for screening using demographic and biochemical indicators. Methods: Analyzing data from 6,102 individuals and 61 variables, we identified 17 key variables to construct models using six machine learning classifiers: Logistic Regression, SVM, Multilayer Perceptron, Random Forest, XGBoost, and LightGBM. Performance was evaluated by accuracy, precision, recall, F1 score, specificity, kappa statistic, and AUC, with internal and external validations assessing generalizability. Shapley values determined feature importance, and Decision Curve Analysis evaluated clinical benefits. Results: Random Forest showed the highest internal validation accuracy (78.3%) and AUC (89.1%). LightGBM demonstrated robust external validation performance. Key factors included age, gender, and urinary iodine levels, with significant clinical benefits at various thresholds. Clinical benefits were observed across various risk thresholds, particularly in ensemble models. Conclusion: Machine learning, particularly ensemble methods, accurately predicts thyroid nodule presence using demographic and biochemical data. This cost-effective strategy offers valuable insights for thyroid health management, aiding in early detection and potentially improving clinical outcomes. These findings enhance our understanding of the key predictors of thyroid nodules and underscore the potential of machine learning in public health applications for early disease screening and prevention.

Clinical risk factors and cancer risk of thyroid imaging reporting and data system category 4 A thyroid nodules.

PURPOSE: Beyond the Thyroid Imaging Reporting and Data System (TIRADS) classification of thyroid nodules, additional factors must be weighed in the decision to perform fine needle aspiration (FNA). In this study, we aimed to identify risk factors for malignancy in patients with ultrasound-classified Chinese-TIRADS (C-TIRADS) 4 A nodules. METHODS: Patients who underwent thyroid FNA at our institution between May 2021 and September 2022 were enrolled. We collected demographic data, including age, sex, previous radiation exposure, and family history. An in-person questionnaire was used to collect lifestyle data, such as smoking habits and alcohol consumption. Body mass index (BMI) was calculated. The serum levels of thyroid stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), and thyroglobulin antibody (TGAb) were measured. Prior to FNA, ultrasonic inspection reports were reviewed. The cytologic diagnoses for FNA of thyroid nodules followed the Bethesda System for Reporting Thyroid Cytopathology (2017). RESULTS: Among the 252 C-TIRADS 4 A nodules, 103 were malignant. Compared to those in the benign group, the patients in the malignant group had a younger age (42.2 ± 13.6 vs. 51.5 ± 14.0 years, P < 0.001). Logistic regression showed that advanced age was associated with a lower risk of malignancy in C-TIRADS 4 A nodules (OR = 0.95, 95% CI 0.93 ~ 0.97, P < 0.001). We demonstrated a decreased risk of malignancy in patients with 48.5 years or older. CONCLUSION: Advanced age was associated with a decreased risk of malignancy in patients with C-TIRADS 4 A nodules. This study indicated that in addition to sonographic characteristics, patient age should be considered when assessing the risk of malignancy.

Comparison the accuracy of thyroid sono-elastography vs. ultrasound-guided fine needle aspiration cytology with thyroid malignancy diagnosis histopathology.

Objective. The intend of the present study was to assess the diagnostic performance of strain elastography in investigating the thyroid nodule malignancy taking the surgical biopsy as a gold standard reference test. Methods. The study included 120 patients with 123 thyroid nodules, of which 67 had total thyroidectomy. The American College of Radiology Thyroid Imaging Reporting and Data Systems (ACR-TIRADS) were evaluated for all nodules. All suspicious nodules were referred for a fine needle aspiration cytology (FNAC) if they fulfilled the required size. Strain elastography was performed for each suspicious nodule. Ultrasound-guided FNAC was performed for all suspicious nodules. Total thyroidectomy was performed in those whom the suspicious nodules were proven by FNAC. Results. Strain ratio had a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of 84%, 81%, 95%, 85%, and 84%, respectively, with a cut point 1.96. Elasticity score had a sensitivity, specificity, PPV, NPV, and diagnostic accuracy of 100%, 80%, 95%, 85% and 87%, respectively, with a cut point 0.96. The elasticity score had a statistically significantly odds ratio for detecting the benignity 3.9 C. I (1.6-9.3). Conclusion. Strain elastography has a high diagnostic performance in detecting the malignant as well as benign nodules, thus it can limit the rate of unneeded FNAC or surgery especially among B3 and B4 groups with indeterminate cytology.

Diagnostic Accuracy of American College of Radiology Thyroid Imaging Reporting Data System: A Single-center Cross-sectional Study.

Objective: This study aims to evaluate the diagnostic accuracy of the American College of Radiology Thyroid Imaging Reporting Data System (ACR TI-RADS) in identifying nodules that need to undergo fine-needle aspiration biopsy (FNAB) and identify specific thyroid ultrasound characteristics of nodules associated with thyroid malignancy in Filipinos in a single tertiary center. Methodology: One hundred seventy-six thyroid nodules from 130 patients who underwent FNAB from January 2018 to December 2018 were included. The sonographic features were described and scored using the ACR TI-RADS risk classification system, and the score was correlated to their final cytopathology results. Results: The calculated malignancy rates for TI-RADS 2 to TI-RADS 5 were 0%, 3.13%, 7.14%, and 38.23%, respectively, which were within the TI-RADS risk stratification thresholds. The ACR TI-RADS had a sensitivity of 89.5% and specificity of 54%, LR + of 1.95 and LR - of 0.194, NPV of 97.7%, PPV of 19.1%, and accuracy of 58%. Conclusion: The ACR TI-RADS may provide an effective malignancy risk stratification for thyroid nodules and may help guide the decision for FNAB among Filipino patients. The classification system may decrease the number of unnecessary FNABs for nodules with low-risk scores.

Impact of ultrasound elastography in evaluating Bethesda category IV thyroid nodules with histopathological correlation.

Introduction: Fine needle aspiration (FNA) is the gold standard method recommended in the diagnosis of thyroid nodules. Bethesda IV cytology results are identified in 7-9% of nodules investigated through FNA, with reported malignancy rate in a wide range of 10-40%. The recommended treatment is either surgical or risk additional molecular testing before surgery. However, a large number of nodules belonging to this category (60-80%) are observed to be benign after surgical excision, which can put the patient at risk of unnecessary surgical morbidity. This study aimed to assess the diagnostic performance of conventional ultrasound, the ACR TI-RADS score and elastography in cases of Bethesda IV cytology on FNA. Methods: We evaluated ninety-seven consecutive cases with Bethesda category IV results on FNA by using conventional B-mode ultrasound, qualitative strain or shear-wave elastography (Hitachi Preirus Machine, Hitachi Inc., Japan and Aixplorer Mach 30 Supersonic Imagine, Aix-en-Provence, France) and all nodules were classified according to the ACR TI-RADS system. Conventional ultrasound was used to categorize the nodules as potentially malignant based on the following features: hypoechogenicity, inhomogeneity, a taller than wide shape, irregular margins, presence of microcalcifications, an interrupted thyroid capsule and suspicious cervical lymph nodes. Elastography classified nodules with increased stiffness as suspicious for malignancy. Results: We considered pathology results as the gold standard diagnosis, finding that 32 out of 97 nodules were carcinomas (33%) and 65 out of 97 were benign nodules (67%). The benign group included twenty cases of non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Finally, we compared ultrasound data with pathology results, which showed that nineteen out of the 32 malignant nodules presented with increased stiffness on elastography (p=0.0002). On conventional ultrasound, we found that microcalcifications (p=0.007), hypoechogenicity and irregular margins (p=0.006) are features which can distinguish between benign and malignant nodules with statistical significance. Discussion: Integrating elastography as a parameter of the ACR TI-RADS score in the evaluation of Bethesda category IV nodules showed a sensitivity of 90.62% in detecting thyroid cancer cases (p=0.006). We can conclude that elastographic stiffness as an addition to high risk features observed on conventional ultrasound improves the detection of malignant nodules in cases with Bethesda IV cytology.

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.

Thyroid imaging reporting and data system with MRI morphological features for thyroid nodules: diagnostic performance and unnecessary biopsy rate.

BACKGROUND: To assess MRI-based morphological features in improving the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) for categorizing thyroid nodules. METHODS: A retrospective analysis was performed on 728 thyroid nodules (453 benign and 275 malignant) that postoperative pathology confirmed. Univariate and multivariate logistic regression analyses were used to find independent predictors of MRI morphological features in benign and malignant thyroid nodules. The improved method involved increasing the ACR-TIRADS level by one when there are independent predictors of MRI-based morphological features, whether individually or in combination, and conversely decreasing it by one. The study compared the performance of conventional ACR-TIRADS and different improved versions. RESULTS: Among the various MRI morphological features analyzed, restricted diffusion and reversed halo sign were determined to be significant independent risk factors for malignant thyroid nodules (OR = 45.1, 95% CI = 23.2-87.5, P < 0.001; OR = 38.0, 95% CI = 20.4-70.7, P < 0.001) and were subsequently included in the final assessment of performance. The areas under the receiver operating characteristic curves (AUCs) for both the conventional and four improved ACR-TIRADSs were 0.887 (95% CI: 0.861-0.909), 0.945 (95% CI: 0.926-0.961), 0.947 (95% CI: 0.928-0.962), 0.945 (95% CI: 0.926-0.961) and 0.951 (95% CI: 0.932-0.965), respectively. The unnecessary biopsy rates for the conventional and four improved ACR-TIRADSs were 62.8%, 30.0%, 27.1%, 26.8% and 29.1%, respectively, while the malignant missed diagnosis rates were 1.1%, 2.8%, 3.7%, 5.4% and 1.2%. CONCLUSIONS: MRI morphological features with ACR-TIRADS has improved diagnostic performance and reduce unnecessary biopsy rate while maintaining a low malignant missed diagnosis rate.

[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.

Repetition of thyroid fine-needle aspiration cytology after an initial nondiagnostic result: Is there an optimal timing?

INTRODUCTION: It is suggested to wait at least 3 months to repeat a fine needle aspiration cytology (FNAC) to avoid possible inflammatory cytological changes induced by a previous procedure. This study evaluated the influence of the interval between 2 FNACs in a cohort with a previous non-diagnostic (ND) FNAC. We analysed the occurrence of ND or atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) results in the second FNAC, based on the intervals between procedures. PATIENTS AND METHODS: Retrospective study (2017-2020) including thyroid nodules with a ND result, subjected to another FNAC. Demographic, clinical and echographic data, interval between FNACs and their results were collected. We considered the intervals: ≤/>3 months and ≤/>6 months. Second FNAC results were classified as ND, AUS/FLUS or diagnostic (including the other Bethesda categories). RESULTS: Included 190 nodules (190 patients - 82.1% women, mean age 60±13.7 years) with a first ND FNAC. The second FNAC results were: ND in 63 cases, AUS/FLUS in 9 and diagnostic in 118 cases. There were no statistical differences in FNAC results performed≤3 months (13 ND, 2 AUS/FLUS, 19 diagnostic) vs >3 months (50 ND, 7 AUS/FLUS, 99 diagnostic; p=0.71). Similarly, there were no statistical differences considering a longer time interval: ≤6 months (32 ND, 3 AUS/FLUS, 59 diagnostic) vs >6 months (31 ND, 6 AUS/FLUS, 59 diagnostic; p=0.61). CONCLUSIONS: Time interval between FNACs was not relevant to the final cytological result. Early FNAC repetition did not increase the cases of ND or AUS/FLUS.

Differential diagnosis of thyroid nodules by DCE-MRI based on compressed sensing volumetric interpolated breath-hold examination: A feasibility study.

OBJECTIVES: To explore the potential and performance of quantitative and semi-quantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on compressed sensing volumetric interpolated breath-hold (CS-VIBE) examination in the differential diagnosis of thyroid nodules. MATERIALS AND METHODS: A total of 208 patients with 259 thyroid nodules scheduled for surgery operation were prospectively recruited. All participants underwent routine and DCE-MRI. DCE-MRI quantitative parameters [Ktrans, Kep, Ve], semi-quantitative parameters [wash-in, wash-out, time to peak (TTP), arrival time (AT), peak enhancement intensity (PEI), and initial area under curve in 60 s (iAUC)] and time-intensity curve (TIC) types were analyzed. Differential diagnostic performances were assessed using area under the receiver operating characteristic curve (AUC) and compared with the Delong test. RESULTS: Ktrans, Kep, Ve, wash-in, wash-out, PEI and iAUC were statistically significantly different between malignant and benign nodules (P < 0.001). Among these parameters, ROC analysis revealed that Ktrans showed the highest diagnostic performance in the differentiation of benign and malignant nodules, followed by wash-in. ROC analysis also revealed that Ktrans achieved the best diagnostic performance for distinguishing papillary thyroid carcinoma (PTC) from non-PTC, follicular adenoma (FA) from non-FA, nodular goiter (NG) from non-NG, with AUC values of 0.854, 0.895 and 0.609, respectively. Type III curve is frequently observed in benign thyroid nodules, accounting for 77.4% (82/106). While malignant nodules are more common in type II, accounting for 57.5% (88/153). CONCLUSION: Thyroid examination using CS-VIBE based DCE-MRI is a feasible, non-invasive method to identify benign and malignant thyroid nodules and pathological types.

Radiating blood flow signal: A new ultrasound feature of thyroid carcinoma.

OBJECTIVE: To summary radiating blood flow signals and evaluate their diagnostic value in differentiating benign and malignant thyroid nodules. MATERIALS AND METHODS: We retrospectively recruited consecutive patients undergoing US at 4 hospitals from 2018 to 2022. In a training dataset, the correlations of US features with malignant thyroid nodules were assessed by multivariate logistic analysis. Multivariate logistic regression models involving the ACR TI-RADS score, radiating blood flow signals and their combination were built and validated internally and externally. The AUC with 95% asymptotic normal confidence interval as well as sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) with 95% exact binomial confidence intervals were calculated. RESULTS: Among 2475 patients (1818 women, age: 42.47 ± 11.57; 657 men, age: 42.16 ± 11.69), there were 3187 nodules (2342 malignant nodules and 845 benign nodules). Radiating blood flow signals were an independent risk factor for diagnosing thyroid carcinoma. In the training set, the AUC of the model using the combination of radiating blood flow signals and the ACR TI-RADS score (0.95 95 % CI: [0.94, 0.97]; P < 0.001) was significantly higher than that of the ACR TI-RADS model (0.91 [0.89, 0.93]). In the two internal validation sets and the external validation set, the AUCs of the combination model were 0.97 [0.96, 0.98], 0.92 [0.88, 0.96], and 0.91 [0.86, 0.95], respectively, and were all significantly higher than that of the ACR TI-RADS score (0.92 [0.90, 0.95], 0.86 [0.81, 0.91], 0.84 [0.79, 0.89]; P < 0.001). CONCLUSION: Radiating blood flow is a new US feature of thyroid carcinomas that can significantly improve the diagnostic performance vs. the ACR TI-RADS score.

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.

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.

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.

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.

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.

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.