Malignant Thyroid Nodules Research Articles

Letter to the editor in response to: Serum thyroid autoantibodies in malignant thyroid nodules (Awal TR et al.)

Not available

The role of elastography in determining the risk of malignant thyroid nodules in children

IntroductionUltrasonography is fundamental method of diagnosing focal thyroid lesions. The additional element of ultrasound examination is Strain Elastography which allows for determining the degree of elasticity of the nodule while comparing it to the surrounding thyroid parenchyma. Pediatric thyroid nodules have a higher malignancy risk than in adults, warranting the consideration of fine-needle aspiration biopsy (FNAB) in children.Material and methodsA prospective data analysis of children with focal thyroid lesions treated from 2021 to 2022 was performed. The patients underwent ultrasound and elastography examinations to obtain the Strain Ratio (SR) of the nodules and were qualified for FNAB. SR was determined by the windowing method of relative strains in a semi-quantitative assessment. The FNAB score was determined on the Bethesda scale and the histological examination of the thyroid nodule was performed. The SR values were stratified in three groups: I - thyroid cancer, II - low-risk thyroid tumors, and III - benign lesion. The Kruskal-Wallis test was used to find the relation between the value of SR elastography and the malignancy, with the p value < 0.05 considered significant. The data were analyzed using the multiple comparisons test.ResultsThe 123 FNABs were performed in 100 patients. The final analysis included 119 nodules in 96 patients. In 19 cases, the nodule was malignant, in 5 cases they were low-risk tumors, and in 95 - benign lesions. A difference of the SR value between groups in the pairs of malignant and benign tumors, and malignant and low-risk tumors was revealed. Since no statistically significant difference in the level of elastography was found between benign and low-risk tumors, both groups were combined and formed a group of benign tumors. For the combined groups, the Man-Whitney test was performed, confirming that there was a statistically significant difference between the groups of malignant and benign tumors in the value of SR elastography. The cut-off point for SR for malignant tumors was >3.ConclusionsThe SR index of elastography is significantly higher in malignant nodules. and might be used to select changes with an increased risk of malignancy in thyroid ultrasound of children.

Integration of radiomics ultrasound and TIRADS in diagnosis of thyroid nodules: a narrative review

Abstract Background The most popular technique for evaluating thyroid imaging is high-frequency ultrasonography; however, the TIRADS tool, intended to help with noninvasive risk assessment, has limitations in detecting thyroid cancerous nodules. The purpose of this article was to review the application of TIRADS in ultrasound radiomics and discuss its advantages and limitations. Main text A novel approach to medical picture processing called radiomics can help identify these nodules more precisely. Radiomics involves obtaining high-quality imaging for planning or diagnostic reasons, identifying a macroscopic tumor, extracting quantitative imaging features, and ranking the most informative findings according to prominence, independence, and reproducibility. Conclusions Radiomics combined with TIRADS has demonstrated strong potential in enhancing the accuracy of thyroid nodule malignancy predictions, though challenges such as the need for larger, diverse datasets, and thorough validation persist. Incorporating clinical data, using deep learning models, and standardized imaging protocols could improve diagnostic precision, and further research will, therefore, be essential for its implementation in routine clinical practice.

Radiomics analysis of dual-energy CT-derived iodine maps for differentiating malignant from benign thyroid nodules.

Many thyroid nodules are detected incidentally with the widespread use of sensitive imaging techniques; however, only a fraction of these nodules are malignant, resulting in unnecessary medical expenditures and anxiety. The major challenge is to differentiate benign thyroid nodules from malignant ones. The application of dual-energy computed tomography (DECT) and radiomics provides a new diagnostic approach. Studies applying radiomics from primary tumours on iodine maps to differentiate malignant from benign thyroid nodules are still lacking. To determine the ability of an iodine map-based radiomic nomogram in the venous phase for differentiating malignant thyroid nodules from benign nodules. A total of 141 patients with thyroid nodules who underwent DECT were enrolled and randomly assigned to the training and test cohorts between January 2018 and January 2019. The radiomic score (Rad-score) was derived from nine quantitative features of the iodine maps. Stepwise logistic regression analysis was used to develop radiomic, clinical and combined models. Age, normalized iodine concentration (NIC), and cyst changes were used to construct the clinical model. Receiver operating characteristic (ROC) curve analysis, sensitivity and specificity were performed to analyse the ability of the models to predict malignant thyroid nodules. Calibration analysis was used to test the fitness of the models. Decision curve analysis (DCA) and nomogram construction were also performed. According to the clinical model, age (0.989 [0.984, 0.995]; p<0.001), NIC (0.778 [0.640, 0.995]; p=0.01), and cyst changes (0.617 [0.507, 0.751]; p<0.001) were independently associated with malignant thyroid nodules. According to the combined model, age (0.994 [0.989, 0.999]; p=0.01), NIC (0.797 [0.674, 0.941]; p=0.008), cyst changes (0.786 [0.653, 0.947]; p=0.01), and the rad-score (1.106 [1.070, 1.143]; p<0.001) were independently associated with malignant thyroid nodules. The combined model achieved satisfactory discrimination in predicting malignant thyroid nodules and had greater predictive value in the training (AUC [areas under the curve], 0.96vs. 0.87; p=0.01) and test (AUC, 0.90vs. 0.79; p=0.04) cohorts than did the clinical model. The radiomics nomogram based on iodine maps is useful to distinguish malignant thyroid nodules from benign thyroid nodules.

The value of NGS-based multi-gene testing for differentiation of benign from malignant and risk stratification of thyroid nodules

BackgroundFine-needle aspiration (FNA) biopsy is typically used in conjunction with cytopathologic evaluation to differentiate between benign and malignant thyroid nodules. Even so, the cytology results for 20-30% of thyroid nodules are indeterminate. This study sought to evaluate the usefulness of next-generation sequencing (NGS)-based multi-gene panel testing for risk stratification and the differentiation of benign from malignant thyroid nodules.MethodsThyroid nodule samples were obtained from a cohort of 359 patients who underwent FNA. An NGS-based multi-gene panel testing was conducted for these samples, in which single-nucleotide variants (SNVs) and small insertion/deletions (InDels) can be detected in 11 genes and fusion events can be identified in 5 genes. Surgical resection was conducted for 113 patients (113/359), and then histopathology results were obtained.ResultsIn comparison to cytology alone, the diagnostic sensitivity of NGS combination cytology increased from 0.7245 (95% CI: 0.6289-0.8032) to 0.898 (95% CI: 0.8223-0.9437); the associated AUC was 0.8303 (vs. Cytology AUC: 0.7622, P &amp;lt; 0.001). BRAFV600E was identified in 136 patients, of whom 79 underwent surgery and were diagnosed with papillary thyroid carcinoma (PTC) pathologically. TERT promoter mutations or BRAF/RAS co-mutations with other genes were identified in 5 patients, while 4 patients were diagnosed with malignant thyroid cancer using the pathological method. RAS mutations were identified in 27 patients, while 10 patients underwent surgery, which showed that 3 patients were classified as PTC and 7 cases were benign. In addition, 4 RET fusions, 1 RET activation mutation, and 3 TP53 inactivation mutations were identified in the remaining 8 patients who have not undergone surgery. Negative genetic test results or variants with uncertain significance were identified in 183 patients. Among these patients, 12 malignant thyroid tumors, including 11 PTC and 1 MTC, were diagnosed in 20 patients who received surgery.ConclusionThyroid nodules coupled with BRAFV600E, TERT promoter variants, BRAF/RAS co-mutations with other genes, RET fusions, and RET activating mutations were classified as high-risk. Nodules with RAS mutations (NRAS, KRAS, HRAS) and TP53 inactivating mutations were considered to be in the intermediate-risk group, while those with non-pathogenic mutations (negative and variants of uncertain significance) were placed in the low-risk group. When combined with cytopathology, NGS increases the sensitivity of diagnosing benign and malignant thyroid nodules, and the reference is useful for patient risk stratification.

Heterotopic Ossification with Intrathyroidal Extramedullary Hematopoiesis in Multifocal Papillary Thyroid Carcinoma: Histopathological Findings and Literature Review.

In recent decades, there has been an increase in the identification of thyroid nodules, both benign and malignant, due to the rise in imaging studies and complementary tests. Among thyroid gland tumors, papillary thyroid carcinoma (PTC) stands out as the most prevalent. Degenerative changes, mainly in the form of nodular goiter, have been recorded, occasionally including areas of calcification and, more rarely, ossification; although the latter seldom progresses to calcinosis. Ectopic bone formation, known as osseous metaplasia, is a rare phenomenon in the thyroid gland, even more so when associated with extramedullary hematopoiesis (EMH), characterized by the presence of hematopoietic elements outside the bone marrow. We present the first documented instance in our country of a patient diagnosed with malignant thyroid nodule, specifically PTC, exhibiting areas of heterotopic ossification with EMH on histopathological examination of the surgical specimen. A possible relationship between heterotopic ossification, EMH, and PTC is suggested. Various growth factors such as bone morphogenetic proteins (BMPs), specific BMP subtypes, and associated receptors could play a crucial role in initiating and developing ectopic bone formation in the context of PTC. However, further research is needed to fully elucidate its clinical significance and impact on the therapeutic management of these patients.

Artificial intelligence-enhanced infrared thermography as a diagnostic tool for thyroid malignancy detection

Introduction Thyroid nodules are common, and investigation is crucial for excluding malignancy. Increased intranodular vascularity is frequently observed in malignant tumors, which can be detected through increased skin surface temperatures using noninvasive infrared thermography. We aimed to develop a diagnostic tool for thyroid cancer using infrared thermal images combined with an artificial intelligence (AI) algorithm. Methods We conducted a prospective cross-sectional study involving participants with thyroid nodules undergoing thyroid surgery. Infrared thermal images were collected using a thermal camera on the day prior to surgery. In combination with the final thyroid pathological reports, we utilized a machine learning model based on the pre-trained ResNet50V2 model, a convolutional neural network, to evaluate diagnostic accuracy for malignancy diagnosis. Results The study included 98 participants, 58 with malignant thyroid nodules and 40 with benign thyroid nodules, as determined by pathological results. The AI-enhanced infrared thermal image analyses demonstrated good performance in distinguishing between benign and malignant thyroid nodules, achieving an accuracy of 75% and a sensitivity of 78%. These parameters were slightly lower than those of the AI-model predictor that integrated current practice using preoperative thyroid ultrasound findings and cytological results, yielding an accuracy of 81% and a sensitivity of 84%. Conclusions The infrared thermal images, assisted by an AI model, exhibit good performance in distinguishing thyroid malignancy from benign nodules. This imaging modality has great potential to be used as a noninvasive screening tool for adjunct evaluation of thyroid nodules.

Analysis of factors influencing prevalence and malignancy of thyroid nodules in various iodine uptake areas

Analysis of factors influencing prevalence and malignancy of thyroid nodules in various iodine uptake areas

Contrast-Enhanced Ultrasound for Diagnosing Thyroid Nodules With Indeterminate Cytology: A Retrospective Study.

A small number of thyroid nodules cannot be clearly diagnosed using ultrasound-guided fine needle aspiration biopsy. Contrast-enhanced ultrasound (CEUS) has high diagnostic performance for thyroid nodules. We explored the value of CEUS for diagnosing thyroid nodules with indeterminate cytology. Between September 2019 and July 2022, 27,646 patients with thyroid nodule(s) underwent conventional ultrasound (CUS) in our hospital. From these patients, 597 nodules were subjected to CEUS and ultrasound-guided fine needle aspiration biopsy and 116 thyroid nodules with indeterminate cytology diagnose were enrolled in this study. The independent risk factors for predicting malignancy were determined using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curves were drawn for CUS, CEUS, and CEUS combined with CUS. The area under the curve (AUC) was calculated and compared. Of the 116 thyroid nodules, 40 (34.5%) were benign and 76 (65.5%) were malignant. Univariate analysis showed that the shape, echogenicity, margin, microcalcification, enhancement intensity, enhancement homogeneity, wash in, and wash out were significantly different between benign and malignant thyroid nodules (all p < 0.05). Multivariate logistic regression analysis showed that taller-than-wide, irregular margin, microcalcification, hypo-enhancement, heterogeneity enhancement, synchronous/slower wash in, and synchronous/slower wash out were independent risk factors for malignancy (all p < 0.05). ROC curve analysis showed that the AUC of CUS and CEUS were 0.769 and 0.848, respectively. No significant difference was observed in the AUC between the two modalities (p > 0.05). However, the AUC (0.934) of the CUS combined with CEUS was significantly higher than that of CEUS or CUS alone (both p < 0.05). CEUS is helpful in diagnosing thyroid nodules with indeterminate cytology. CUS combined with CEUS is highly valuable for predicting malignancy.

Optimizing thyroid AUS nodules malignancy prediction: a comprehensive study of logistic regression and machine learning models

BackgroundThe accurate diagnosis of thyroid nodules with indeterminate cytology, particularly in the atypia of undetermined significance (AUS) category, remains challenging. This study aims to predict the risk of malignancy in AUS nodules by comparing two machine learning (ML) and three conventional logistic regression (LR) models.MethodsA retrospective study on 356 AUS nodules in 342 individuals from 6728 patients who underwent thyroid surgery in 2021. All the clinical, ultrasonographic, and molecular data were collected and randomly separated into training and validation cohorts at a ratio of 7: 3. ML (random forest and XGBoost) and LR (lasso regression, best subset selection, and backward stepwise regression) models were constructed and evaluated using area under the curve (AUC), calibration, and clinical utility metrics.ResultsApproximately 90% (321/356) of the AUS nodules were malignant, predominantly papillary thyroid carcinoma with 68.6% BRAF V600E mutations. The final LR prediction model based on backward stepwise regression exhibited superior discrimination with AUC values of 0.83 (95% CI: 0.73-0.92) and 0.80 (95% CI: 0.67-0.94) in training and validation, respectively. Well calibration, and clinical utility were also confirmed. The ML models showed moderate performance. A nomogram was developed on the final LR model.ConclusionsThe LR model developed using the backward stepwise regression, outperformed ML models in predicting malignancy in AUS thyroid nodules. The corresponding nomogram based on this model provides a valuable and practical tool for personalized risk assessment, potentially reducing unnecessary surgeries and enhancing clinical decision-making.

The genomic landscape of papillary thyroid carcinoma on next-generation sequencing in patients undergoing total thyroidectomy.

Thyroidectomy is increasingly performed for suspected malignancy. This cohort study aimed to identify genetic markers associated with malignancy and determine the molecular landscape of papillary thyroid carcinoma (PTC) through next-generation sequencing (NGS) in patients undergoing total thyroidectomy. Among 116 surgical candidates, 103 patients (age=42.9±13.7years; Male: Female=14:89) with benign or malignant thyroid nodules were eligible. Live thyroid tissue samples harvested intraoperatively with adequate DNA and RNA yield were subjected to NGS on the Illumina NovaSeq 6000 platform for genomic and transcriptomic analysis, respectively. Histopathology comprised 20 malignant (19.4%) and 83 benign (80.6%) cases, including 16 PTC (15.5%) cases. On NGS, single nucleotidepolymorphisms (SNPs) in NTRK1 at NC_000001.11:156879016 on chromosome 1 and ALK at NC_000002.12:29717663 on chromosome 2 were frequent in malignant lesions (p<0.05). A SNP in ALK at NC_000002.12:29193706 was consistently a homozygous alternate alleleacross the cohort. DNA-sequencing of PTC lesions identified recurrentsomatic mutations in BRAF (100%), ALK (56.3%), RET (18.8%), PIK3CA (12.5%), NTRK1 (12.5%), NTRK2 (87.5%), NTRK3(12.5%), NRAS(6.3%), and PTCH1 (31.3%) genes. RNA sequencing revealednovelfusiongenes, including MKRN1-BRAF, RN7SL1-CDH1, IRF2BPL-MED12, MED12-IRF2BPL, CPM-MDM2, and AC005895.3-PDGFRB. Inreceiveroperative characteristics analysis, the AUCs ofALKmutationpredictingrecurrence and metastases were 0.818 and 0.783. This Indian study identified novel somatic mutations and fusion genes in PTC, revealing a distinct genomic landscape with implications in precision diagnostics and personalized therapies. NGS with intraoperative live sampling shows promise in prognostication and therapeutic optimization of advanced/metastatic PTC cases. CTRI/2020/09/027607dt 04/09/2020; REF/2020/08/036119.

Do as Sonographers Think: Contrast-Enhanced Ultrasound for Thyroid Nodules Diagnosis via Microvascular Infiltrative Awareness.

Dynamic contrast-enhanced ultrasound (CEUS) imaging can reflect the microvascular distribution and blood flow perfusion, thereby holding clinical significance in distinguishing between malignant and benign thyroid nodules. Notably, CEUS offers a meticulous visualization of the microvascular distribution surrounding the nodule, leading to an apparent increase in tumor size compared to gray-scale ultrasound (US). In the dual-image obtained, the lesion size enlarged from gray-scale US to CEUS, as the microvascular appeared to be continuously infiltrating the surrounding tissue. Although the infiltrative dilatation of microvasculature remains ambiguous, sonographers believe it may promote the diagnosis of thyroid nodules. We propose a deep learning model designed to emulate the diagnostic reasoning process employed by sonographers. This model integrates the observation of microvascular infiltration on dynamic CEUS, leveraging the additional insights provided by gray-scale US for enhanced diagnostic support. Specifically, temporal projection attention is implemented on time dimension of dynamic CEUS to represent the microvascular perfusion. Additionally, we employ a group of confidence maps with flexible Sigmoid Alpha Functions to aware and describe the infiltrative dilatation process. Moreover, a self-adaptive integration mechanism is introduced to dynamically integrate the assisted gray-scale US and the confidence maps of CEUS for individual patients, ensuring a trustworthy diagnosis of thyroid nodules. In this retrospective study, we collected a thyroid nodule dataset of 282 CEUS videos. The method achieves a superior diagnostic accuracy and sensitivity of 89.52% and 94.75%, respectively. These results suggest that imitating the diagnostic thinking of sonographers, encompassing dynamic microvascular perfusion and infiltrative expansion, proves beneficial for CEUS-based thyroid nodule diagnosis.

The accuracy of fine needle aspiration cytology and ultrasonography in assessing thyroid nodules in correlation with histopathology: a retrospective study

Background: Accurately diagnosing thyroid nodules is vital for preventing unnecessary surgeries and providing prompt therapy. Although fine needle aspiration cytology (FNAC) and ultrasonography (US) are widely used diagnostic methods, their reliability is questioned. This study investigates the effectiveness of US and FNAC in thyroid nodule diagnosis and differentiates benign from malignant nodules in relation to final histopathological diagnosis. Method: A retrospective study including 307 adult patients with thyroid diseases who underwent neck US and FNAC before surgery was conducted between April 2019 and May 2023. The diagnostic efficacy of US, FNAC, and their combination usage was compared to histopathological results. Result: Histopathological findings revealed that 187(61%) cases were benign, while 120 (39%) were malignant. The US features of “taller-than-wider” forms and hypoechoic appearance had the highest diagnostic accuracy in characterizing malignant thyroid nodules, with 83% and 73% accuracy, respectively. The combination of US parameters demonstrated high sensitivity, specificity, positive predictive value, and negative predictive value (NPV) of 88.33%, 63.10%, 60.6%, and 89.4%, with a statistically significant area under the ROC curve (AUC: 0.828, P&lt;0.001) than individual parameters. FNAC’s sensitivity, specificity, PPV NPV, and accuracy in detecting malignant lesions were 50%, 95%, 86%, 75%, and 77%, respectively, with acceptable discrimination and statistical significance (AUC: 0.723, P&lt;0.0001). The combination of US parameters and FNAC significantly improved the AUC value (AUC: 0.878, P&lt;0.0001), sensitivity (83.33%), and specificity (79.14%). Univariate analysis showed that hypoechoic appearance, heterogenicity, large mass size (&gt;4 cm), “taller-than-wider,” infiltrative margins, and microcalcifications were risk factors for malignancy in thyroid nodules and were statistically significant (all P values &lt;0.05). Conclusion: Combining US characteristics with FNAC results can afford the maximum analytical accuracy in distinguishing benign from malignant thyroid nodules. This strategy is practical due to its simplicity, minimal invasiveness, and cost-effectiveness, enabling robust management regimens and avoiding additional surgical procedures.

Volatile Organic Compounds in Exhaled Breath, Blood, and Urine Detected in Patients with Thyroid Carcinoma Using Gas Chromatography-Ion Mobility Spectrometry- A Pilot Study.

Background&#xD;Distinguishing between malignant and benign thyroid nodules remains a significant challenge for clinicians and researchers globally. The use of volatile organic compounds (VOCs) has emerged as a novel approach in cancer diagnosis. This prospective pilot study aims to identify VOCs in exhaled breath, blood, and urine that can differentiate benign from malignant thyroid nodules using gas chromatography-ion mobility spectrometry (GC-IMS).&#xD;Methods&#xD;Patients with thyroid nodules scheduled for surgery were enrolled at the Maastricht University Medical Center (MUMC+). Breath samples were analyzed using a BreathSpec GC-IMS machine (G.A.S. Dortmund, Germany), specifically designed for breath analysis. All blood and urine samples were analyzed with a separate GC-IMS device, the FlavourSpec® (G.A.S., Dortmund, Germany).&#xD;Results &#xD;In this proof-of-concept study, 70 patients undergoing thyroid surgery at MUMC+ were consecutively included. Of these patients, 29 were confirmed to have thyroid cancer after surgical resection. The overall analysis did not reveal statistically significant differences in VOCs in breath, urine and blood, between patients with benign and malignant thyroid cancer. &#xD;Conclusion&#xD;This proof-of-concept study demonstrated that GC-IMS was unable to adequately distinguish between the VOC profiles of malignant and benign thyroid nodules. However, this study had a small sample size and future larger studies are needed to investigate the potential of using VOCs to distinguish between benign and malignant thyroid nodules. Furthermore, future research should focus on investigating potential confounders that affect patient VOC profiles.&#xD;(NCT04883294)&#xD.

Ring-Enhancement on CEUS: Is it Useful in the Differential Diagnosis of Solid Thyroid Nodules?

To investigate the efficiency of contrast-enhanced ultrasound (CEUS) features, particularly ring-enhancement patterns, in the differential diagnosis of thyroid nodules. 302 nodules with CEUS ring-enhancement were retrospectively enrolled, including 135 benign and 167 malignant ones. The ring-enhancement patterns were classified into regular and irregular hyper- or hypo-ring enhancement. Comparative analyses of ultrasound (US) and CEUS features between benign and malignant nodules were performed. The diagnostic performances of the ring-enhancement patterns and Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) were compared in nodules with different sizes. Irregular hypo-ring enhancement was much more common in malignancies than that in benign ones, and it was an independent predictor for thyroid malignant nodules. With irregular hypo-ring enhancement as the diagnostic criteria for malignant nodules, the specificity was higher than that of C-TIRADS (85.2% vs. 75.6%, p = .037) while the AUC was comparable (0.845 vs. 0.803, p = .136) in all nodules. When the nodule size was taken into account, the specificity and AUC were both significantly higher than those of C-TIRADS (92.8% vs. 81.1%, p = .021; 0.907 vs. 0.823, p = .026) in nodules ≥10 mm, which can decrease the unnecessary FNA rate. Irregular hypo-ring enhancement was a valuable CEUS feature for the differential diagnosis of thyroid nodules, especially in nodules ≥10 mm.

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.

Background: Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently artificial intelligence (AI) algorithms has improved nodule classification. The purpose of this study was to evaluate the performance of a recent Food and Drug Administration (FDA)-cleared AI tool for detection of malignancy in thyroid nodules on US. Methods: One year of consecutive thyroid US with ≥1 nodule from Duke University Hospital and its affiliate community hospital (649 nodules from 347 patients) were retrospectively evaluated. Included nodules had ground truth diagnoses by surgical pathology, fine needle aspiration (FNA), or three-year follow-up US showing stability. An FDA-cleared AI tool (Koios DS Thyroid) analyzed each nodule to generate (i) American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) descriptors, scores, and follow-up recommendations and (ii) an AI-adapter score to further adjust risk assessments and recommendations. Four groups were then compared: (i) Koios with AI-adapter, (ii) Koios without AI-adapter, (iii) clinical radiology report, and (iv) radiology report combined with AI-adapter. Performance of the final recommendations (FNA or no FNA) was determined based on ground truth, and comparison between the four groups was made using sensitivity, specificity, and receiver-operating-curve analysis. Results: Of 649 nodules, 32 were malignant and 617 were benign. Performance of Koios with AI-adapter enabled was similar to radiologists (area under the curve [AUC] 0.70 for both, [CI 0.60-0.81] and [0.60-0.79], respectively). Koios with AI-adapter had improved specificity compared to radiologists (0.63 [CI: 0.59-0.67] versus 0.43 [CI: 0.38-0.48]) but decreased sensitivity (0.69 [CI: 0.50-0.83) versus 0.81 [CI: 0.61, 0.92]). Highest performance was seen when the radiology interpretation was combined with the AI-adapter (AUC 0.76, [CI: 0.67-0.85]). Combined with the AI-adapter, radiologist specificity improved from 0.43 ([CI: 0.38-0.48]) to 0.53 ([CI: 0.49-0.58]) (McNemar's test p < 0.001), resulting in 17% fewer FNA recommendations, with unchanged sensitivity (0.81, p = 1). Conclusion: Koios DS demonstrated standalone performance similar to radiologists, though with lower sensitivity and higher specificity. Performance was best when radiologist interpretations were combined with the AI-adapter component, with improved specificity and reduced unnecessary FNA recommendations.

The Current Progress of Artificial Intelligence in Approach to Thyroid Nodules: A Narrative Review

Background: Artificial intelligence (AI) can play a significant role in the future of thyroidology. Thyroid nodules are common conditions that may benefit from AI through more accurate and efficient diagnosis, risk stratification, and medical or surgical management. Objective: This paper aims to review the latest developments in AI applications for diagnosing and managing thyroid nodules and cancers. Methods: English full-text articles published in the PubMed and Google Scholar databases from January 2014 to March 2024 were collected and reviewed to provide a comprehensive understanding of the topic. A total of 45 studies were selected based on relevance, robust methodology, statistical significance, and broader topic coverage. Results: Artificial intelligence has emerged as a powerful tool for managing thyroid nodules. First, several studies have demonstrated how AI-powered ultrasound interpretation enhances the diagnosis and classification of nodules while reducing the need for invasive fine-needle aspiration (FNA) biopsies. Second, AI significantly improves the cytopathological differentiation between benign and malignant thyroid nodules by minimizing reliance on pathologists' expertise and implementing standardized diagnostic criteria. When cytopathology is inconclusive, AI also aids in identifying molecular markers from omics data, distinguishing between normal and cancerous cells. Moreover, AI tools have been developed for prognosis assessment, predicting distant metastasis, recurrence, and surveillance by integrating medical imaging features with molecular and clinical factors. Additionally, some AI tools are designed for intraoperative evaluation, improving surgical techniques and reducing complications during thyroidectomy. In non-surgical treatments, several models have been developed to optimize therapeutic doses of radioactive iodine (RAI) and predict the outcomes of new drug formulations. Conclusions: Artificial intelligence has the potential to assist physicians in accurate thyroid nodule diagnosis, classification, decision-making, optimizing treatment strategies, and improving patient outcomes. However, there are still limitations to this technology. Artificial intelligence-driven tools require further advancements before they can be fully integrated into clinical practice and replace specialists.

The Clinical Application of Superb Microvascular Imagingin Evaluating Thyroid Related Diseases: ASystematic Review.

This article reviews all published articles on the application of superb microvascular imaging (SMI) technique in thyroid related diseases before December 31, 2023. SMI, a recently developed ultrasound imaging modality, could display microvascular information by eliminating clutter and preserving low flow signals. Most studies used SMI to help diagnose malignant thyroid nodules and concluded that SMI performed better than color Doppler imaging and power Doppler imaging. At present, there is no consensus when depicting the morphology of vascularity of thyroid nodules (TNs). This problem may be solved by quantitative SMI which makes it possible to quantitatively evaluate the vascularity of TNs. SMI is also applied to evaluate cervical lymph node or thyroid inflammatory diseases. Although, SMI has some limitations, such as no standard for the normal perfusion patterns, and it has a broad application prospect in the diagnosis and management of thyroid related diseases.

Evaluation of ACR TI-RADS for Predicting Malignancy in Thyroid Nodules: Insights from Fine-Needle Aspiration Cytology and Histopathology Results

Evaluation of ACR TI-RADS for Predicting Malignancy in Thyroid Nodules: Insights from Fine-Needle Aspiration Cytology and Histopathology Results

Improving the diagnostic strategy for thyroid nodules: a combination of artificial intelligence-based computer-aided diagnosis system and shear wave elastography.

Thyroid nodules are highly prevalent in the general population, posing a clinical challenge in accurately distinguishing between benign and malignant cases. This study aimed to investigate the diagnostic performance of different strategies, utilizing a combination of a computer-aided diagnosis system (AmCAD) and shear wave elastography (SWE) imaging, to effectively differentiate benign and malignant thyroid nodules in ultrasonography. A total of 126 thyroid nodules with pathological confirmation were prospectively included in this study. The AmCAD was utilized to analyze the ultrasound imaging characteristics of the nodules, while the SWE was employed to measure their stiffness in both transverse and longitudinal thyroid scans. Twelve diagnostic patterns were formed by combining AmCAD diagnosis and SWE values, including isolation, series, parallel, and integration. The diagnostic performance was assessed using the receiver operating characteristic curve and area under the curve (AUC). Sensitivity, specificity, accuracy, missed malignancy rate, and unnecessary biopsy rate were also determined. Various diagnostic schemes have shown specific advantages in terms of diagnostic performance. Overall, integrating AmCAD with SWE imaging in the transverse scan yielded the most favorable diagnostic performance, achieving an AUC of 72.2% (95% confidence interval (CI): 63.0-81.5%), outperforming other diagnostic schemes. Furthermore, in the subgroup analysis of nodules measuring <2 cm or 2-4 cm, the integrated scheme consistently exhibited promising diagnostic performance, with AUCs of 74.2% (95% CI: 61.9-86.4%) and 77.4% (95% CI: 59.4-95.3%) respectively, surpassing other diagnostic schemes. The integrated scheme also effectively addressed thyroid nodule management by reducing the missed malignancy rate to 9.5% and unnecessary biopsy rate to 22.2%. The integration of AmCAD and SWE imaging in the transverse thyroid scan significantly enhances the diagnostic performance for distinguishing benign and malignant thyroid nodules. This strategy offers clinicians the advantage of obtaining more accurate clinical diagnoses and making well-informed decisions regarding patient management.