Characterization Of Thyroid Nodules Research Articles

CT-Based Radiomics Models for Differentiation of Benign and Malignant Thyroid Nodules: A Multicenter Development-and-Validation.

Background: CT is increasingly detecting thyroid nodules. Prior studies indicated a potential role of CT-based radiomics models in characterizing thyroid nodules, although lacked external validation. Objectives: To develop and validate a CT-based radiomics model for the differentiation of benign and malignant thyroid nodules. Methods: This retrospective study included 378 patients (mean age, 46.3±13.9 years; 86 men, 292 women) with 408 resected thyroid nodules (145 benign, 263 malignant) from two centers (center 1: 293 nodules, January 2018-December 2022; center 2: 115 nodules, January 2020-December 2022), who underwent preoperative multiphase neck CT (noncontrast, arterial, and venous phases). Nodules from center 1 were divided into training (n=206) and internal validation (n=87) sets; all nodules from center 2 formed an external validation set. Radiologists assessed nodules for morphologic CT features. Nodules were manually segmented on all phases, and radiomic features were extracted. Conventional (clinical and morphologic CT), noncontrast radiomics, arterial-phase radiomics, venous-phase radiomics, multiphase radiomics, and combined (clinical, morphologic, and multiphase radiomics) models were established using feature selection methods and evaluated by ROC curve analysis, calibration curves, and decision-curve analysis. Results: The combined model included patient age, three morphologic features (cystic change, edge interruption sign, abnormal cervical lymph nodes), and 28 radiomic features (from all three phases). In the external validation set, the combined model had AUC of 0.923 and, at an optimal threshold derived in the training set, sensitivity of 84.0%, specificity of 94.1%, and accuracy of 87.0%. In the external validation set, AUC was significantly higher for the combined model than for the conventional model (0.827), noncontrast radiomics model (0.847), arterial-phase radimoics model (0.826), venous-phase radiomics model (0.773), and multiphase radiomics model (0.824) (all p<.05). In the external validation set, the calibration curves indicated lowest (i.e., best) Brier score for the combined model; in decision-curve analysis, the combined model had the highest net benefit for most of the range of threshold probabilities. Conclusion: A combined model incorporating clinical, morphologic CT, and multiphasic radiomics CT features, exhibited robust performing in differentiating benign and malignant thyroid nodules. Clinical Impact: The combined radiomics model may help guide further management for thyroid nodules detected on CT.

Multimodal ultrasonography evaluation in thyroid nodule characterization: what is the ideal algorithm?

The aim of this study is to investigate the diagnostic performances of Ultrasonography (US), Shear-wave Elastography(SWE), and Superb Microvascular Imaging (SMI) findings in the diagnosis of malignant thyroid nodules (MTNs) and todetermine the US algorithm with the best diagnostic performance. Eighty-one nodules in 77 patientswho had underwent multimodal US with biopsy results, were evaluated. Echogenicity, nodule components, contours, presenceand type of calcification, and size were analyzed with US. Nodule stiffness and vascular index (VI) measurements wereperformed via SWE and SMI. The power of the US algorithm in predicting malignancy was evaluated. Hypoechogenicity,irregular contour, aspect ratio (anteroposterior (AP)/transvers diameter) >1, and >43.9 kPa were the characteristicshad significant efficacy in the diagnosis of MTNs. Sensitivity, specificity, and AUC values were respectively 100%, 48.5%,and 0.742 for hypoechogenicity; 80%, 90.1%, and 0.855 for irregular contour; 60%, 71.2%, and 0.656 for aspect ratio >1;60%, 72.7%, and 0.671 for >43.9 kPa; and 93.3%, 90.9%, and 0.921 for the US algorithm. VI did not show significant efficacyin diagnosis. Some B-mode and SWE findings showed sufficient efficacy in differentiating benign and malignnodules on their own. However, diagnostic accuracy increased significantly when the US algorithm was applied.

Integrated use of contrast-enhanced and grey-scale ultrasound in assigning American College of Radiology Thyroid Imaging–Reporting and Data System scores for characterisation of thyroid nodules: A prospective observational study

Background: The advent and increased use of high-resolution ultrasonography has resulted in improved detection of thyroid nodules. Even with the use of various Thyroid Imaging–Reporting and Data System, accurate imaging diagnosis of malignant thyroid nodules has been suboptimal, which necessitated use of newer modalities like contrast-enhanced ultrasonography alone and in combination for this purpose. Although the combined use of various Thyroid Imaging–Reporting and Data System and contrast-enhanced ultrasonography has turned out to be accurate in many studies, the ideal way to integrate contrast-enhanced ultrasonography into the Thyroid Imaging–Reporting and Data System algorithm is under-investigated. Purpose: To estimate and compare the diagnostic accuracy of American College of Radiology Thyroid Imaging–Reporting and Data System and contrast-enhanced ultrasonography in differentiating benign and malignant nodules alone and in combination. To estimate the diagnostic accuracy of contrast-enhanced ultrasonography in re-categorisation of Thyroid Imaging–Reporting and Data System 3 and Thyroid Imaging–Reporting and Data System 4 thyroid nodules. Materials and methods: This was a prospective cohort study performed in a tertiary care university–based hospital for 3 years. Adult patients with clinical or previous sonographic diagnosis of thyroid nodules were selected. Each of the nodules were assessed using ultrasonography and categorised using American College of Radiology Thyroid Imaging–Reporting and Data System criteria. The lesion was then assessed for contrast-enhanced ultrasonography features. The final diagnosis of the nodules was made using fine needle aspiration cytology. The diagnostic accuracy in diagnosis of malignant thyroid nodules for each of the American College of Radiology Thyroid Imaging–Reporting and Data System and contrast-enhanced ultrasonography alone and in combination was assessed. The diagnostic accuracy of contrast-enhanced ultrasonography in diagnosis of malignant thyroid nodules categorised as Thyroid Imaging–Reporting and Data System 3 and Thyroid Imaging–Reporting and Data System 4 was also assessed. Results: American College of Radiology Thyroid Imaging–Reporting and Data System had a sensitivity, specificity, negative predictive value, positive predictive value and diagnostic accuracy of 86.6%, 54.5%, 17.4%, 97.3% and 57.7%, respectively, in diagnosis of malignant thyroid nodules. Contrast-enhanced ultrasonography had a sensitivity, specificity, negative predictive value, positive predictive value and diagnostic accuracy of 86.6%, 95.4%, 67.9%, 98.4% and 94.4%, respectively, in diagnosis of malignant thyroid nodules. Contrast-enhanced ultrasonography had sensitivity, specificity, negative predictive value, positive predictive value and diagnostic accuracy of 93.3%, 100.0%, 100.0%, 99.2% and 99.3%, respectively, in re-categorisation of Thyroid Imaging–Reporting and Data System 3 and Thyroid Imaging–Reporting and Data System 4 nodules. Conclusion: Contrast-enhanced ultrasonography can play a key role in diagnosis of malignant thyroid nodules which are categorised as indeterminate on grey-scale ultrasound.

Biopsy or no biopsy? Risk of Malignancy in Thyroid Nodules Based on TI-RADS Criteria

Purpose: To assess the risk of malignancy in thyroid nodules recommended for biopsy using the American College of Radiology (ACR) Thyroid Imaging Reporting &amp; Data System (TIRADS).&#x0D; Methods: We conducted a retrospective review of ultrasound (US) guided thyroid biopsies performed from 2018- 2023 at IU Health hospitals for nodules which were recommended for biopsy based on ACR TI-RADS criteria and compared with histopathology results. The research was conducted at University and Methodist Hospital radiology reading rooms.&#x0D; Results: Of the total 210 thyroid nodules recommended for biopsy, only 8.57% of nodules biopsied were malignant. Positive malignancy rate for TR-3 was 10.2%, TR-4 was 7.1%, and TR-5 was 12.5%. TR-3 nodules less than 2.9 cm were all benign on histopathology. TR-4 nodules less than 1.5 cm were all benign on histopathology. TR-5 nodules were all benign on histopathology. The most common malignancy was papillary thyroid carcinoma.&#x0D; Conclusion: ACR TI-RADS is currently the standard of care for guidelines and lexicon for radiologists for characterizing thyroid nodules. Our results demonstrate that all TR-3 nodules less than 2.9 cm were all benign and most of the TR-4 and TR-5 nodules biopsied were benign on histopathology. The lower biopsy rate for positive malignancy even for TR-4 and TR-5 nodules respectively raises the need for revision for existing TI-RADS criteria, as more nodules could be followed up with ultrasound rather than percutaneous biopsy, which will alleviate patient anxiety as well as decrease overall healthcare costs.

Thyroid nodule characterization using Spectral Detector Computed Tomography (SDCT) in comparison to ultrasound

ObjectiveTo characterize thyroid nodules seen on Spectral Detector computed tomography (SDCT) in respect to their Thyroid Imaging Reporting and Data System (TI-RADS) category on Ultrasound (US). MethodsWe included patients that underwent US examination for the evaluation of thyroid nodules and contrast-enhanced SDCT examination of the neck/thorax, between the years 2018–2020. The SDCT and US were performed within 6 months of each other. Only patients with a visible thyroid nodule on SDCT were included. Attenuation measurements of the nodules in Hounsfield units (HU) were performed on the conventional CT images, virtual non-contrast (VNC) images and virtual monoenergetic images of 40 keV and 100 keV. The Iodine concentration, spectral slope and enhancement estimation results of the nodules were measured. We compared the spectral results between two groups of nodules, according to the US report: TI-RADS 2–3 and TI-RADS 4–5 groups. ResultsThirty-eight nodules were included in the study, 22 nodules in the TI-RADS 2–3 group and 16 in the TI-RADS 4–5 group. The nodules of the TI-RADS 4–5 group had significantly higher Iodine concentration measurement, 4.6 ± 1.8 mg/ml, compared to 2.3 ± 1.2 mg/ml in the TI-RADS 2–3 group; significantly higher estimated enhancement, 3.9 ± 1.5, compared to 2.2 ± 0.7; and significantly higher calculated spectral slope, 5.6 ± 2.2 compared to 2.9 ± 1.5 (p < 0.001). ConclusionSpectral results of SDCT may assist in differentiating intermediate-high risk (TI-RADS 4–5) from low risk (TI-RADS 2–3) thyroid nodules.Advances in knowledge: SDCT offers additional information for the characterization of thyroid nodules.

Thyroid ultrasound and its ancillary techniques.

Ultrasound (US) of the thyroid has been used as a diagnostic tool since the late 1960s. US is the most important imaging tool for diagnosing thyroid disease. In the majority of cases a correct diagnosis can already be made in synopsis of the sonographic together with clinical findings and basal thyroid hormone parameters. However, the characterization of thyroid nodules by US remains challenging. The introduction of Thyroid Imaging Reporting and Data Systems (TIRADSs) has improved diagnostic accuracy of thyroid cancer significantly. Newer techniques such as elastography, superb microvascular imaging (SMI), contrast enhanced ultrasound (CEUS) and multiparametric ultrasound (MPUS) expand diagnostic options and tools further. In addition, the use of artificial intelligence (AI) is a promising tool to improve and simplify diagnostics of thyroid nodules and there is evidence that AI can exceed the performance of humans. Combining different US techniques with the introduction of new software, the use of AI, FNB as well as molecular markers might pave the way for a completely new area of diagnostic accuracy in thyroid disease. Finally, interventional ultrasound using US-guided thermal ablation (TA) procedures are increasingly proposed as therapy options for benign as well as malignant thyroid diseases.

A Deep Learning Framework for the Characterization of Thyroid Nodules from Ultrasound Images Using Improved Inception Network and Multi-Level Transfer Learning.

In the past few years, deep learning has gained increasingly widespread attention and has been applied to diagnosing benign and malignant thyroid nodules. It is difficult to acquire sufficient medical images, resulting in insufficient data, which hinders the development of an efficient deep-learning model. In this paper, we developed a deep-learning-based characterization framework to differentiate malignant and benign nodules from the thyroid ultrasound images. This approach improves the recognition accuracy of the inception network by combining squeeze and excitation networks with the inception modules. We have also integrated the concept of multi-level transfer learning using breast ultrasound images as a bridge dataset. This transfer learning approach addresses the issues regarding domain differences between natural images and ultrasound images during transfer learning. This paper aimed to investigate how the entire framework could help radiologists improve diagnostic performance and avoid unnecessary fine-needle aspiration. The proposed approach based on multi-level transfer learning and improved inception blocks achieved higher precision (0.9057 for the benign class and 0.9667 for the malignant class), recall (0.9796 for the benign class and 0.8529 for malignant), and F1-score (0.9412 for benign class and 0.9062 for malignant class). It also obtained an AUC value of 0.9537, which is higher than that of the single-level transfer learning method. The experimental results show that this model can achieve satisfactory classification accuracy comparable to experienced radiologists. Using this model, we can save time and effort as well as deliver potential clinical application value.

Quantitative Biomarkers Derived from a Novel Contrast-Free Ultrasound High-Definition Microvessel Imaging for Distinguishing Thyroid Nodules.

Low specificity in current ultrasound modalities for thyroid cancer detection necessitates the development of new imaging modalities for optimal characterization of thyroid nodules. Herein, the quantitative biomarkers of a new high-definition microvessel imaging (HDMI) were evaluated for discrimination of benign from malignant thyroid nodules. Without the help of contrast agents, this new ultrasound-based quantitative technique utilizes processing methods including clutter filtering, denoising, vessel enhancement filtering, morphological filtering, and vessel segmentation to resolve tumor microvessels at size scales of a few hundred microns and enables the extraction of vessel morphological features as new tumor biomarkers. We evaluated quantitative HDMI on 92 patients with 92 thyroid nodules identified in ultrasound. A total of 12 biomarkers derived from vessel morphological parameters were associated with pathology results. Using the Wilcoxon rank-sum test, six of the twelve biomarkers were significantly different in distribution between the malignant and benign nodules (all p < 0.01). A support vector machine (SVM)-based classification model was trained on these six biomarkers, and the receiver operating characteristic curve (ROC) showed an area under the curve (AUC) of 0.9005 (95% CI: [0.8279,0.9732]) with sensitivity, specificity, and accuracy of 0.7778, 0.9474, and 0.8929, respectively. When additional clinical data, namely TI-RADS, age, and nodule size were added to the features, model performance reached an AUC of 0.9044 (95% CI: [0.8331,0.9757]) with sensitivity, specificity, and accuracy of 0.8750, 0.8235, and 0.8400, respectively. Our findings suggest that tumor vessel morphological features may improve the characterization of thyroid nodules.

Diagnostic Feasibility of Ultrasound in Characterization of Thyroid Nodules Using Thyroid Imaging Reporting and Data System

Diagnostic Feasibility of Ultrasound in Characterization of Thyroid Nodules Using Thyroid Imaging Reporting and Data System

Value of CEUS features in diagnosing thyroid nodules with halo sign on B-mode ultrasound

BackgroundThe results of halo sign in the differential diagnosis of thyroid nodules were conflicting, and the value of contrast-enhanced ultrasound (CEUS) in characterization of thyroid nodules with halo has not been fully evaluated. This study was therefore designed to investigate the value of contrast-enhanced ultrasound features in the differential diagnosis of thyroid nodules with halo sign on B-mode ultrasound.Material and methodsSeventy-four consecutive thyroid nodules with halo sign on B-mode ultrasound were pathologically confirmed by surgery or fine needle aspiration, including 43 benign and 31 malignant lesions. All these lesions underwent pre-operative CEUS examination. The CEUS features, including enhanced time, enhanced intensity and homogeneity, and presence of enhancing ring, were compared between benign and malignant ones.ResultsEnhanced intensity was significant different between benign and malignant lesions with halo. Hypo-enhancement was more frequently detected in malignant nodules than that in benign ones, compared with iso-enhancement and hyper-enhancement (p = 0.013, and = 0.014, respectively). Detection rate of high-enhancing ring was significantly higher in benign nodules than that in malignant group (p = 0.001). While in nodules > 10 mm, only high-enhancing ring was the distinguishing feature between benign and malignant nodules.ConclusionsEnhanced intensity and high-enhancing ring may be helpful in the differential diagnosis of thyroid nodules with halo sign on B-mode ultrasound.

Accuracy of American College of Radiology Thyroid Imaging Reporting and Data System in Characterisation of Thyroid Nodules: A Tertiary Hospital Study in India

Background: Thyroid nodules (TNs) are commonly and frequently encountered in the day-to-day clinical practice. Clinical suspicion of malignancy as well as patient anxiety is the frequent reasons for fine-needle aspiration cytology or biopsy (FNAC/B) of TN. American College of Radiology has developed a Thyroid Imaging Reporting and Data System (ACR TI-RADS) based on TN ultrasound features to suggest malignancy to minimise the number of TN subjected to FNAC/B. Aim to assess the diagnostic accuracy of ACR TI-RADS in our Tertiary Hospital Health Facility. Patients, Materials and Methods: Sixty-nine patients with TN included in our study following institutional ethics committee approval and written informed consent underwent high-resolution ultrasonography followed by FNAC using a high-frequency linear transducer on Siemen Acuson S3000 ultrasound scanner. The data were recorded in the predesigned proforma followed by statistical analysis to assess the diagnostic accuracy of ACR TI-RADS. Results: Our study had female predominance (50/69) with the left side more commonly affected (37/69). The mean age of the participants was 42.2 ± 15.6 years with significantly higher age at presentation in those with malignant TN. Mixed nodules were predominant (46/69) with nearly half of the malignant nodules being solid and only 8/61 benign nodules being solid. Although the majority of the nodules were hyperechoic (50/69 including 2 malignant), a significant number of the hypoechoic/very hypoechoic nodules (6/14) were malignant with only 8/61 benign nodules being hypoechoic. Half of the malignant nodules (4/8) had lobulated or ill-defined margins, whereas only 2/61 benign nodules had similar margins. The majority (5/8) of the malignant TN had internal punctate echogenic foci with only 1/61 benign TN showing similar appearance. Half of the malignant TN (4/8) had lymphadenopathy, whereas it was noted in only 2/61 cases of benign TN. Conclusions: TNs though have a high prevalence, but malignant TNs are uncommon with majority being solid, taller than wider, hypoechoic with microcalcifications. The presence of adenopathy is usually a feature of malignant thyroid nodule rarely seen with benign nodules. Thus, to summarise all TNs should be thoroughly evaluated as per ACR TI-RADS for stratification of risk of malignancy.

Role of Diffusion Weighted MRI, Sonography in Characterization of Thyroid Nodules Guided by TIRADS Classification

Role of Diffusion Weighted MRI, Sonography in Characterization of Thyroid Nodules Guided by TIRADS Classification

Automatic classification of thyroid nodules in ultrasound images using a multi-task attention network guided by clinical knowledge

Thyroid cancer has been the most prevalent cancer in the recent three decades. Ultrasonography is one of the mainly used methods for diagnosing thyroid nodules. Several computer-aided diagnostic methods were proposed to aid radiologists in analyzing ultrasound images of the thyroid gland. Most methods, however, only determine the benignity or malignancy of the thyroid nodule and do not explain the decision-making process of them, which cannot gain the trustworthiness of clinicians because they are not consistent with the physician’s diagnostic process. In our work, we design a multi-task branching attention network in which each of the descriptors of the ACR TI-RADS lexicon is first classified. All respective scores are calculated to get the risk stratification of the nodule. Ultimately, based on the risk stratification, the benignity or malignancy of the nodule is determined. This work provides an automated method that incorporates the ACR TI-RADS characterization of thyroid nodules for detecting the level of risk and the benignity or malignancy of thyroid nodules. Thus the work establishes the trustworthiness of clinicians in deep learning models and improves physician efficiency and diagnostic rates to some extent compared to previous studies. For the diagnosis of thyroid nodules, evaluation indices including accuracy, sensitivity, and specificity were 93.55%, 93.86%, and 93.14%, respectively. The experiments show that our approach obtains comparable performance to most advanced methods in diagnosing ultrasound images of the thyroid nodules and is supported by explanations in clinical terms using the ACR TI-RADS lexicon.

Diagnostic Performance of Artificial Intelligence for Interpreting Thyroid Cancer in Ultrasound images

Thyroid ultrasonography is mainly used for the detection and characterization of thyroid nodules. However, there is some limitation since the diagnostic performance remains highly subjective and depends on radiologist experiences. Therefore, artificial intelligence (AI) was expected to improve the diagnostic performance of thyroid ultrasound. To evaluate the diagnostic performance of the AI for differentiating malignant and benign thyroid nodules and compare it with that of an experienced radiologist and a third-year diagnostic radiology resident, 648 patients with 650 thyroid nodules, who underwent thyroid ultrasound guided-FNA biopsy and had a decisive diagnosis from FNA cytology at Siriraj Hospital between January 2014 and June 2020, were enrolled. Although the specificity and accuracy were slightly higher in AI than the experienced radiologist and the resident (specificity 78.85% vs. 67.31% vs. 69.23%; accuracy 78.46% vs. 70.77% vs. 70.77%, respectively), the AI showed comparable diagnostic sensitivity and specificity to the experienced radiologist and the resident (p=0.187-0.855).

Review of Deep Learning Approaches for Thyroid Cancer Diagnosis

Thyroid nodule is one of the common life-threatening diseases, and it had an increasing trend over the last years. Ultrasound imaging is a commonly used diagnostic method for detecting and characterizing thyroid nodules. However, assessing the entire slide images is time-consuming and challenging for the experts. For assessing ultrasound images in a meaningful manner, there is a need for automated, trustworthy, and objective approaches. The recent advancements in deep learning have revolutionized many aspects of computer-aided diagnosis (CAD) and image analysis tools that address the problem of diagnosing thyroid nodules. In this study, we explained the objectives of deep learning in thyroid cancer imaging and conducted a literature review on its potential, limits, and current application in this area. We gave an overview of recent progress in thyroid cancer diagnosis using deep learning methods and discussed various challenges and practical problems that might limit the growth of deep learning and its integration into clinical workflow.

SP2.2.2 Thyroid Ultrasound reporting – an audit of practice

Abstract Aims Ultrasound (US) is the first line investigation for evaluating thyroid nodules. British Thyroid Association (BTA) guidelines set out a structured characterisation of thyroid nodules to guide further investigation. We conducted a closed loop audit at a District General Hospital to assess compliance of US reporting with BTA guidelines. Methods 208 examinations were retrospectively analysed in the initial audit cycle. The reports were assessed for U status; number, measurements, characteristics and locations of nodules; comment on lymph nodes; whether FNA was performed or recommended; FNA histology; and the number of examinations performed by each radiologist/radiographer. Departmental teaching was then provided. A re-audit of 100 thyroid ultrasounds was then conducted. Results In the initial audit and re-audit, respectively, the number of reporting clinicians decreased from 21 to 15, however the vast majority had reported fewer than 5 scans in each time period. Where nodules were present, the use of U classification in reports increased from 32.5% to 85.3%. Reporting of measurements, characteristics and location of nodules increased from 86.4%, 92.9% and 96.8% to 96.0%, 100.0% and 100.0% of reports, respectively. In both audits, 4.5% of U1/2 nodules were recommended for FNA. For U3–5 nodules, FNA or referral to specialty increased from 71.4% to 75.0%. Comment on lymph nodes increased from 89.4% to 95.9%. Conclusions Following education, improvement in compliance was seen in nearly all domains, particularly in the utilisation of U classification. Further work is needed regarding the appropriate use of FNA, and caseload variation between clinicians.

Role of the American College Of Radiology –Thyroid Imaging Reporting and Data System (ACR TI-RADS) in Differentiating Benign and Malignant Thyroid Nodules

Introduction: Thyroid nodule is commonly detected while performing an ultrasound of the neck. American College of Radiology, Thyroid imaging reporting and data system (ACR-TIRADS) is a classification used to characterize thyroid nodules based on ultrasound findings. The objective of this study is to evaluate the role of ACR-TIRADS classification in differentiating benign and malignant thyroid nodules.&#x0D; Methods: This is a prospective study conducted at Manipal Teaching Hospital. A total of 70 patients presenting to our radiology department for fine needle aspiration cytology (FNAC) of thyroid nodule were enrolled in the study. Thyroid nodules were grouped as 1- 5 according to the ACR-TIRADS. The ACR-TIRADS findings were correlated with FNAC and or biopsy findings as available. Subsequently; sensitivity, specificity, accuracy, positive predictive value and negative predictive value of ACR-TIRADS in differentiating benign from malignant nodules were assessed.&#x0D; Results: Mean age of patients in our study was 52.16 years (18-89 years). Male patients were 11 (15.7 %) and female were 59 (84.30 %). According to Ultrasonography; 1.4% of thyroid nodules were labelled as ACR-TIRADS 1; 21.4% as ACR-TIRADS 2; 32.85% as ACR- TIRADS 3; 28.6 % as ACR-TIRADS 4 and 15.7 % as ACR-TIRADS 5. ROC curve analysis showed AUC of 0.78 (95% CI). ACR-TRIADS had sensitivity, specificity, positive predictive value, negative predictive value, diagnostic accuracy of 87.5%, 68.5 %, 45.16 %, 94.87 %, 72.86 % respectively.&#x0D; Conclusion: ACR- TIRADS has high sensitivity and accuracy in differentiating benign and malignant thyroid nodules.

Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing?

Simple SummaryIn the present review, an up-to-date summary of the state of the art of artificial intelligence (AI) implementation for thyroid nodule characterization and cancer is provided. The opinion on the real effectiveness of AI systems remains controversial. Taking into consideration the largest and most scientifically valid studies, it is possible to state that AI provides results that are comparable or inferior to expert ultrasound specialists and radiologists. Promising data approve AI as a support tool and simultaneously highlight the need for a radiologist supervisory framework for AI provided results. Therefore, current solutions might be more suitable for educational purposes.Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.

Advanced imaging and theranostics in thyroid cancer.

Thyroid cancers are endocrine neoplasms with diverse gene expression and behavior, for which constantly evolving anatomic and functional imaging/theranostic agents have an essential role for diagnosis, staging, and treatment. To achieve definitive diagnosis, neck ultrasound and associated risk stratification systems, notably Thyroid Imaging Reporting and Data System (TI-RADS), allow improved thyroid nodule characterization and management guidance. Radioactive iodine-131 (RAI) has long played a role in management of differentiated thyroid cancer (DTC), with recent literature emphasizing its effectiveness for intermediate-high risk cancers, exploring use of dosimetry for personalized medicine, and potential for retreatment with RAI following tumor redifferentiation. Iodine-124 positron emission tomography/computed tomography (PET/CT) has promising application for DTC staging and dosimetry. F18-fluorodeoxyglucose (FDG) PET/CT is used for staging of high risk DTC and identification of noniodine-avid disease recurrences, with metabolic uptake consistently portending poor prognosis. Poorly differentiated and anaplastic thyroid cancers are best assessed with anatomic imaging and F18-FDG PET/ CT, though recent studies show a potential theranostic role for Ga68/Lu177-prostate-specific membrane antigen. Medullary thyroid cancers are evaluated with ultrasound, CT, magnetic resonance imaging, and various positron-emitting radiotracers for PET imaging (F18-DOPA, F18-FDG, and recently Ga68-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA)-octreotate (DOTATATE)); the latter may enable treatment with Lu177-DOTATATE. Multidisciplinary collaboration is essential to streamline appropriate management, given the wide array of available imaging and new therapies for metabolic and genetically complex cancers.

Comparative Study of TI-RADS, Elasticity Score, Strain Ratio and Quantitative Diffusion MR Weighted Imaging in Characterization of Thyroid Nodules; Which Has the Best Diagnostic performance?

Comparative Study of TI-RADS, Elasticity Score, Strain Ratio and Quantitative Diffusion MR Weighted Imaging in Characterization of Thyroid Nodules; Which Has the Best Diagnostic performance?