Interobserver agreement of Thyroid Imaging Reporting and Data System (TIRADS) and strain elastography for the assessment of thyroid nodules

Background: Thyroid Imaging Reporting and Data System (TIRADS) was developed to improve patient management and cost-effectiveness by avoiding unnecessary fine needle aspiration biopsy (FNAB) in patients with thyroid nodules. However, its clinical use is still very limited. Strain elastography (SE) enables the determination of tissue elasticity and has shown promising results for the differentiation of thyroid nodules. Methods: The aim of the present study was to evaluate the interobserver agreement (IA) of TIRADS developed by Horvath et al. and SE. Three blinded observers independently scored stored images of TIRADS and SE in 114 thyroid nodules (114 patients). Cytology and/or histology was available for all benign (n = 99) and histology for all malignant nodules (n = 15). Results: The IA between the 3 observers was only fair for TIRADS categories 2–5 (Coheńs kappa = 0.27,p = 0.000001) and TIRADS categories 2/3 versus 4/5 (ck = 0.25,p = 0.0020). The IA was substantial for SE scores 1–4 (ck = 0.66,p<0.000001) and very good for SE scores 1/2 versus 3/4 (ck = 0.81,p<0.000001). 92–100% of patients with TIRADS-2 had benign lesions, while 28–42% with TIRADS-5 had malignant cytology/histology. The negative-predictive-value (NPV) was 92–100% for TIRADS using TIRADS-categories 4&5 and 96–98% for SE using score ES-3&4 for the diagnosis of malignancy, respectively. However, only 11–42% of nodules were in TIRADS-categories 2&3, as compared to 58–60% with ES-1&2. Conclusions: IA of TIRADS developed by Horvath et al. is only fair. TIRADS and SE have high NPV for excluding malignancy in the diagnostic work-up of thyroid nodules

Interobserver agreement of various thyroid imaging reporting and data systems

Ultrasonography is the best available tool for the initial work-up of thyroid nodules. Substantial interobserver variability has been documented in the recognition and reporting of some of the lesion characteristics. A number of classification systems have been developed to estimate the likelihood of malignancy: several of them have been endorsed by scientific societies, but their reproducibility has yet to be assessed. We evaluated the interobserver variability of the AACE/ACE/AME, ACR, ATA, EU-TIRADS, and K-TIRADS classification systems and the interobserver concordance in the indication to FNA biopsy. Two raters independently evaluated 1055 ultrasound images of thyroid nodules identified in 265 patients at multiple time points, in two separate sets (501 and 554 images). After the first set of nodules, a joint reading was performed to reach a consensus in the feature definitions. The interobserver agreement (Krippendorff alpha) in the first set of nodules was 0.47, 0.49, 0.49, 0.61, and 0.53, for AACE/ACE/AME, ACR, ATA, EU-TIRADS, and K-TIRADS systems, respectively. The agreement for the indication to biopsy was substantial to near-perfect, being 0.73, 0.61, 0.75, 0.68, and 0.82, respectively (Cohen's kappa). For all systems, agreement on the nodules of the second set increased. Despite the wide variability in the description of single ultrasonographic features, the classification systems may improve the interobserver agreement, that further ameliorates after a specific training. When selecting nodules to be submitted to FNA biopsy, that is main purpose of these classifications, the interobserver agreement is substantial to almost perfect

Segmentation of ultrasound images of thyroid nodule for assisting fine needle aspiration cytology

The incidence of thyroid nodule is very high and generally increases with the age. Thyroid nodule may presage the emergence of thyroid cancer. The thyroid nodule can be completely cured if detected early. Fine needle aspiration cytology is a recognized early diagnosis method of thyroid nodule. There are still some limitations in the fine needle aspiration cytology, and the ultrasound diagnosis of thyroid nodule has become the first choice for auxiliary examination of thyroid nodular disease. If we could combine medical imaging technology and fine needle aspiration cytology, the diagnostic rate of thyroid nodule would be improved significantly. The properties of ultrasound will degrade the image quality, which makes it difficult to recognize the edges for physicians. Image segmentation technique based on graph theory has become a research hotspot at present. Normalized cut (Ncut) is a representative one, which is suitable for segmentation of feature parts of medical image. However, how to solve the normalized cut has become a problem, which needs large memory capacity and heavy calculation of weight matrix. It always generates over segmentation or less segmentation which leads to inaccurate in the segmentation. The speckle noise in B ultrasound image of thyroid tumor makes the quality of the image deteriorate. In the light of this characteristic, we combine the anisotropic diffusion model with the normalized cut in this paper. After the enhancement of anisotropic diffusion model, it removes the noise in the B ultrasound image while preserves the important edges and local details. This reduces the amount of computation in constructing the weight matrix of the improved normalized cut and improves the accuracy of the final segmentation results. The feasibility of the method is proved by the experimental results.Comment: 15pages,13figure

Grey-scale analysis improves the ultrasonographic evaluation of thyroid nodules

Ultrasonography is the main imaging method for the workup of thyroid nodules. However, interobserver agreement reported for echogenicity and echotexture is quite low. The aim of this study was to perform quantitative measurements of the degree of echogenicity and heterogeneity of thyroid nodules, to develop an objective and reproducible method to stratify these features to predict malignancy.A retrospective study of patients undergoing ultrasonography-guided fine-needle aspiration was performed in an University hospital thyroid center. From January 2010 to October 2012, 839 consecutive patients (908 nodules) underwent US-guided fine-needle aspiration. In a single ultrasound image, 3 regions of interest (ROIs) were drawn: the first including the nodule; the second including a portion of the adjacent thyroid parenchyma; the third, the strap muscle. Histogram analysis was performed, expressing the median, mean, and SD of the gray levels of the pixels comprising each region. Echogenicity was expressed as a ratio: the nodule/parenchyma, the nodule/muscle, and parenchyma/muscle median gray ratios were calculated. The heterogeneity index (HI) was calculated as the coefficient of variation of gray histogram for each of the 3 ROIs. Cytology and histology reports were recorded.Nodule/parenchyma median gray ratio was significantly lower (more hypoechoic) in nodules found to be malignant (0.45 vs 0.61; P = 0.002) and can be used as a continuous measure of hypoechogenicity (odds ratio [OR] 0.12; 95% confidence interval [CI] 0.03-0.49). Using a cutoff derived from ROC curve analysis (<0.46), it showed a substantial inter-rater agreement (k = 0.74), sensitivity of 56.7% (95% CI 37.4-74.5%), specificity of 72.0% (67.8-75.9%), positive likelihood ratio (LR) of 2.023 (1.434-2.852), and negative LR of 0.602 (0.398-0.910) in predicting malignancy (diagnostic odds ratio 3.36; 1.59-7.10). Parenchymal HI was associated with anti-thyroperoxidase positivity (OR 19.69; 3.69-105.23). The nodule HI was significantly higher in malignant nodules (0.73 vs 0.63; P = 0.03) and, if above the 0.60 cutoff, showed sensitivity of 76.7% (57.7-90.1%), specificity of 46.8% (42.3-51.4%), positive LR of 1.442 (1.164-1.786), and negative LR of 0.498 (0.259-0.960).Evaluation of nodule echogenicity and echotexture according to a numerical estimate (nodule/parenchyma median gray ratio and nodule HI) allows for an objective stratification of nodule echogenicity and internal structure

Piezoelectric needle sensor reveals mechanical heterogeneity in human thyroid tissue lesions.

Palpable thyroid lesions are common, and although mostly benign, lethal malignant nodules do occur and may be difficult to differentiate. Here, we introduce the use of a piezoelectric system called Smart-touch fine needle (or STFN) mounted directly onto conventional biopsy needles, to evaluate abnormal tissues, through quantitative real-time measurements of variations in tissue stiffness as the needle penetrates tissue. Using well-characterized biomaterials of known stiffness and explanted animal tissue models, we first established experimental protocols for STFN measures on biological tissues, as well as optimized device design for high signal-to-noise ratio. Freshly excised patient thyroids with varying fibrotic and malignant potential revealed discrete variations in STFN based tissue stiffness/stiffness heterogeneity and correlated well with final histopathology. Our piezoelectric needle sensor reveals mechanical heterogeneity in thyroid tissue lesions and provides a foundation for the design of hand-held tools for the rapid, mechano-profiling of malignant lesions in vivo while performing fine needle aspiration (FNA)

ENDOCRINE TUMOURS: Imaging in the follow up of differentiated thyroid cancer: current evidence and future perspectives for a risk-adapted approach

The clinical and epidemiological profiles of differentiated thyroid cancers (DTCs) have changed in the last three decades. Today's DTCs are more likely to be small, localized, asymptomatic papillary forms. Current practice is though moving towards more conservative approaches (e.g. lobectomy instead of total thyroidectomy, selective use of radioiodine). This evolution has been paralleled and partly driven by rapid technological advances in the field of diagnostic imaging. The challenge of contemporary DTCs follow up is to tailor a risk-of-recurrence-based management, taking into account the dynamic nature of these risks, which evolve over time, spontaneously and in response to treatments. This review provides a closer look at the evolving evidence-based views on the use and utility of imaging technology in the post-treatment staging and the short- and long-term surveillance of patients with DTCs. The studies considered range from cervical US with Doppler flow analysis to an expanding palette of increasingly sophisticated second-line studies (cross-sectional, functional, combined-modality approaches), which can be used to detect disease that has spread beyond the neck and, in some cases, shed light on its probable outcome. 

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

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