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

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

A Review of Thyroid Disorder Detection Using Medical Images

Thyroid is one of the largest endocrine gland. It is a small butterfly shaped gland which is located in the front portion of the neck.It is located just below the Adams apple. Thyroid gland produces hormones that help the body to control metabolism. There are various thyroid disorders.It includes Hyperthyroidism, Hypothyroidism, goiter and thyroid nodules (benign/malignant). is most commonly. Various modalities that are used to detect and classify abnormalities of the thyroid gland are Ultrasoundimaging,(ComputerTomography(CT), Magnetic Resonance Imaging(MRI). Computer Aided Diagnosis (CAD) help radiologists and doctors to increase the diagnosis accuracy, reduce biopsy ratio and save their time and effort. Thyroid medical images are utilized for the diagnosis process

False-Positive Malignant Diagnosis of Nodule Mimicking Lesions by Computer-Aided Thyroid Nodule Analysis in Clinical Ultrasonography Practice

This study aims to test computer-aided diagnosis (CAD) for thyroid nodules in clinical ultrasonography (US) practice with a focus towards identifying thyroid entities associated with CAD system misdiagnoses. Two-hundred patients referred to thyroid US were prospectively enrolled. An experienced radiologist evaluated the thyroid nodules and saved axial images for further offline blinded analysis using a commercially available CAD system. To represent clinical practice, not only true nodules, but mimicking lesions were also included. Fine needle aspiration biopsy (FNAB) was performed according to present guidelines. US features and thyroid entities significantly associated with CAD system misdiagnosis were identified along with the diagnostic accuracy of the radiologist and the CAD system. Diagnostic specificity regarding the radiologist was significantly (p < 0.05) higher than when compared with the CAD system (88.1% vs. 40.5%) while no significant difference was found in the sensitivity (88.6% vs. 80%). Focal inhomogeneities and true nodules in thyroiditis, nodules with coarse calcification and inspissated colloid cystic nodules were significantly (p < 0.05) associated with CAD system misdiagnosis as false-positives. The commercially available CAD system is promising when used to exclude thyroid malignancies, however, it currently may not be able to reduce unnecessary FNABs, mainly due to the false-positive diagnoses of nodule mimicking lesions

Cost-Effective and Non-Invasive Automated Benign & Malignant Thyroid Lesion Classification in 3D Contrast-Enhanced Ultrasound Using Combination of Wavelets and Textures: A Class of ThyroScan™ Algorithms

Ultrasound has great potential to aid in the differential diagnosis of malignant and benign thyroid lesions, but interpretative pitfalls exist and the accuracy is still poor. To overcome these difficulties, we developed and analyzed a range of knowledge representation techniques, which are a class of ThyroScan™ algorithms from Global Biomedical Technologies Inc., California, USA, for automatic classification of benign and malignant thyroid lesions. The analysis is based on data obtained from twenty nodules (ten benign and ten malignant) taken from 3D contrast-enhanced ultrasound images. Fine needle aspiration biopsy and histology confirmed malignancy. Discrete Wavelet Transform (DWT) and texture algorithms are used to extract relevant features from the thyroid images. The resulting feature vectors are fed to three different classifiers: K-Nearest Neighbor (K-NN), Probabilistic Neural Network (PNN), and Decision Tree (DeTr). The performance of these classifiers is compared using Receiver Operating Characteristic (ROC) curves. Our results show that combination of DWT and texture features coupled with K-NN resulted in good performance measures with the area of under the ROC curve of 0.987, a classification accuracy of 98.9%, a sensitivity of 98%, and a specificity of 99.8%. Finally, we have proposed a novel integrated index called Thyroid Malignancy Index (TMI), which is made up of texture features, to diagnose benign or malignant nodules using just one index. We hope that this TMI will help clinicians in a more objective detection of benign and malignant thyroid lesions

Thyroid Nodule Image Analysis using Morphological Segmentation

Computer-aided investigative processing has become an important part of medical practice. New growth of high expertise and use of a choice of imaging modalities, more confront arise so that high rate information can be produced for disease finding and behavior. Ultrasonography of Thyroid gland is the most common, portable, widely accessible, cheap, painless and secure. It is used to distinct the thyroid nodule images that are classified into two categories: (i) benign thyroid ample, (ii) malignant lump of thyroid gland. In this paper, Mathematical Morphology is used to segment the thyroid region and measure the area, perimeter, width and height of the thyroid area. Thyroid nodule images are taken from twenty peoples as samples.Keywords— Thyroid, Morphological operation, Ultrasound, Segmentation, Tumo