Risk Stratification of Thyroid Nodule: From Ultrasound Features to TIRADS

Since the 1990s, ultrasound (US) has played a major role in the assessment of thyroid nodules and their risk of malignancy. Over the last decade, the most eminent international societies have published US-based systems for the risk stratification of thyroid lesions, namely, Thyroid Imaging Reporting And Data Systems (TIRADSs). The introduction of TIRADSs into clinical practice has significantly increased the diagnostic power of US to a level approaching that of fine-needle aspiration cytology (FNAC). At present, we are probably approaching a new era in which US could be the primary tool to diagnose thyroid cancer. However, before using US in this new dominant role, we need further proof. This Special Issue, which includes reviews and original articles, aims to pave the way for the future in the field of thyroid US. Highly experienced thyroidologists focused on US are asked to contribute to achieve this goal

Diagnostic accuracy of Acoustic Radiation Force Impulse (ARFI) in differentiating benign from malignant thyroid nodules in patients with solitary thyroid nodules and comparison with histopathology

OBJECTIVES: To assess the value of shear wave elastography (SWE) using ARFI technology in differentiating benign and malignant thyroid nodules METHODS: IRB approved prospective study in a 2800 bedded tertiary care teaching hospital. Ultrasound, shear wave velocity (SWV) measurements were obtaining by virtual touch quantification (VTQ) and virtual touch imaging (VTI) using ARFI technology on patients with solitary thyroid nodule, one or two dominant nodule of multinodular goiter >1cm. These were compared with cytology and surgical histopathology. Diagnostic performance of SWV measurement, VTI and conventional ultrasound were compared. RESULTS: Of 193 patients with 217 thyroid nodules, 153 patients (37 males, 166 females; age 16-82 years) with 172 nodules were included. There was significant difference in the mean SWV between benign (2.18+/-1.35 [95% CI=1.874-2.512] m/s) and malignant (3.97+/-2.65 [95% CI=3.43-4.503] m/s) nodules, p<0.001. There is significant difference in the elasticity score obtained by VTI between benign and malignant nodules (chi-square =70.522, p < 0.001). Sensitivity, specificity, PPV, NPV, accuracy of VTI was 82.8%, 79.0%, 84.5%, 77%, and 81.2% respectively and that of VTQ at a cut off SWV of 2.87 m/s was 82.5%, 57.1%, 53.6%, 84.5% and 65.5% respectively. Diagnostic performance of VTI (AUC = 0.849) and combined VTI+VTQ (AUC= 0.831) was better than VTQ alone, conventional ultrasound and combined criteria (conventional ultrasound + VT I + VTQ); AUC was 0.699, 0.682 and 0.727 respectively

Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images

Raman Microspectroscopy for the Discrimination of Thyroid and Lung Cancer Subtypes for Application in Clinical Cytopathology

The branch of cytology known as cytopathology, studies and diagnoses diseases at a cellular level, and is a useful method for detecting cancer. The procedures used to attain cytological samples for diagnostic purposes, such as aspiration and exfoliative methods are safe, accurate and cost-effective. Histochemical and immunohistochemical (IHC) techniques are commonly applied to cytological samples to aid cancer diagnosis, however multiple limitations occur using these methods for the diagnosis of thyroid cancer (TC) and non-small cell lung cancer (NSCLC). Fine needle aspiration cytology (FNAC) is the prominent diagnostic method used for the initial investigation of thyroid nodules but is limited by the inability to accurately diagnose malignancy in follicular-patterned lesion. As a result, more than 20% of cases under investigation for TC are classified as cytologically “indeterminate”, requiring surgical resection for accurate diagnosis. In the case of NSCLC, with the advent of targeted therapies, it is imperative to accurately differentiate (NSCLC) subtypes in order to ensure efficacy of treatment for patients. Immunohistochemistry and molecular techniques are increasingly part of the diagnostic work-up of NSCLC patients however due to the limitation of small sample size, overlapping morphological features and molecular characterisation, differential diagnosis of NSCLC still proves challenging. Raman spectroscopy has shown promising results for the detection of a variety of cancers, however to date there has been no evaluation of Raman spectroscopy on cytology bronchoscopy samples or thyroid FNAC samples, which may eliminate the limitations of current methods. This thesis explores the use of Raman spectroscopy as an alternative or adjunct tool for the diagnosis of TC and NSCLC using cytological specimens

Nodal staging in head and neck squamous cell carcinoma by combining different imaging techniques

Head and neck cancer accounts for around 4% of all malignancies. The presence of cervical lymph node metastases will reduce expected survival with approximately 50%. Therapy should thus be as effective as possible with a minimum of therapeutic side effects and depends next to tumour size on the presence or absence of nodal metastases. Physical examination and imaging with magnetic resonance imaging (MRI), positron emission tomography – computed tomography (PET-CT), ultrasound (US), ultrasound guided fine needle aspiration (USgFNAC) are commonly used to examine cervical lymph node metastases, but in 30% of the cases, they are still overlooked. New imaging techniques such as real time image fusion of ultrasound and PET-CT and micro flow imaging (MFI) are thus developed. The aim of this thesis was to improve the detection rate of lymph node metastases by improving the selection criteria of nodes for ultrasound guided fine needle aspiration to prove cytological malignancy.<br/

Diagnosis and Management of Pediatric Diseases

A screenshot of some the most rapidly evolving fields in Neonatology and Pediatrics with articles reviewing some metabolic dysregulations as well as non-oncologic diseases that may occur in infancy, childhood, youth. The illustrative material with original photographs and drawings highlighting some pathogenetic concepts are keystones of this book

Novel Methods of Diagnostics of Thyroid and Parathyroid Lesions

Thyroid nodular disease is one of the most frequent endocrine diseases. The prevalence of thyroid focal lesions detected by imaging techniques, according to studies on different populations, ranges from 10 to 70%. In a population of women over 50 years of age, approximately half of them will have a thyroid focal lesion. However, only 18% of thyroid nodules are diagnosed as malignant. Thyroid nodular disease is the most frequently diagnosed endocrine pathology, while thyroid cancer constitues the most common endocrine malignancy and is reponsible for about 67% of deaths due to neoplasms derived from endocrine organs. The incidence of thyroid cancer has risen by about 240% in the last three decades. Due to the increased availability of imaging techniques, recently, a rise in the detectability of thyroid cancer at the stage of microcarcinoma has been observed. Diagnostic and therapeutic decisions in patients with thyroid nodules require an interdisciplinary consensus between endocrinologists and physicians of other specialities (radiologists, pathologists, surgeons, oncologists). This book focuses on current trends in novel techniques of thyroid nodule diagnostics before they are implemented in the current guidelines on the management of thyroid nodular disease

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Background: The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs. Methods: PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study. Results: ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy. Conclusion: Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial design