Enter the matrix:On how to improve thyroid nodule management using 3D ultrasound

Roughly two-thirds of the adult population has a thyroid nodule, of which 90% are benign. Of the adults that have a nodule, approximately 5% will experience symptoms that include a feeling of a marble stuck in the throat, difficulty swallowing and breathing, and cosmetic complaints. Thyroid nodule management primarily makes use of ultrasound as the imaging modality for diagnosis, image guidance during therapy (radiofrequency ablation i.e. RFA), and follow-up. Although ultrasound is relatively easy to apply, it is hard to standardize for repeated measurements and across various users. Further, RFA can benefit from 3D imaging information and a planning and navigation system to improve clinical outcome. These challenges may be overcome by using 3D ultrasound. In this thesis, two phantoms were created on which these methods can be developed. Further, it offers insight into the use of 2D and 3D ultrasound for thyroid nodule management.To assess the impact of changes to an intervention, a baseline was determined of the effectiveness of RFA in Dutch hospitalsUsing a simple phantom, we have shown that utilizing a volume-based measurement technique, that the matrix transducer offers, results in improved measurement accuracy. The more complex, anthropomorphic, phantom serves as a platform on which thermal treatments, such as RFA, can be improved. Using this phantom, we have shown that the impact of 2D and 3D ultrasound on RFA efficacy does not differ from one another; however, the matrix transducer might be more user-friendly for needle placement due to the dual-plane imaging. An additional use case for these phantoms is their capacity to compare dominant and non-dominant hand ablations, as well as serve as a training platform. Additional research is required that employs more operators to find stronger evidence supporting a difference between the ablating hands and the difference in effect of 2D and 3D ultrasound guidance.To make full use of 3D ultrasound, stitching algorithms should be integrated into the ultrasound systems to acquire larger volumes. These can then be processed by deep-learning algorithms for use in computer-aided diagnosis and intervention systems. To further improve the applicability of 3D ultrasound in the clinic, integrating analysis methods such as 3D elastography and 3D Doppler is suggested

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

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of “radiomics and genomics” has been considered under the umbrella of “radiogenomics”. Furthermore, AI in a radiogenomics’ environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor’s characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them

Arterial mechanical motion estimation based on a semi-rigid body deformation approach

Arterial motion estimation in ultrasound (US) sequences is a hard task due to noise and discontinuities in the signal derived from US artifacts. Characterizing the mechanical properties of the artery is a promising novel imaging technique to diagnose various cardiovascular pathologies and a new way of obtaining relevant clinical information, such as determining the absence of dicrotic peak, estimating the Augmentation Index (AIx), the arterial pressure or the arterial stiffness. One of the advantages of using US imaging is the non-invasive nature of the technique unlike Intra Vascular Ultra Sound (IVUS) or angiography invasive techniques, plus the relative low cost of the US units. In this paper, we propose a semi rigid deformable method based on Soft Bodies dynamics realized by a hybrid motion approach based on cross-correlation and optical flow methods to quantify the elasticity of the artery. We evaluate and compare different techniques (for instance optical flow methods) on which our approach is based. The goal of this comparative study is to identify the best model to be used and the impact of the accuracy of these different stages in the proposed method. To this end, an exhaustive assessment has been conducted in order to decide which model is the most appropriate for registering the variation of the arterial diameter over time. Our experiments involved a total of 1620 evaluations within nine simulated sequences of 84 frames each and the estimation of four error metrics. We conclude that our proposed approach obtains approximately 2.5 times higher accuracy than conventional state-of-the-art techniques.The authors thank Ana Palomares for revising their English text. This work has been supported by the National Grant (AP2007-00275), the projects ARC-VISION (TEC2010-15396), ITREBA (TIC-5060), and the EU project TOMSY (FP7-270436)

Preoperative localisation of parathyroid adenoma in primary hyperparathyroidism using 99mTc-sestamibi SPECT/CT : an evolving scanning protocol

Primary hyperparathyroidism (pHPT) is caused by one or more hyperfunctional parathyroid gland causing an inappropriately high release of parathyroid hormone (PTH) in relation to the calcium concentration in the blood. PTH acts on the bones to release more calcium and on the kidneys to reabsorb calcium, causing hypercalcemia. Approximately 75% of the patients are women and median age is 62. The only permanent cure is surgical removal of all pathologic parathyroid glands. To minimise the surgical exploration preoperative imaging localisation methods, have for decades been used and refined to pinpoint the culprit gland(s). The performance data for different imaging modalities used for preoperative localisation of hyperfunctional parathyroid glands are difficult to interpret. There are large numbers of studies on different methods with varying protocols and quality, often with insufficient reporting on important influencing factors such as adenoma weight and frequency of multiglandular disease (MGD). In this thesis we have analysed the performance of dual timepoint 99mTc-sestamibi SPECT/CT for preoperative localisation of PTAs with regards to its individual components: 99mTc-sestamibi SPECT alone [S], nonenhanced CT (native phase) [N], contrast-enhanced CT (arterial- and venous phase), [A] and [V] respectively and in combination [AN], [VN], [SN], [ANS], [VNS] and [SNAV]. Additionally, the impact of the adenoma weight and MGD on PTA localisation was also investigated. In Study I we retrospectively analysed 249 patients examined with nonenhanced 99mTcsestamibi SPECT/CT and found that adding a diagnostic native phase to 99mTc-sestamibi SPECT significantly increased the localisation specificity from 93.5% to 95.9% (p<0.01), but not the sensitivity. In a prospective examination of 192 patients (Study II) we reported that adding an arterial and venous phase to nonenhanced SPECT/CT [SN] significantly increased the localisation sensitivity from 81.1% to 89.9% (p<0.01) without changing the specificity. Using the same cohort, in Study III we showed that adding 99mTc-sestamibi SPECT to different combinations of CT phases increased sensitivity e.g., 80.8% for [AN] as compared to 86.5% for [ANS] (p<0.01). However, the use of both contrast-enhanced phases was found redundant in terms of sensitivity gain, just adding 4 extra mSv. The specificity was 97.9% for both. Although small parathyroid adenomas are known to be a challenge in preoperative localisation, we showed that it could be overcome using [ANS] or [SNAV]. The performance in patients with MGD remained unsatisfactory for all image sets, with a per-patient sensitivity of merely 30-40%. As a way of mitigating the consequences of this, in Study IV we trained a Machine Learning Classifier to recognise cases were preoperative localisation misclassified patients with MDG as single gland disease (SGD). As predictors, we used a set of pHPT related biochemical variables and the measured adenoma weight on patients cured after parathyroidectomy. On test data, the current classifier reached a 72% true positive prediction rate for MGD-patients and a misclassification rate of 6% for SGD-patients. These results call for further exploration before clinical implementation