Thyroid cancer diagnosis in the era of precision imaging

Spatial-Temporal Information Fusion for Thyroid Nodule Segmentation in Dynamic Contrast-Enhanced MRI: A Novel Approach

This study aims to develop a novel segmentation method that utilizes spatio-temporal information for segmenting two-dimensional thyroid nodules on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Leveraging medical morphology knowledge of the thyroid gland, we designed a semi-supervised segmentation model that first segments the thyroid gland, guiding the model to focus exclusively on the thyroid region. This approach reduces the complexity of nodule segmentation by filtering out irrelevant regions and artifacts. Then, we introduced a method to explicitly extract temporal information from DCE-MRI data and integrated this with spatial information. The fusion of spatial and temporal features enhances the model's robustness and accuracy, particularly in complex imaging scenarios. Experimental results demonstrate that the proposed method significantly improves segmentation performance across multiple state-of-the-art models. The Dice similarity coefficient (DSC) increased by 8.41%, 7.05%, 9.39%, 11.53%, 20.94%, 17.94%, and 15.65% for U-Net, U-Net +  + , SegNet, TransUnet, Swin-Unet, SSTrans-Net, and VM-Unet, respectively, and significantly improved the segmentation accuracy of nodules of different sizes. These results highlight the effectiveness of our spatial-temporal approach in achieving accurate and reliable thyroid nodule segmentation, offering a promising framework for clinical applications and future research in medical image analysis.

The Role of Positron Emission Tomography/Computed Tomography in the Management of Differentiated Thyroid Cancer: Current Applications and Future Perspectives

Differentiated thyroid cancer (DTC), comprising papillary and follicular thyroid carcinoma, is the most common thyroid malignancy and typically has a favourable prognosis when detected early. Positron emission tomography/computed tomography (PET/CT) has emerged as a valuable imaging modality, integrating metabolic and anatomical data. Although PET/CT is not usually part of the initial diagnostic process due to DTC's indolent nature and low metabolic activity, it plays an essential role in selected clinical scenarios. This includes identifying recurrence in patients with elevated thyroglobulin (Tg) levels and negative radioactive iodine (RAI) scans, evaluating metastatic disease, and guiding treatment in advanced cases. As the use of PET/CT evolves in oncology, this review explores its application in regard to staging, detection of recurrence, and follow-up in terms of managing DTC while also evaluating potential challenges that may occur in the future. The review also considers emerging radiotracers and the theragnostic potential of PET/CT.

Web-based prediction models for predicting overall survival and cancer specific survival in lung metastasis of patients with thyroid cancer: a study based on the SEER database and a Chinese cohort

Background: The current high incidence of thyroid cancer (TC) is usually accompanied by poor prognosis of patients who also develop lung metastasis. Therefore, the present study aimed to develop a survival prediction model to guide clinical decision-making. Methods: This study retrospectively analyzed 679 patients with TCLM from 2010 to 2015 using the Surveillance, Epidemiology, and End Results (SEER) database. The external validation cohort consisted of 48 patients from Tianjin Medical University General Hospital (TMUGP) and Tianjin Cancer Hospital (TCH). Cox proportional risk regression models were used to analyze prognostic influences on patients and the screened variables were used to build the survival prediction models. The present study used the C-index, time-dependent ROC curves, calibration curves, and decision curve analysis (DCA) were used to assess the performance of the nomogram models. Results: The Cox proportional risk regression model analysis identified independent prognostic factors in patients with TCLM. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.813, cancer specific survival (CSS) was 0.822. The area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.884, 0.879 and 0.883. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.887, 0.885 and 0.886. The C-index, time-dependent ROC curve, calibration curve, and DCA for the training group, internal validation group, and external validation group showed that the Nomogram had a clear advantage. Conclusion: In this study, two new nomograms were constructed to predict the risk of TCLM patients. The nomograms can be applied in clinical practice to help clinicians assess patient prognosis.

Thyroid Screening Techniques via Bioelectromagnetic Sensing: Imaging Models and Analytical and Computational Methods

The thyroid gland, which is sensitive to electromagnetic radiation, plays a crucial role in the regulation of the hormonal levels of the human body. Biosensors, on the other hand, are essential to access information and derive metrics about the condition of the thyroid by means of of non-invasive techniques. This paper provides a systematic overview of the recent literature on bioelectromagnetic models and methods designed specifically for the study of the thyroid. The survey, which was conducted within the scope of the radiation transmitter-thyroid model-sensor system, is centered around the following three primary axes: the bands of the frequency spectrum taken into account, the design of the model, and the methodology and/or algorithm. Our review highlights the areas of specialization and underscores the limitations of each model, including its time, memory, and resource requirements, as well as its performance. In this manner, this specific work may offer guidance throughout the selection process of a bioelectromagnetic model of the thyroid, as well as a technique for its analysis based on the available resources and the specific parameters of the electromagnetic problem under consideration.

Role of advanced MRI sequences for thyroid lesions assessment. A narrative review

Despite not being the first imaging modality for thyroid gland assessment, Magnetic Resonance Imaging (MRI), thanks to its optimal tissue contrast and spatial resolution, has provided some advancements in detecting and characterizing thyroid abnormalities. Recent research has been focused on improving MRI sequences and employing advanced techniques for a more comprehensive understanding of thyroid pathology. Although not yet standard practice, advanced MRI sequences have shown high accuracy in preliminary studies, correlating well with histopathological results. They particularly show promise in determining malignancy risk in thyroid lesions, which may reduce the need for invasive procedures like biopsies. In this line, functional MRI sequences like Diffusion Weighted Imaging (DWI), Dynamic Contrast-Enhanced MRI (DCE-MRI), and Arterial Spin Labeling (ASL) have demonstrated their potential usefulness in evaluating both diffuse thyroid conditions and focal lesions. Multicompartmental DWI models, such as Intravoxel Incoherent Motion (IVIM) and Diffusion Kurtosis Imaging (DKI), and novel methods like Amide Proton Transfer (APT) imaging or artificial intelligence (AI)-based analyses are being explored for their potential valuable insights into thyroid diseases. This manuscript reviews the critical physical principles and technical requirements for optimal functional MRI sequences of the thyroid and assesses the clinical utility of each technique. It also considers future prospects in the context of advanced MR thyroid imaging and analyzes the current role of advanced MRI sequences in routine practice.

Multi-class classification of thyroid nodules from automatic segmented ultrasound images: Hybrid ResNet based UNet convolutional neural network approach

BACKGROUND AND OBJECTIVES

Early detection and diagnosis of thyroid nodule types are important because they can be treated more effectively in their early stages. The types of thyroid nodules are generally stated as atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), benign follicular, and papillary follicular. The risk of malignancy for AUS/FLUS is typically stated to be between 5 and 15 %, while some studies indicate a risk as high as 25 %. Without complete histology, it is difficult to classify nodules and these diagnostic operations are pricey and risky. To minimize laborious workload and misdiagnosis, recently various AI-based decision support systems have been developed.

METHODS

In this study, a novel AI-based decision support system has been developed for the automated segmentation and classification of the types of thyroid nodules. This system is based on a hybrid deep-learning procedure that makes both an automatic thyroid nodule segmentation and classification tasks, respectively. In this framework, the segmentation is executed with some U-Net architectures such as ResUNet and ResUNet++ integrating with the feature extraction and upsampling with dropout operations to prevent overfitting. The nodule classification task is achieved by various deep nets architecture such as VGG-16, DenseNet121, ResNet-50, and Inception ResNet-v2 considering some accurate classification criteria such as Intersection over Union (IOU), Dice coefficient, accuracy, precision, and recall.

RESULTS

In analysis, a total of 880 patients with ages ranging from 10 to 90 years were included by taking the ultrasound images and demographics. The experimental evaluations showed that ResUNet++ demonstrated excellent segmentation outcomes, attaining remarkable evaluation scores including a dice coefficient of 92.4 % and a mean IOU of 89.7 %. ResNet-50 and Inception ResNet-v2 trained over the images segmented with UNets have shown better performance in terms of achieving high evaluation scores for the classification accuracy such as 96.6 % and 95.0 %, respectively. In addition, ResNet-50 and Inception ResNet-v2 classified AUS/FLUS from the images segmented with UNets with AUC=97.0 % and 96.0 %, respectively.

CONCLUSIONS

The proposed AI-based decision support system improves the automatic segmentation performance of AUS/FLUS and it has shown better performance than available approaches in the literature with respect to ACC, Jaccard and DICE losses. This system has great potential for clinical use by both radiologists and surgeons as well.

Evaluating the Effectiveness of Triiodothyronine Suppression and Withdrawal Versus Thyrogen Injections in Thyroid Cancer Assessments

Objective This study aimed to evaluate the specificity and effectiveness of triiodothyronine (T3) suppression and withdrawal, as compared to the conventional diagnostic approach using Thyrogen recombinant thyroid-stimulating hormone (TSH) injections, in the assessment of thyroid cancer patients post-thyroidectomy. Methods In this retrospective study, 18 patients diagnosed with thyroid cancer at a tertiary care hospital (Mediclinic City Hospital) in Dubai were included. The patients underwent total thyroidectomy, iodine ablation, and neck ultrasound. The cohort's clinical characteristics were analyzed, and histopathological examination of thyroid nodules was performed. In this study, paired T-tests were applied to evaluate the before-and-after impact of T3 and Thyrogen treatments on TSH and thyroglobulin (TG) levels in individual patients. To further analyze the effectiveness of these treatments, independent T-tests were conducted, allowing for a comparison of TSH and TG levels between different treatment groups within the patient cohort. This approach provided a comprehensive assessment of the treatments' effects on key thyroid indicators. Additionally, the diagnostic accuracy of T3 withdrawal and Thyrogen post-test on TG levels was assessed using statistical measures including sensitivity, specificity, and predictive values. Results The cohort had a mean age of 42.1 years and a female predominance. Distinct clinical profiles were observed across different thyroid cancer subtypes. Histopathological analysis confirmed typical features of papillary carcinoma variants. Significant changes in TSH levels post-treatment were noted, with T3 treatments showing a marked increase in TSH and TG levels, although changes in TG levels were not always statistically significant. Diagnostic test evaluation showed a sensitivity of 77.78%, a specificity of 83.33%, and an overall accuracy of 80.00% for T3 withdrawal and Thyrogen post-test on TG. Conclusion The study provides comprehensive insights into the clinical profiles and treatment responses in thyroid cancer patients post-thyroidectomy. The effectiveness of T3 and Thyrogen treatments in altering TSH and TG levels was established, with significant implications for patient management. The diagnostic tests for T3 withdrawal and Thyrogen post-test on TG demonstrated high accuracy, underlining their clinical utility in the post-treatment evaluation of thyroid cancer patients.

Diagnostic significance of MRI versus CT using identical PET data in patients with recurrent differentiated thyroid cancer: A PET/MRI study

In this retrospective study we compared magnet resonance imaging (MRI) and computed tomography (CT) each combined with identical 2-deoxy-2-[18F] fluoro-D-glucose or 2-[18F] F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) data in patients with recurrent differentiated thyroid cancer (DTC). In total 42 patients with DTC were examined. All patients underwent FDG PET/MRI and CT, the latter originating from one of the following examinations: I-131 single photon emission computed tomography/CT (32/42), low dose FDG PET/CT (5/42) or diagnostic FDG PET/CT (5/42). Two readers assessed FDG PET/MRI as well as FDG PET/CT, with the latter CT coming from one of the above examinations performed at a maximum temporal interval of 5 days from PET/MRI. Local recurrence, cervical lymph node - and pulmonary metastases were assessed in a consensus read. Lesions rated with a high malignancy score (score 4 or 5) were further analyzed. Every malignant lesion was verified if it was identified by one of both or by both modalities. In 20 of 42 patients altogether 100 malignant lesions were present. In 11/20 patients in total 15 local recurrences (15 in MRI/ 9 in CT: 9 CT/MRI, 6 MRI only, 0 CT only; P = .04) were found with a statistically significant better performance of MRI. Regarding lymph node metastases, in total 13 lesions (12 in MRI/ 8 in CT: 7 CT/MRI, 5 MRI only, 1 CT only; P = .22) in 8/20 patients were found with no significant difference between both modalities. Furthermore, in 9/20 patients in total 72 lung lesions (40 in MRI/ 63 in CT: 31 CT/MRI, 9 MRI only, 32 CT only; P = .001) were found with a statistically significant better performance of CT. In 33/42 patients follow up was available and supported the observations. In patients with recurrent DTC, PET/MRI showed superiority compared to PET/CT in evaluation of the neck region. PET/MRI was inferior to PET/CT in evaluation of the lung. PET/MRI in combination with a low dose CT of the lung may thus represent the ideal staging tool in patients with recurrent DTC.

Evaluation of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Clinical-Ultrasound Radiomic Machine Learning-Based Model

We aim to develop a clinical-ultrasound radiomic (USR) model based on USR features and clinical factors for the evaluation of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC). This retrospective study used routine clinical and US data from 205 PTC patients. According to the pathology results, the enrolled patients were divided into a non-CLNM group and a CLNM group. All patients were randomly divided into a training cohort (n = 143) and a validation cohort (n = 62). A total of 1046 USR features of lesion areas were extracted. The features were reduced using Pearson's Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified 15-fold cross-validation. Several machine learning classifiers were employed to build a Clinical model based on clinical variables, a USR model based solely on extracted USR features, and a Clinical-USR model based on the combination of clinical variables and USR features. The Clinical-USR model could discriminate between PTC patients with CLNM and PTC patients without CLNM in the training (AUC, 0.78) and validation cohorts (AUC, 0.71). When compared to the Clinical model, the USR model had higher AUCs in the validation (0.74 vs. 0.63) cohorts. The Clinical-USR model demonstrated higher AUC values in the validation cohort (0.71 vs. 0.63) compared to the Clinical model. The newly developed Clinical-USR model is feasible for predicting CLNM in patients with PTC.

Analysis of the Application Value of Ultrasound Imaging Diagnosis in the Clinical Staging of Thyroid Cancer

Thyroid cancer affects 1.3 percent of the population, with rates of occurrence rising in recent years (approximately 2 percent per year). Thyroid cancer is a common endocrine cancer with an annual increase in occurrence. Although the general prognosis for differentiated subtypes is favorable, the rate of mortality linked with thyroid cancer has been steadily progressing. The presence of suspicious thyroid nodules necessitates more diagnostic testing, including laboratory evaluation, additional imaging, and biopsy. For clinical staging and appropriate patient therapy design, accurate diagnosis is necessary. In this paper, we examined the application value of ultrasound imaging diagnosis in the clinical staging of thyroid tumor in this research. The benefit of early diagnosis is determined in this article using ultrasonography reports from Chinese patients. Images of benign and malignant thyroid nodules were collected and annotated in this work, and deep learning-based image recognition and diagnostic system was built utilizing the adaptive wavelet transform-based AdaBoost algorithm (AWT-AA). The system's efficacy in diagnosing thyroid nodules was assessed, and the use of ultrasound imaging in clinical practice was studied. The variables that had a significant impact on malignant nodules were studied using logistic multiple regression analysis. The sensitivity and specificity of ultrasonography thyroid imaging reporting and data system (TI-RADS) categorization outcomes for benign and malignant tumors were also calculated.

Thyroid Cancer Diagnostics Related to Occupational and Environmental Risk Factors: An Integrated Risk Assessment Approach

There are still many questions remaining about the etiopathogenesis of thyroid cancer, the most common type of endocrine neoplasia. Numerous occupational and environmental exposures have been shown to represent important risk factors that increase its incidence. Updated information about thyroid cancer diagnostics related to occupational and environmental risk factors is reviewed here, considering an integrated risk assessment approach; new data concerning thyroid cancer etiology and pathogenesis mechanisms, diagnostic biomarkers and methodologies, and risk factors involved in its pathogenesis are presented. A special emphasis is dedicated to specific occupational risk factors and to the association between environmental risk agents and thyroid cancer development. The occupational environment is taken into consideration, i.e., the current workplace and previous jobs, as well as data regarding risk factors, e.g., age, gender, family history, lifestyle, use of chemicals, or radiation exposure outside the workplace. Finally, an integrative approach is presented, underlying the need for an accurate Risk Assessment Matrix based on a systematic questionnaire. We propose a complex experimental design that contains different inclusion and exclusion criteria for patient groups, detailed working protocols for achieving coherent and sustainable, well-defined research stages from sample collection to the identification of biomarkers, with correlations between specific oncometabolites integrated into the Risk Assessment Matrix.

Fine-Needle Aspiration Cytology and Histological Types of Thyroid Cancer in the Elderly: Evaluation of 9070 Patients from a Single Referral Centre

Simple Summary

Elderly patients have a high prevalence of thyroid nodules, and their management should consider the presence of comorbidities, which are frequent in this age group. In this retrospective monocentric study, we analyzed data of more than 13,000 nodules in order to highlight differences between the elderly and the general population in terms of cytological and histological diagnoses. Thyroid nodules in the elderly are more often benign than in younger patients. Nevertheless, in case of malignancy, follicular-derived well-differentiated tumors are almost always diagnosed in younger patients. Instead, elderly patients more often have tumors with aggressive histotypes. In addition, even in presence of well-differentiated tumors, elderly patients present a higher rate of high-risk pathological features.

Abstract

Background. The prevalence of thyroid nodules increases with age. Their management takes into account the presence of co-morbidities, which are frequent among the elderly. We sought to highlight the differences between the elderly and the general population in cytological and histological diagnoses. Methods. In this retrospective cohort study, we gathered 13,747 nodule data and compared cytological and histological diagnoses between patients aged over 65 years and a control group. Results. Elderly patients had a higher prevalence of cytologically benign nodules and, consequently, they were less frequently subject to surgery. However, there were no differences in terms of malignancy-risk after surgery. At histology, elderly patients often presented aggressive histology such as medullary thyroid carcinoma, poorly-differentiated and anaplastic cancer, tall cell variant of papillary thyroid carcinoma and Hürthle cell carcinoma. Even in presence of well-differentiated cancer, older patients had higher rates of local invasiveness, lateral lymph node involvement and vascular invasion. Conclusion. Thyroid nodules in elderly patients represent a challenging entity since they are very often benign, but, in case of malignancy, aggressive histotypes and high-risk features are more frequent. Therefore, presurgical characterization of nodules in older patients is crucial and might require strict monitoring.

GINS2 affects cell proliferation, apoptosis, migration and invasion in thyroid cancer via regulating MAPK signaling pathway

Globally, thyroid cancer (TC) is considered to be the commonest endocrine malignancy. GINS complex subunit 2 (GINS2) belongs to the GINS complex family and is associated with cellular migration, invasion and growth. The present study aimed to investigate the underlying mechanisms of GINS2 on cell viability, migration and invasion in TC cells. By using MTT, wound healing and Transwell assays, the cell viability, migration and invasion were determined. Apoptosis was examined by immunofluorescence. Western blotting was used to detect protein expression levels. In the present study, biological function analysis demonstrated that GINS2 interference attenuated cell viability, migration and invasion in TC cell lines (K1 and SW579). It was discovered that, compared with the control group, GINS2 silencing induced apoptosis in TC cells. Additionally, GINS2 interference inhibited key proteins in the MAPK signaling pathway, including JNK, ERK and p38. According to these comparative experiments, GINS2 was considered to act a pivotal part in cell viability, migration and invasion of TC by regulating the MAPK signaling pathway and might be a potential therapeutic target for treating TC.

A comparison of the efficiency of diagnostic ultrasound and magnetic resonance imaging of cervical lymph nodes in papillary thyroid carcinoma

OBJECTIVE

To compare and evaluate diagnostic capabilities of preoperative ultrasonography (US) and magnetic resonance imaging (MRI) in the cervical lymph nodes of patients with papillary thyroid cancer.

METHODS

A retrospective dataset involving 156 patients who had undergone thyroidectomy and preoperative US and MRI was assembled. Among these, 69 had cervical lymph node metastasis and 87 did not. At least four radiologists unilaterally and spontaneously investigated the US and MRI attributes of the cervical lymph nodes. The efficiency of diagnostic imaging for cervical lymph nodes, including their true-positive rate or sensitivity, true-negative rate or specificity, positive predictive value, negative predictive value, and predictive accuracy were analysed and assessed.

RESULTS

In the assessment of cervical lymph node metastases of papillary thyroid cancer, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of diagnostic US vs. MRI were 58.0% vs. 79.7%, 69.0% vs. 83.9%, 59.7% vs. 79.7%, 67.4% vs. 83.9%, and 64.1% vs. 82.1%, respectively. The accuracy consistency of the two imaging modalities was 83.5%.

CONCLUSIONS

MRI is more effective than US in diagnosing and assessing cervical lymph node metastases of papillary thyroid cancer.