Thyroid Nodule Characterization: How to Assess the Malignancy Risk. Update of the Literature

DAC-Net: A light-weight U-shaped network based efficient convolution and attention for thyroid nodule segmentation

Recently, there has been a focused effort to improve the efficiency of thyroid nodule segmentation algorithms. This endeavor has resulted in the development of increasingly complex modules, such as the Transformer, leading to models with a higher number of parameters and computing requirements. Sophisticated models have difficulties in being implemented in clinical medicine platforms because of limited resources. DAC-Net is a Lightweight U-shaped network created to achieve high performance in segmenting thyroid nodules. Our method consists of three main components: DWSE, which combines depthwise convolution and squeeze-excitation block to enhance feature extraction and connections between samples; ADA, which includes Split Atrous and Dual Attention to extract global and local feature information from various viewpoints; and CSSC, which involves channel- scale and spatial-scale connections. This module enables the fusing of multi-stage features at global and local levels, producing feature maps at different channel and geographical scales, delivering a streamlined integration of multi-scale information. Combining these three components in our U- shaped design allows us to achieve competitive performance while also decreasing the number of parameters and computing complexity. Several experiments were conducted on the DDTI and TN3K datasets. The experimental results demonstrate that our model outperforms state-of-the-art thyroid nodule segmentation architectures in terms of segmentation performance. Our model not only reduces the number of parameters and computing expenses by 73x and 56x, respectively, but also exceeds TransUNet in segmentation performance. The source code is accessible at https://github.com/Phil-y/DAC-Net.

A bibliometric analysis of follicular thyroid carcinoma: Current situation, hot spots, and global trends

BACKGROUND

Follicular thyroid carcinoma (FTC), the second most prevalent thyroid cancer after papillary thyroid cancer (PTC), tends to metastasize distantly, leading to poorer outcomes. Despite substantial research, a holistic bibliometric analysis of FTC literature is lacking. This study aims to fill this gap by employing bibliometric methods to track FTC research evolution.

METHODS

English FTC publications were systematically gathered from the Web of Science. Bibliometric analysis, using R, VOSviewer, CiteSpace, and Excel, synthesized data and explored global research trends and topics.

RESULTS

From 2000 to 2023, 9086 authors from 1953 institutions across 75 countries contributed to 1776 papers in 491 academic journals on FTC. The last two decades have witnessed a steady increase in publications related to FTC, with the United States leading in terms of publication volume. The United States dominated both in publications and citations, with the National Cancer Institute and Sheue-Yann Cheng as leading contributors. The journal 'Thyroid' featured the most publications, while the 'Journal of Clinical Endocrinology and Metabolism' ranked highest in citation frequency. Research focused on gene expression analysis and preoperative diagnostics, with recent trends shifting toward prognosis management and machine learning due to advances in medical technology and increased health awareness.

CONCLUSION

This comprehensive bibliometric analysis has mapped the landscape of FTC research, highlighting key contributors, institutions, and thematic trends. Current discourse predominantly revolves around genetic analysis, prognostic determinants, and preoperative diagnostics in FTC. This foundational work guides future FTC research, providing insights into its evolution.

A Multi-View Deep Learning Model for Thyroid Nodules Detection and Characterization in Ultrasound Imaging

Thyroid Ultrasound (US) is the primary method to evaluate thyroid nodules. Deep learning (DL) has been playing a significant role in evaluating thyroid cancer. We propose a DL-based pipeline to detect and classify thyroid nodules into benign or malignant groups relying on two views of US imaging. Transverse and longitudinal US images of thyroid nodules from 983 patients were collected retrospectively. Eighty-one cases were held out as a testing set, and the rest of the data were used in five-fold cross-validation (CV). Two You Look Only Once (YOLO) v5 models were trained to detect nodules and classify them. For each view, five models were developed during the CV, which was ensembled by using non-max suppression (NMS) to boost their collective generalizability. An extreme gradient boosting (XGBoost) model was trained on the outputs of the ensembled models for both views to yield a final prediction of malignancy for each nodule. The test set was evaluated by an expert radiologist using the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS). The ensemble models for each view achieved a mAP0.5 of 0.797 (transverse) and 0.716 (longitudinal). The whole pipeline reached an AUROC of 0.84 (CI 95%: 0.75-0.91) with sensitivity and specificity of 84% and 63%, respectively, while the ACR-TIRADS evaluation of the same set had a sensitivity of 76% and specificity of 34% (p-value = 0.003). Our proposed work demonstrated the potential possibility of a deep learning model to achieve diagnostic performance for thyroid nodule evaluation.

Thyroid ultrasound and its ancillary techniques

Ultrasound (US) of the thyroid has been used as a diagnostic tool since the late 1960s. US is the most important imaging tool for diagnosing thyroid disease. In the majority of cases a correct diagnosis can already be made in synopsis of the sonographic together with clinical findings and basal thyroid hormone parameters. However, the characterization of thyroid nodules by US remains challenging. The introduction of Thyroid Imaging Reporting and Data Systems (TIRADSs) has improved diagnostic accuracy of thyroid cancer significantly. Newer techniques such as elastography, superb microvascular imaging (SMI), contrast enhanced ultrasound (CEUS) and multiparametric ultrasound (MPUS) expand diagnostic options and tools further. In addition, the use of artificial intelligence (AI) is a promising tool to improve and simplify diagnostics of thyroid nodules and there is evidence that AI can exceed the performance of humans. Combining different US techniques with the introduction of new software, the use of AI, FNB as well as molecular markers might pave the way for a completely new area of diagnostic accuracy in thyroid disease. Finally, interventional ultrasound using US-guided thermal ablation (TA) procedures are increasingly proposed as therapy options for benign as well as malignant thyroid diseases.

[SFE-AFCE-SFMN 2022 consensus on the management of thyroid nodules: Interest and place of thyroid cytology]

The SFE-AFCE-SFMN 2022 consensus deals with the management of thyroid nodules, a symptom that is a frequent reason for consultation in endocrinology. Thyroid nodules are very common and mostly benign. Thyroid ultrasound and thyroid fine-needle aspiration biopsy (FNAB) are the reference tests for the analysis of these nodules. The aim of this article is to describe for the cytopathologist the key points of the SFE-AFCE-SFMN 2022 consensus involving thyroid cytology: the indications for thyroid FNAB, the technique and analysis, and the management (treatment, follow-up) following this cytological screening examination, a key element in the management of the thyroid nodule.

Evaluation of cervical lymph nodes using multispectral optoacoustic tomography: a proof-of-concept study

OBJECTIVES

Examination of lymph nodes is one of the most common indications for imaging in the head and neck region. The purpose of this study is to evaluate whether multispectral optoacoustic tomography can be used to observe chromophore differences between benign and malignant neck lymph nodes.

MATERIALS AND METHODS

Proof-of-concept ex vivo study of resected cervical lymph nodes from 11 patients. The examination of lymph nodes included imaging with hybrid ultrasound and multispectral tomography system followed by spectral unmixing to separate signals from the endogenous chromophores water, lipid, hemoglobin and oxygenated hemoglobin; calculation of semi-quantitative parameters (total hemoglobin and relative oxygenation of hemoglobin). Comparison of the results from the hybrid measurement with the histopathological results.

RESULTS

Most patients suffered from squamous cell carcinoma (n = 7), also metastasis from salivary gland adenocarcinoma and papillary thyroid carcinoma, were included. The comparison between benign cervical lymph nodes and metastases showed significant differences for the absorbers water, lipid, hemoglobin and oxygenated hemoglobin and total hemoglobin.

CONCLUSIONS

Our ex vivo study suggests that multispectral optoacoustic tomography can be used to detect differences between reactive lymph nodes and metastases. The measurement of endogenous chromophores can be used for this purpose. The examinations are non-invasively and thus potentially improve diagnostic prediction. However, potential influences from the ex vivo setting must be considered.

Joint Segmentation and Differential Diagnosis of Thyroid Nodule in Contrast-Enhanced Ultrasound Images

OBJECTIVE

Microvascular perfusion can be observed in real time with contrast-enhanced ultrasound (CEUS), which is a novel ultrasound technology for visualizing the dynamic patterns of parenchymal perfusion. Automatic lesion segmentation and differential diagnosis of malignant and benign based on CEUS are crucial but challenging tasks for computer-aided diagnosis of thyroid nodule.

METHODS

To tackle these two formidable challenges concurrently, we provide Trans-CEUS, a spatial-temporal transformer-based CEUS analysis model to finish the joint learning of these two challenging tasks. Specifically, the dynamic swin-transformer encoder and multi-level feature collaborative learning are combined into U-net for achieving accurate segmentation of lesions with ambiguous boundary from CEUS. In addition, variant transformer-based global spatial-temporal fusion is proposed to obtain long-distance enhancement perfusion of dynamic CEUS for promoting differential diagnosis.

RESULTS

Empirical results of clinical data showed that our Trans-CEUS model achieved not only a good lesion segmentation result with a high Dice similarity coefficient of 82.41%, but also superior diagnostic accuracy of 86.59%. Conclusion & significance: This research is novel since it is the first to incorporate the transformer into CEUS analysis, and it shows promising results on dynamic CEUS datasets for both segmentation and diagnosis tasks of the thyroid nodule.

Value of TI-RADS and elastography strain ratio in predicting malignant thyroid nodules: experience from a single center in Egypt

Introduction

Thyroid nodules are highly prevalent in the general population; therefore, it is crucial to discriminate benign from malignant nodules. A practical thyroid imaging reporting and data system (TI-RADS) for thyroid nodules and ultrasound elastography are valuable tools not only for characterization of nodules but also for the selection of tumors for fine-needle aspiration cytology (FNAC).

Objective

This study aimed to evaluate the diagnostic accuracy of US evaluation in the prediction of malignant thyroid nodules and evaluate the role of the elastography score, strain ratio (SR), and the TI-RADS scoring system as non-invasive tools in differentiation between malignant and benign thyroid nodules.

Material and methods

A total of 1269 patients were evaluated between February 2017 and April 2020 by a single expert thyroid ultrasound operator. The final diagnosis was achieved from cytological and/or histological evaluation and follow-up for at least 1 year.

Results

There were 1088 females and 181 males with a mean age of 44 ± 10 SD. The final diagnosis was 1197 benign nodules and 72 malignant nodules. Most malignant nodules were scored elastography score 4 (83.3%) and TI-RADS category 5 (86.11%). We found that nodules with antero-posterior to transverse (A-P/T) diameter > 1, have 21 times more risk to be malignant than those with A-P/T diameter < 1, patients with solitary thyroid nodules have 4.5 times to develop malignancy compared to those with multinodular goiter (MNG), nodules with absent halo have 4 times more risk of malignancy. Furthermore, microcalcifications in thyroid nodules increase the risk of malignancy 9 times compared to those without calcifications. SR was found to be an excellent discriminator to differentiate between benign and malignant nodules with P < 0.001. Also, we found that for every unit increase in SR, the risk of malignancy increased by 20%. We reported that the accuracy of ultrasonography in the detection of malignant thyroid nodules had a sensitivity of 89%, specificity of 98%, 70% PPV, and 99.3% NPV, with an overall accuracy of 97.2%.

Conclusion

The application of ultrasonographic non-invasive criteria for thyroid nodules in clinical practice might significantly reduce the number of unnecessary FNAC. Elastography, SR, and TI-RADS classification could be good predictors for malignant thyroid nodules.

Effect of the categorization method on the diagnostic performance of ultrasound risk stratification systems for thyroid nodules

Objective

To evaluate whether the categorization methods of risk stratification systems (RSSs) is a decisive factor that influenced the diagnostic performances and unnecessary FNA rates in order to choose optimal RSS for the management of thyroid nodules.

Methods

From July 2013 to January 2019, 2667 patients with 3944 thyroid nodules had undergone pathological diagnosis after thyroidectomy and/or US-guided FNA. US categories were assigned according to the six RSSs. The diagnostic performances and unnecessary FNA rates were calculated and compared according to the US-based final assessment categories and the unified size thresholds for biopsy proposed by ACR-TIRADS, respectively.

Results

A total of 1781 (45.2%) thyroid nodules were diagnosed as malignant after thyroidectomy or biopsy. Significantly lowest specificity and accuracy, along with the highest unnecessary FNA rates were seen in EU-TIRADS for both US categories (47.9%, 70.2%, and 39.4%, respectively, all P < 0.05) and indications for FNA (54.2%, 50.0%, and 55.4%, respectively, all P < 0.05). Diagnostic performances for US-based final assessment categories exhibited similar accuracy for AI-TIRADS, Kwak-TIRADS, C-TIRADS, and ATA guidelines (78.0%, 77.8%, 77.9%, and 76.3%, respectively, all P > 0.05), while the lowest unnecessary FNA rate was seen in C-TIRADS (30.9%) and without significant differences to that of AI-TIRADS, Kwak-TIRADS, and ATA guideline (31.5%, 31.7%, and 33.6%, respectively, all P > 0.05). Diagnostic performance for US-FNA indications showed similar accuracy for ACR-TIRADS, Kwak-TIRADS, C-TIRADS and ATA guidelines (58.0%, 59.7%, 58.7%, and 57.1%, respectively, all P > 0.05). The highest accuracy and lowest unnecessary FNA rate were seen in AI-TIRADS (61.9%, 38.6%) and without significant differences to that of Kwak-TIRADS(59.7%, 42.9%) and C-TIRADS 58.7%, 43.9%, all P > 0.05).

Conclusion

The different US categorization methods used by each RSS were not determinant influential factors in diagnostic performance and unnecessary FNA rate. For daily clinical practice, the score-based counting RSS was an optimal choice.

Nomogram incorporating preoperative clinical and ultrasound indicators to predict aggressiveness of solitary papillary thyroid carcinoma

Objective

To construct a nomogram based on preoperative clinical and ultrasound indicators to predict aggressiveness of solitary papillary thyroid carcinoma (PTC).

Methods

Preoperative clinical and ultrasound data from 709 patients diagnosed with solitary PTC between January 2017 and December 2020 were analyzed retrospectively. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with PTC aggressiveness, and these factors were used to construct a predictive nomogram. The nomogram's performance was evaluated in the primary and validation cohorts.

Results

The 709 patients were separated into a primary cohort (n = 424) and a validation cohort (n = 285). Univariate analysis in the primary cohort showed 13 variables to be associated with aggressive PTC. In multivariate logistic regression analysis, the independent predictors of aggressive behavior were age (OR, 2.08; 95% CI, 1.30-3.35), tumor size (OR, 4.0; 95% CI, 2.17-7.37), capsule abutment (OR, 2.53; 95% CI, 1.50-4.26), and suspected cervical lymph nodes metastasis (OR, 2.50; 95% CI, 1.20-5.21). The nomogram incorporating these four predictors showed good discrimination and calibration in both the primary cohort (area under the curve, 0.77; 95% CI, 0.72-0.81; Hosmer-Lemeshow test, P = 0.967 and the validation cohort (area under the curve, 0.72; 95% CI, 0.66-0.78; Hosmer-Lemeshow test, P = 0.251).

Conclusion

The proposed nomogram shows good ability to predict PTC aggressiveness and could be useful during treatment decision making.

Advances in knowledge

Our nomogram-based on four indicators-provides comprehensive assessment of aggressive behavior of PTC and could be a useful tool in the clinic.

The progress of radiomics in thyroid nodules

Due to the development of Artificial Intelligence (AI), Machine Learning (ML), and the improvement of medical imaging equipment, radiomics has become a popular research in recent years. Radiomics can obtain various quantitative features from medical images, highlighting the invisible image traits and significantly enhancing the ability of medical imaging identification and prediction. The literature indicates that radiomics has a high potential in identifying and predicting thyroid nodules. So in this article, we explain the development, definition, and workflow of radiomics. And then, we summarize the applications of various imaging techniques in identifying benign and malignant thyroid nodules, predicting invasiveness and metastasis of thyroid lymph nodes, forecasting the prognosis of thyroid malignancies, and some new advances in molecular level and deep learning. The shortcomings of this technique are also summarized, and future development prospects are provided.

A Super-resolution Guided Network for Improving Automated Thyroid Nodule Segmentation

BACKGROUND AND OBJECTIVE

A thyroid nodule is an abnormal lump that grows in the thyroid gland, which is the early symptom of thyroid cancer. In order to diagnose and treat thyroid cancer at the earliest stage, it is desired to characterize the nodule accurately. Ultrasound thyroid nodules segmentation is a challenging task due to the speckle noise, intensity heterogeneity, low contrast and low resolution. In this paper, we propose a novel framework to improve the accuracy of thyroid nodules segmentation.

METHODS

Different from previous work, a super-resolution reconstruction network is firstly constructed to upscale the resolution of the input ultrasound image. After that, our proposed N-shape network is utilized to perform the segmentation task. The guidance of super-resolution reconstruction network can make the high-frequency information of the input thyroid ultrasound image richer and more comprehensive than the original image. Our N-shape network consists of several atrous spatial pyramid pooling blocks, a multi-scale input layer, a U-shape convolutional network with attention blocks and a proposed parallel atrous convolution(PAC) module. These modules are conducive to capture context information at multiple scales so that semantic features can be fully utilized for lesion segmentation. Especially, our proposed PAC module is beneficial to further improve the segmentation by extracting high-level semantic features from different receptive fields. We use the UTNI-2021 dataset for model training, validating and testing.

RESULTS

The experimental results show that our proposed method achieve a Dice value of 91.9%, a mIoU value of 87.0%, a Precision value of 88.0%, a Recall value 83.7% and a F1-score value of 84.3%, which outperforms most state-of-the-art methods.

CONCLUSIONS

Our method achieves the best performance on the UTNI-2021 dataset and provides a new way of ultrasound image segmentation. We believe that our method can provide doctors with reliable auxiliary diagnosis information in clinical practice.

Malignancy risk stratification of thyroid nodules smaller than 10 mm with ACR-TIRADS, K-TIRADS, and ATA-2015 guidelines: a prospective study

Background

Small thyroid nodules (≤ 10 mm) are common findings in thyroid ultrasonography. The first purpose of this study was to compare the performance of three guidelines in the diagnosis of malignancy for small thyroid nodules. The second aim was to find the ultrasonographic characteristics potentially associated with the risk of malignancy. This prospective cross-sectional study was performed on the patients with a diagnosis of small thyroid nodules (≤ 10 mm), who were rereferred to the radiologists for sonography and FNA. Sonographic features were recorded and scored according to the American College of Radiology-Thyroid Imaging Reporting and Data System (ACR-TIRADS), American Thyroid Association (ATA-2015), and Korean-TIRADS (K-TIRADS). Finally, FNA was conducted and cytological findings were reported.

Results

In total, 287 thyroid nodules from 256 subjects (64 men and 192 women) were finally included in the study. The accuracy of ACR-TIRADS categories TR5 and TR4/5 was 88.9% and 72.1%, respectively. This rate for ATA-2015 classes high suspicion and intermediate suspicion/high suspicion was 88.9% and 82.6%, respectively. For K-TIRADS classes 5 and 4/5, the diagnostic accuracy was 89.6% and 82.9%, respectively. Significant direct associations were found between malignancy and punctate echogenic foci (odds ratio [OR] = 6.46), hypoechogenicity (OR = 6.39), ill-defined margin (OR = 4.38), and irregular margin (OR = 7.33).

Conclusion

The differences in the strength of the three guidelines in the prediction of the malignancy should be considered by clinicians and radiologists in the management of thyroid nodules smaller than 10 mm.

SFE-AFCE-SFMN 2022 Consensus on the management of thyroid nodules : Recommendations in thyroid cytology: from technique to interpretation

The SFE-AFCE-SFMN 2022 consensus deals with the management of thyroid nodules, a condition that is a frequent reason for consultation in endocrinology. In more than 90% of cases, patients are euthyroid, with benign non-progressive nodules that do not warrant specific treatment. The clinician's objective is to detect malignant thyroid nodules at risk of recurrence and death, toxic nodules responsible for hyperthyroidism or compressive nodules warranting treatment. The diagnosis and treatment of thyroid nodules requires close collaboration between endocrinologists, nuclear medicine physicians and surgeons, but also involves other specialists. Therefore, this consensus statement was established jointly by 3 societies: the French Society of Endocrinology (SFE), French Association of Endocrine Surgery (AFCE) and French Society of Nuclear Medicine (SFMN); the various working groups included experts from other specialties (pathologists, radiologists, pediatricians, biologists, etc.). This section deals with the technique and interpretation of thyroid fine-needle aspiration biopsy (FNAB), a reference test for the analysis of thyroid nodules.

Combined Shear Wave Elastography and EU TIRADS in Differentiating Malignant and Benign Thyroid Nodules

Although multimodal ultrasound approaches have been suggested to potentially improve the diagnosis of thyroid cancer; the diagnostic utility of the combination of SWE and malignancy-risk stratification systems remains vague due to the lack of standardized criteria. The purpose of the study was to assess the diagnostic value of the combination of grey scale ultrasound assessment using EU TIRADS and shear wave elastography. 121 patients (126 nodules−81 benign; 45 malignant) underwent grey scale ultrasound and SWE imaging of nodules between 0.5 cm and 5 cm prior to biopsy and/or surgery. Nodules were analyzed based on size stratifications: <1 cm (n = 43); 1−2 cm (n = 52) and >2 cm (n = 31) and equivocal cytology status (n = 52), and diagnostic performance assessments were conducted. The combination of EU TIRADS with SWE using the SD parameter; maintained a high sensitivity and significantly improved the specificity of sole EU TIRADS for nodules 1−2 cm (SEN: 72.2% vs. 88.9%, p > 0.05; SPEC: 76.5% vs. 55.9%, p < 0.01) and >2 cm (SEN: 71.4% vs. 85.7%, p > 0.05; SPEC: 95.8% vs. 62.5%, p < 0.01). For cytologically-equivocal nodules; the combination with the SWE minimum parameter resulted in a significant reduction in sensitivity with increased specificity (SEN: 60% vs. 80%; SPEC: 83.4% vs. 37.8%; all p < 0.05). SWE in combination with EU TIRADS is diagnostically efficient in discriminating nodules > 1 cm but is not ideal for discriminating cytologically-equivocal nodules.

The Italian Consensus for the Classification and Reporting of Thyroid Cytology: Cytohistologic and molecular correlations on 37,371 nodules from a single institution

BACKGROUND

The Italian Consensus for the Classification and Reporting of Thyroid Cytology (ICCRTC) includes six diagnostic categories (TIR 1/1C, TIR 2, TIR 3A, TIR 3B, TIR 4, and TIR 5), each indicating a different risk of malignancy. The objective of this monocentric retrospective study was to evaluate the distribution of the ICCRTC classes at the authors' institution and assess their cytohistologic correlations.

METHODS

The authors retrospectively collected 37,371 consecutive cytologic reports of thyroid nodules and described the clinical-pathologic features of the different cytologic categories. The cytologic diagnoses also were compared with histologic outcomes in a subset of patients.

RESULTS

The cytologic classes were distributed as follows: nondiagnostic, 15.6%; benign, 66.5%; low-risk indeterminate, 10% (TIR 3A); high-risk indeterminate, 3.5% (TIR 3B); suspicious, 1.7%; and malignant, 2.6%. According to histology, the risk of malignancy was very high in the nondiagnostic category (29.8%), with young male patients more exposed to malignancy, and it was relatively high among benign (7.8%) and indeterminate nodules (32.5% in TIR 3A; 52.1% in TIR 3B), mainly because of the high prevalence of follicular architecture in malignant tumors. On histology, the malignancy rates were 92.4% and 99.3% for the suspicious and malignant categories, respectively; aggressive variants of papillary thyroid carcinoma were mostly diagnosed in these categories.

CONCLUSIONS

In this series, nondiagnostic nodules showed high prevalence and, surprisingly, high malignancy rates. Malignant tumors with follicular architecture represented a diagnostic pitfall in benign and indeterminate nodules. The suspicious and malignant categories had high specificity for malignancy. Importantly, the ICCRTC had high reliability for identifying preoperatively aggressive histotypes of thyroid carcinoma.

Multiparametric Sonographic Imaging of Thyroid Lesions: Chances of B-Mode, Elastography and CEUS in Relation to Preoperative Histopathology

Simple Summary

As the incidence of thyroid lesions in Europe is rising, more and more people affected by thyroid pathologies seek treatment in a clinic. Every suspicious thyroid nodule needs to be confirmed as benign or malignant in order to be treated correctly. Unnecessary invasive diagnostics and thyroid surgery should be avoided. The aim of this retrospective study was to improve the distinction between benign and malignant nodules by using new high-performance multiparametric ultrasound examination techniques. By analyzing 122 thyroid nodules we created a score-based system combining B-mode, shear-wave elastography and contrast-enhanced ultrasound malignancy criteria. This system allows for a quite accurate detection of thyroid carcinomas with a sensitivity of 95% and specificity of 75.49%. Shear-wave elastography and contrast-enhanced ultrasound can detect unique malignancy features, which cannot be found in B-mode. Therefore, these criteria would present a relevant addition to the B-mode TI-RADS classification.

Abstract

Background: The aim was to improve preoperative diagnostics of solid non-cystic thyroid lesions by using new high-performance multiparametric ultrasound examination techniques. Methods: Multiparametric ultrasound consists of B-mode, shear-wave elastography and contrast enhanced ultrasound (CEUS) including Time-Intensity-Curve (TIC) analysis. A bolus of 1–2.4 mL Sulfur Hexafluorid microbubbles was injected for CEUS. Postoperative histopathology was the diagnostic gold standard. Results: 116 patients were included in this study. 102 benign thyroid nodules were diagnosed as well as 20 carcinomas. Suspicious B-mode findings like microcalcifications, a blurry edge and no homogeneous sonomorphological structure were detected in 60, 75 and 80% of all carcinomas but only in 13.7, 36.3 and 46.1% of all benign lesions. The average shear-wave elastography measurements of malignant lesions (4.6 m/s or 69.8 kPa centrally and 4.2 m/s or 60.1 kPa marginally) exceed the values of benign nodules. Suspicious CEUS findings like a not-homogeneous wash-in and a wash-out were detected almost twice as often in carcinomas. Conclusion: Multiparametric ultrasound offers new possibilities for the preoperative distinction between benign and malignant thyroid nodules. A score based system of B-mode, shear-wave and CEUS malignancy criteria shows promising results in the detection of thyroid carcinomas. It reaches a sensitivity of 95% and specificity of 75.49%.

Thyroid and renal cancers: A bidirectional association

There is a deep interrelation between the thyroid gland and the kidney parenchyma, with dysfunction of the first leading to significant changes in renal metabolism and vice versa. Given the recognition of cancer as a systemic disease, the raise of thyroid tumors and the common association of several malignancies, such as breast cancer, prostate cancer, colorectal cancer, and other, with an increased risk of kidney disease, public health alert for these conditions is warranted. A systematic review of the current evidence on the bidirectional relationship between thyroid and renal cancers was conducted including 18 studies, highlighting patient’s characteristics, histology, time for secondary malignancy to develop from the first diagnosis, treatment, and follow-up. A total of 776 patients were identified; median age was 64 years (range: 7–76 years). Obesity and family history were identified as the most common risk factors, and genetic susceptibility was suggested with a potential strong association with Cowden syndrome. Controversy on chemo and radiotherapy effects was found, as not all patients were previously exposed to these treatments. Men were more likely to develop kidney cancer after a primary thyroid malignancy, with 423/776 (54%) experiencing renal disease secondarily. Median time after the first malignancy was 5.2 years (range: 0–20 years). With the advancement of current oncological therapy, the prognosis for thyroid cancer patients has improved, although there has been a corresponding rise in the incidence of multiple secondary malignancy within the same population, particularly concerning the kidney. Surgery can achieve disease-free survival, if surveillance follow-up allows for an early localized form, where radical treatment is recommended.

Is Melanoma Progression Affected by Thyroid Diseases?

Clinical and epidemiological evidence indicate a relationship between thyroid diseases and melanoma. In particular, the hypothyroidism condition appears to promote melanoma spread, which suggests a protective role of thyroid hormones against disease progression. In addition, experimental data suggest that, in addition to thyroid hormones, other hormonal players of the hypothalamic–pituitary–thyroid (HPT) axis, namely the thyrotropin releasing hormone and the thyrotropin, are likely to affect melanoma cells behavior. This information warrants further clinical and experimental studies in order to build a precise pattern of action of the HPT hormones on melanoma cells. An improved knowledge of the involved molecular mechanism(s) could lead to a better and possibly personalized clinical management of these patients.

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

Simple Summary

In the present review, an up-to-date summary of the state of the art of artificial intelligence (AI) implementation for thyroid nodule characterization and cancer is provided. The opinion on the real effectiveness of AI systems remains controversial. Taking into consideration the largest and most scientifically valid studies, it is possible to state that AI provides results that are comparable or inferior to expert ultrasound specialists and radiologists. Promising data approve AI as a support tool and simultaneously highlight the need for a radiologist supervisory framework for AI provided results. Therefore, current solutions might be more suitable for educational purposes.

Abstract

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.

Improving the diagnosis of thyroid cancer by machine learning and clinical data

Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid gland. Much effort has been invested in improving its diagnosis, and thyroidectomy remains the primary treatment method. A successful operation without unnecessary side injuries relies on an accurate preoperative diagnosis. Current human assessment of thyroid nodule malignancy is prone to errors and may not guarantee an accurate preoperative diagnosis. This study proposed a machine learning framework to predict thyroid nodule malignancy based on our collected novel clinical dataset. The ten-fold cross-validation, bootstrap analysis, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The comparison between model prediction and expert assessment shows the advantage of our framework over human judgment in predicting thyroid nodule malignancy. Our method is accurate, interpretable, and thus useable as additional evidence in the preoperative diagnosis of thyroid cancer.

Differentiation of Thyroid Nodules (C-TIRADS 4) by Combining Contrast-Enhanced Ultrasound Diagnosis Model With Chinese Thyroid Imaging Reporting and Data System

Objectives

To establish a contrast-enhanced ultrasound (CEUS) diagnostic schedule by CEUS analysis of thyroid nodules of C-TIRADS 4. To establish a CEUS-TIRADS diagnostic model to differentiate thyroid nodules (C-TIRADS 4) by combining CEUS with Chinese thyroid imaging reporting and data system (C-TIRADS).

Methods

A total of 228 thyroid nodules (C-TIRADS 4) were estimated by CEUS. The arrival time, enhancement degree, enhancement homogeneity, enhancement pattern, enhancement ring, and wash-out time were analyzed in CEUS for all of the nodules. Multivariate factors logistic analysis was performed and a CEUS diagnostic schedule was established. If the nodule had a regular hyper-enhancement ring or got a score of less than 2 in CEUS analysis, CEUS-TIRADS subtracted 1 category. If the nodule got a score of 2 in the CEUS schedule, the CEUS-TIRADS category remained the same as before. If the nodule got a score of more than 2 in the CEUS schedule, CEUS-TIRADS added 1 category. When it reflected an absent enhancement in CEUS, the nodule was judged as CEUS-TIRADS 3. All of the C-TIRADS 4 nodules were re-graded by CEUS-TIRADS. We then compare the diagnosis performance of C-TIRADS, CEUS, and CEUS-TIRADS by sensitivity, specificity, and accuracy.

Results

Among the 228 C-TIRADS 4 nodules, 69 were determined as C-TIRADS 4a, 114 were C-TIRADS 4b, and 45 were C-TIRADS 4c. The sensitivity, specificity, and accuracy of C-TIRADS were 93.1%, 55.3%, and 74.6% respectively. The area under the curve was 0.753. Later arrival time, hypo-enhancement, heterogeneous enhancement, centripetal enhancement, and rapid washout were risk factors of malignancy in multivariate analysis. The sensitivity, specificity, and accuracy of CEUS were 78.7%, 87.5%, and 83.3% respectively. The area under the curve was 0.803. By CEUS-TIRADS diagnostic model combining CEUS with C-TIRADS, a total of 127 cases were determined as malignancy (111 were malignant and 16 were benign) and 101 were diagnosed as benign ones (5 were malignant and 96 were benign). The sensitivity, specificity, and accuracy of CEUS-TIRADS were 95.7%, 85.7%, and 92.1% respectively. The area under the curve was 0.916. The diagnostic performance of CEUS-TIRADS was significantly better than CEUS and C-TIRADS. The difference was statistically significant (P<0.05).

Conclusions

The diagnostic schedule of CEUS could get better diagnostic performance than US in the differentiation of thyroid nodules. The CEUS-TIRADS combining CEUS analysis with C-TIRADS could make up for the deficient sensibility of C-TIRADS, showing a better diagnostic performance than US and CEUS.

Risk of Complications in Patients Undergoing Completion Thyroidectomy after Hemithyroidectomy for Thyroid Nodule with Indeterminate Cytology: An Italian Multicentre Retrospective Study

Simple Summary

The increasing use of high-quality imaging techniques together with improved access to healthcare has led to an increase in the detection of thyroid nodules. Fine-needle aspiration cytology (FNAC) is currently considered the most accurate examination for the assessment of thyroid nodular disease. However, in about 25% of cases, FNAC leads to the diagnosis of an indeterminate thyroid nodule, which represents a problem because malignancy, although relatively low (up to 30%), cannot be excluded with certainty. According to the 2015 American Thyroid Association guidelines, patients with thyroid nodular disease with an indeterminate cytology can undergo, based on established factors, a total thyroidectomy or a hemithyroidectomy. However, if an intermediate or high-risk differentiated thyroid carcinoma is detected after the hemithyroidectomy, through histological examination, the above-mentioned guidelines recommend performing a completion thyroidectomy. The main aim of this study was to assess the rate of complications in patients undergoing a completion thyroidectomy after a hemithyroidectomy for a thyroid nodule with an indeterminate cytology.

Abstract

There is still controversy as to whether patients undergoing a completion thyroidectomy after a hemithyroidectomy for a thyroid nodule with an indeterminate cytology have a comparable, increased or decreased risk of complications compared to those submitted to primary thyroid surgery. The main aim of this study was to investigate this topic. Patients undergoing a thyroidectomy for thyroid nodular disease with an indeterminate cytology in four high-volume thyroid surgery centres in Italy, between January 2017 and December 2020, were retrospectively analysed. Based on the surgical procedure performed, four groups were identified: the TT Group (total thyroidectomy), HT Group (hemithyroidectomy), CT Group (completion thyroidectomy) and HT + CT Group (hemithyroidectomy with subsequent completion thyroidectomy). A total of 751 patients were included. As for the initial surgery, 506 (67.38%) patients underwent a total thyroidectomy and 245 (32.62%) a hemithyroidectomy. Among all patients submitted to a hemithyroidectomy, 66 (26.94%) were subsequently submitted to a completion thyroidectomy. No statistically significant difference was found in terms of complications comparing both the TT Group with the HT + CT Group and the HT Group with the CT Group. The risk of complications in patients undergoing a completion thyroidectomy after a hemithyroidectomy for a thyroid nodule with an indeterminate cytology was comparable to that of patients submitted to primary thyroid surgery (both a total thyroidectomy and hemithyroidectomy).

US-Elastography With Different Techniques for Thyroid Nodule Characterization: Systematic Review and Meta-analysis

Background

Thyroid nodules are frequent in adult population and thyroid cancer incidence has increased dramatically over the past three decades. The aim of this systematic review and meta-analysis was to evaluate the US-Elastosonography (USE) diagnostic performance in assessing the thyroid nodules malignancy risk.

Methods

PubMed and Embase databases were searched from January 2011 to July 2021. We extracted data from selected studies and calculated the overall diagnostic accuracy of qualitative USE, semi-quantitative USE and quantitative USE. Summary receiver operating characteristic (ROC) curve was elaborated to show the results. All statistical tests were performed using Metadisc and Medcal software package.

Results

Finally 72 studies with 13,505 patients and 14,015 thyroid nodules (33% malignant) undergoing elastography were included. The pooled sensitivity, specificity and AUC were 84%, 81%, and 0.89 respectively for qualitative USE; 83%, 80%, and 0.93 for semi-quantitative USE and 78%, 81% and 0.87, for quantitative USE. The qualitative and semiquantitative USE present very similar diagnostic accuracy values and both better than the quantitative USE.

Conclusions

USE is a useful imaging tool for thyroid nodule characterization. In accordance with recent guidelines and meta-analyses, the USE could be used daily in thyroid nodule malignancy risk stratification.

Systematic Review Registration

PROSPERO: CRD42021279257.

The TNAPP web-based algorithm improves thyroid nodule management in clinical practice: A retrospective validation study

BACKGROUND

The detection of thyroid nodules has been increasing over time, resulting in an extensive use of fine-needle aspiration (FNA) and cytology. Tailored methods are required to improve the management of thyroid nodules, including algorithms and web-based tools.

STUDY AIMS

To assess the performance of the Thyroid Nodule App (TNAPP), a web-based, readily modifiable, interactive algorithmic tool, in improving the management of thyroid nodules.

METHODS

One hundred twelve consecutive patients with 188 thyroid nodules who underwent FNA from January to December 2016 and thyroid surgery were retrospectively evaluated. Neck ultrasound images were collected from a thyroid nodule registry and re-examined to extract data to run TNAPP. Each nodule was evaluated for ultrasonographic risk and suitability for FNA. The sensitivity, specificity, positive and negative predictive values, and overall accuracy of TNAPP were calculated and compared to the diagnostic performance of the other two algorithms by the American Association of Clinical Endocrinology/American College of Endocrinology/Associazione Medici Endocrinologi (AACE/ACE/AME), which it was derived from the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS).

RESULTS

TNAPP performed better in terms of sensitivity (>80%) and negative predictive value (68%) with an overall accuracy of 50.5%, which was similar to that found with the AACE/ACE/AME algorithm. TNAPP displayed a slightly better performance than AACE/ACE/AME and ACR TI-RADS algorithms in selectively discriminating unnecessary FNA for nodules with benign cytology (TIR 2 - Bethesda class II: TNAPP 32% vs. AACE/ACE/AME 31% vs. ACR TI-RADS 29%). The TNAPP reduced the number of missed diagnoses of thyroid nodules with suspicious and highly suspicious cytology (TIR 4 + TIR 5 - Bethesda classes V + VI: TNAPP 18% vs. AACE/ACE/AME 26% vs. ACR TI-RADS 20.5%). A total of 14 nodules that would not have been aspirated were malignant, 13 of which were microcarcinomas (92.8%).

DISCUSSION

The TNAPP algorithm is a reliable, easy-to-learn tool that can be readily employed to improve the selection of thyroid nodules requiring cytological characterization. The rate of malignant nodules missed because of inaccurate characterization at baseline by TNAPP was lower compared to the other two algorithms and, in almost all the cases, the tumors were microcarcinomas. TNAPP's use of size >20 mm as an independent determinant for considering or recommending FNA reduced its specificity.

CONCLUSION

TNAPP performs well compared to AACE/ACE/AME and ACR-TIRADS algorithms. Additional retrospective and, ultimately, prospective studies are needed to confirm and guide the development of future iterations that incorporate different risk stratification systems and targets for diagnosing malignancy while reducing unnecessary FNA procedures.

Reconceptualize tall-cell variant papillary thyroid microcarcinoma: From a "sonographic histology" perspective

OBJECTIVE

This study aimed to examine the relationship between sonographic features and histological manifestations in the tall-cell variant of papillary thyroid microcarcinoma (TCV-PTMC), thus proposing the concept of "sonographic histology" and examine its value in the clinical management of the aggressive tall-cell variant.

METHODS

This study retrospectively included 104 participants who were admitted to Peking University Third Hospital from 2015 to 2022 and were histopathologically confirmed as having TCV-PTMC or classical PTMC. We mainly compared the general characteristics, sonographic characteristics, and pathological specimens between the two cohorts.

RESULTS

Hypoechoic nodules with a localized central isoechoic lesion and hypoechoic halo around nodules were most often observed in TCV-PTMC, which correlated with circumferentially distributed tumor epithelium and densely distributed tumor stroma histopathologically. Additionally, TCV-PTMC showed nodules with a more regular margin and less microcalcification than classical PTMC, which led to an underestimation of the risk of TCV-PTMC.

CONCLUSION

The good association between the ultrasound echo pattern and tissue cell arrangement was defined as sonographic histology in this study and can be applied in the preoperative identification of TCV-PTMC. This concept may provide novel insight for the identification of special subtypes of thyroid tumors and may modify pitfalls of the Thyroid Imaging Reporting and Data System in aggressive variants of microcarcinoma.

Contrast-Enhanced Ultrasound: An Effective Method for Noninvasive Diagnosis of Mummified Thyroid Nodules

Mummified thyroid nodules are a special type of thyroid nodule, which is benign, but is often diagnosed as malignant by ultrasound. This study investigated the usefulness of contrast-enhanced ultrasound (CEUS) in the diagnosis of mummified nodules. 66 patients with mummified nodules were divided into two groups: a no-enhancement group and a low-enhancement group. 32 patients with papillary thyroid carcinoma (PTC) were recruited in control group. In the no-enhancement group, CEUS showed that there was no contrast agent entering the nodules, with or without a little dot enhancement or regular ring enhancement around the nodules. The low-enhancement group showed low enhancement inside nodules, which was similar to that in the PTC group. In semiquantitative time-intensity curve analyses, intensity maximum of the central area of nodules in the low-enhancement group was lower than that in the PTC group (P < 0.05) and time to peak of the central area of nodules in the low-enhancement group was lower than that in the PTC group (P < 0.05). The results demonstrate that CEUS could be used to effectively diagnose mummified nodules, obviating the need for patients to undergo invasive examination such as biopsy or even surgery.

Deep Learning Based Fast Screening Approach on Ultrasound Images for Thyroid Nodules Diagnosis

Thyroid nodules are widespread in the United States and the rest of the world, with a prevalence ranging from 19 to 68%. The problem with nodules is whether they are malignant or benign. Ultrasonography is currently recommended as the initial modality for evaluating thyroid nodules. However, obtaining a good diagnosis from ultrasound imaging depends entirely on the radiologists levels of experience and other circumstances. There is a tremendous demand for automated and more reliable methods to screen ultrasound images more efficiently. This research proposes an efficient and quick detection deep learning approach for thyroid nodules. An open and publicly available dataset, Thyroid Digital Image Database (TDID), is used to determine the robustness of the suggested method. Each image is formatted into a pyramid tile-based data structure, which the proposed VGG-16 model evaluates to provide segmentation results for nodular detection. The proposed method adopts a top-down approach to hierarchically integrate high- and low-level features to distinguish nodules of varied sizes by employing fuse features effectively. The results demonstrated that the proposed method outperformed the U-Net model, achieving an accuracy of 99%, and was two times faster than the competitive model.

Poorly Differentiated Thyroid Carcinoma: Single Centre Experience and Review of the Literature

There is controversy in the literature regarding a distinct subset of thyroid carcinoma whose histologically classification falls between well-differentiated and anaplastic carcinomas, previously identified as ‘poorly differentiated thyroid carcinoma’ (PDTC), or ‘insular carcinoma’, in view of the peculiar morphological characteristics of the cell groupings. The correct diagnosis and treatment of this entity have important prognostic and therapeutic significance. In this review, we describe the epidemiology, diagnosis, and management of PDTC and report our single centre experience to add to the limited evidence existing in the literature.

Papillary Thyroid Cancer Prognosis: An Evolving Field

Simple Summary

Over the last couple of decades, the prognostic stratification systems of differentiated thyroid cancer (DTC) patients have been revised several times in an attempt to achieve a tailored clinical management reflecting the single patients’ needs. Such revisions are likely to continue in the near future, since the prognostic value of a number of promising clinicopathological features and new molecular biomarkers are being evaluated. Here, we will review the current staging systems of thyroid cancer patients and discuss the most relevant clinicopathological parameters and new molecular markers that are potentially capable of refining the prognosis.

Abstract

Over the last few years, a great advance has been made in the comprehension of the molecular pathogenesis underlying thyroid cancer progression, particularly for the papillary thyroid cancer (PTC), which represents the most common thyroid malignancy. Putative cancer driver mutations have been identified in more than 98% of PTC, and a new PTC classification into molecular subtypes has been proposed in order to resolve clinical uncertainties still present in the clinical management of patients. Additionally, the prognostic stratification systems have been profoundly modified over the last decade, with a view to refine patients’ staging and being able to choose a clinical approach tailored on single patient’s needs. Here, we will briefly discuss the recent changes in the clinical management of thyroid nodules, and review the current staging systems of thyroid cancer patients by analyzing promising clinicopathological features (i.e., gender, thyroid auto-immunity, multifocality, PTC histological variants, and vascular invasion) as well as new molecular markers (i.e., BRAF/TERT promoter mutations, miRNAs, and components of the plasminogen activating system) potentially capable of ameliorating the prognosis of PTC patients.

Novel Inhibitor-Based Therapies for Thyroid Cancer-An Update

Thyroid cancers (TCs) are the most common tumors of the endocrine system and a constant rise in the number of TC cases has been observed for the past few decades. TCs are one of the most frequent tumors in younger adults, especially in women, therefore early diagnosis and effective therapy are especially important. Ultrasonography examination followed by fine needle biopsy have become the gold standard for diagnosis of TCs, as these strategies allow for early-stage detection and aid accurate qualification for further procedures, including surgical treatment. Despite all the advancements in detection and treatment of TCs, constant mortality levels are still observed. Therefore, a novel generation line of targeted treatment strategies is being developed, including personalized therapies with kinase inhibitors. Recent molecular studies on TCs demonstrate that kinase inhibitor-based therapies might be considered as the most promising. In the past decade, new kinase inhibitors with different mechanisms of action have been reported and approved for clinical trials. This review presents an up-to-date picture of new approaches and challenges of inhibitor-based therapies in treatment of TCs, focusing on the latest findings reported over the past two years.