Outcomes of cytologically indeterminate thyroid nodules managed with Genomic Sequencing Classifier

CONTEXT

Molecular testing can refine the risk of malignancy in thyroid nodules with indeterminate cytology to decrease unnecessary diagnostic surgery.

OBJECTIVE

This study was performed to evaluate the outcomes of cytologically indeterminate thyroid nodules managed with Afirma genomic sequencing classifier (GSC) testing.

DESIGN, SETTING, PATIENTS, AND INTERVENTION

Adult patients who underwent a biopsy at three major academic centers between July 2017 and June 2021 with Bethesda III or IV cytology were included. All patients had surgery or minimum follow-up of 1 year ultrasound surveillance.

MAIN OUTCOME MEASURES

The primary outcomes were the sensitivity, specificity, PPV, and NPV of GSC in Bethesda III and IV nodules.

RESULTS

The median nodule size of the 834 indeterminate nodules was 2.1 cm and the median follow-up was 23 months. GSC's sensitivity, specificity, PPV, and NPV across all institutions were 95%, 81%, 50%, and 99% for Bethesda III nodules and 94%, 82%, 65%, and 98% for Bethesda IV nodules, respectively. The overall false negative rate was 2%. The NPV of GSC in thyroid nodules with oncocytic predominance was 100% in Bethesda III nodules and 98% in Bethesda IV nodules. However, the PPV of oncocytic nodules was low (17% in Bethesda III nodules and 45% in Bethesda IV nodules). Only 22% of thyroid nodules with benign GSC results grew during surveillance.

CONCLUSIONS

GSC is a key tool for managing patients with indeterminate cytology, including the higher-risk Bethesda IV category. GSC benign thyroid nodules can be observed similarly to thyroid nodules with benign cytology.

Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules

BACKGROUND

The low specificity of Thyroid Imaging Reporting and Data System (TI-RADS) for preoperative benign-malignant diagnosis leads to a large number of unnecessary biopsies. This study developed and validated a predictive model based on MRI morphological features to improve the specificity.

METHODS

A retrospective analysis was conducted on 825 thyroid nodules pathologically confirmed postoperatively. Univariate and multivariate logistic regression were used to obtain β coefficients, construct predictive models and nomogram incorporating MRI morphological features in the training cohort, and validated in the validation cohort. The discrimination, calibration, and decision curve analysis of the nomogram were performed. The diagnosis efficacy, area under the curve (AUC) and net reclassification index (NRI) were calculated and compared with TI-RADS.

RESULTS

572 thyroid nodules were included (training cohort: n = 397, validation cohort: n = 175). Age, low signal intensity on T2WI, restricted diffusion, reversed halo sign in delay phase, cystic degeneration and wash-out pattern were independent predictors of malignancy. The nomogram demonstrated good discrimination and calibration both in the training cohort (AUC = 0.972) and the validation cohort (AUC = 0.968). The accuracy, sensitivity, specificity, PPV, NPV and AUC of MRI-based prediction were 94.4%, 96.0%, 93.4%, 89.9%, 96.5% and 0.947, respectively. The MRI-based prediction model exhibited enhanced accuracy (NRI>0) in comparison to TI-RADSs.

CONCLUSIONS

The prediction model for diagnosis of benign and malignant thyroid nodules demonstrated a more notable diagnostic efficacy than TI-RADS. Compared with the TI-RADSs, predictive model had better specificity along with a high sensitivity and can reduce overdiagnosis and unnecessary biopsies.

Interobserver variability in thyroid ultrasound

PURPOSE

Ultrasound evaluation of thyroid nodules is the preferred technique, but it is dependent on operator interpretation, leading to inter-observer variability. The current study aimed to determine the inter-physician consensus on nodular characteristics, risk categorization in the classification systems, and the need for fine needle aspiration puncture.

METHODS

Four endocrinologists from the same center blindly evaluated 100 ultrasound images of thyroid nodules from 100 different patients. The following ultrasound features were evaluated: composition, echogenicity, margins, calcifications, and microcalcifications. Nodules were also classified according to ATA, EU-TIRADS, K-TIRADS, and ACR-TIRADS classifications. Krippendorff's alpha test was used to assess interobserver agreement.

RESULTS

The interobserver agreement for ultrasound features was: Krippendorff's coefficient 0.80 (0.71-0.89) for composition, 0.59 (0.47-0.72) for echogenicity, 0.73 (0.57-0.88) for margins, 0.55 (0.40-0.69) for calcifications, and 0.50 (0.34-0.67) for microcalcifications. The concordance for the classification systems was 0.7 (0.61-0.80) for ATA, 0.63 (0.54-0.73) for EU-TIRADS, 0.64 (0.55-0.73) for K-TIRADS, and 0.68 (0.60-0.77) for K-TIRADS. The concordance in the indication of fine needle aspiration puncture (FNA) was 0.86 (0.71-1), 0.80 (0.71-0.88), 0.77 0.67-0.87), and 0.73 (0.64-0.83) for systems previously described respectively.

CONCLUSIONS

Interobserver agreement was acceptable for the identification of nodules requiring cytologic study using various classification systems. However, limited concordance was observed in risk stratification and many ultrasonographic characteristics of the nodules.

A Narrative Review of the 2023 Korean Thyroid Association Management Guideline for Patients with Thyroid Nodules

The 2023 Korean Thyroid Association (KTA) Management Guideline for Patients with Thyroid Nodules constitute an update of the 2016 KTA guideline for thyroid nodules and cancers that focuses specifically on nodules. The 2023 guideline aim to offer updated guidance based on new evidence that reflects the changes in clinical practice since the 2016 KTA guideline. To update the 2023 guideline, a comprehensive literature search was conducted from January 2022 to May 2022. The literature search included studies, reviews, and other evidence involving human subjects that were published in English in MEDLINE (PubMed), Embase, and other relevant databases. Additional significant clinical trials and research studies published up to April 2023 were also reviewed. The limitations of the current evidence are discussed, and suggestions for areas in need of further research are identified. The purpose of this review is to provide a summary of the 2023 KTA guideline for the management of thyroid nodules released in May 2023 and to give a balanced insight with comparison of recent guidelines from other societies.

Molecular testing for thyroid nodules: Where are we now?

Approximately 25% of the fine needle aspiration samples (FNAB) of thyroid nodules are classified as "indeterminate samples", that means, Bethesda III and IV categories. Until the last decade, most of these cases underwent diagnostic surgery, although only a minority (13-34%) confirmed malignancy postoperatively. In view of this, with the objective of improving the preoperative diagnosis in these cases, the molecular tests emerged, which are validated from the diagnostic point of view, presenting good performance, with good diagnostic accuracy, being able to avoid diagnostic surgeries. With the advancement of knowledge of the role of each of the mutations and gene rearrangements in thyroid oncogenesis, molecular markers have left to play only a diagnostic role and have been gaining more and more space both in defining the prognostic role of the tumor, as well as in the indication of target therapy. Thus, the objective of this review is to show how to use the tool of molecular tests, now commercially available in the world, in the management of indeterminate cytological nodules, assessing the pre-test malignancy risk of the nodule, through clinical, ultrasonographic and cytological characteristics, and decide on the benefit of molecular testing for each patient. In addition, to discuss its new and promising prognostic and therapeutic role in thyroid cancer.

Novel and Advanced Ultrasound Techniques for Thyroid Thermal Ablation

Thyroid radiofrequency ablation and microwave ablation are widely adopted minimally invasive treatments for diverse thyroid conditions worldwide. Fundamental skills such as the trans-isthmic approach and the moving shot technique are crucial for performing thyroid ablation, and advanced techniques, including hydrodissection and vascular ablation, improve safety and efficacy and reduce complications. Given the learning curve associated with ultrasound-guided therapeutic procedures, operators need training and experience. While training models exist, limited attention has been given to ultrasound maneuvers in ablation needle manipulation. This article introduces two essential maneuvers, the zigzag moving technique and the alienate maneuver, while also reviewing the latest ultrasound techniques in thyroid ablation, contributing valuable insights into this evolving field.

US of thyroid nodules: can AI-assisted diagnostic system compete with fine needle aspiration?

OBJECTIVES

Artificial intelligence (AI) systems can diagnose thyroid nodules with similar or better performance than radiologists. Little is known about how this performance compares with that achieved through fine needle aspiration (FNA). This study aims to compare the diagnostic yields of FNA cytopathology alone and combined with BRAFV600E mutation analysis and an AI diagnostic system.

METHODS

The ultrasound images of 637 thyroid nodules were collected in three hospitals. The diagnostic efficacies of an AI diagnostic system, FNA-based cytopathology, and BRAFV600E mutation analysis were evaluated in terms of sensitivity, specificity, accuracy, and the κ coefficient with respect to the gold standard, defined by postsurgical pathology and consistent benign outcomes from two combined FNA and mutation analysis examinations performed with a half-year interval.

RESULTS

The malignancy threshold for the AI system was selected according to the Youden index from a retrospective cohort of 346 nodules and then applied to a prospective cohort of 291 nodules. The combination of FNA cytopathology according to the Bethesda criteria and BRAFV600E mutation analysis showed no significant difference from the AI system in terms of accuracy for either cohort in our multicenter study. In addition, for 45 included indeterminate Bethesda category III and IV nodules, the accuracy, sensitivity, and specificity of the AI system were 84.44%, 95.45%, and 73.91%, respectively.

CONCLUSIONS

The AI diagnostic system showed similar diagnostic performance to FNA cytopathology combined with BRAFV600E mutation analysis. Given its advantages in terms of operability, time efficiency, non-invasiveness, and the wide availability of ultrasonography, it provides a new alternative for thyroid nodule diagnosis.

CLINICAL RELEVANCE STATEMENT

Thyroid ultrasonic artificial intelligence shows statistically equivalent performance for thyroid nodule diagnosis to FNA cytopathology combined with BRAFV600E mutation analysis. It can be widely applied in hospitals and clinics to assist radiologists in thyroid nodule screening and is expected to reduce the need for relatively invasive FNA biopsies.

KEY POINTS

• In a retrospective cohort of 346 nodules, the evaluated artificial intelligence (AI) system did not significantly differ from fine needle aspiration (FNA) cytopathology alone and combined with gene mutation analysis in accuracy. • In a prospective multicenter cohort of 291 nodules, the accuracy of the AI diagnostic system was not significantly different from that of FNA cytopathology either alone or combined with gene mutation analysis. • For 45 indeterminate Bethesda category III and IV nodules, the AI system did not perform significantly differently from BRAFV600E mutation analysis.

Ultrasound microflow patterns help in distinguishing malignant from benign thyroid nodules

BACKGROUND

Vascular features are not commonly used to evaluate thyroid nodules by conventional ultrasound due to the low sensitivity. Superb Microvascular Imaging (SMI) is a new ultrasonic Doppler technology that specializes in depicting microvessels and low-speed flow. The objective of this study was to explore the value of microflow features on SMI in differentiating malignant from benign thyroid nodules.

METHODS

One hundred and seventy-seven adult patients with thyroid nodules in our center from October 2021 to June 2022 with available histopathological results were recruited, including 125 malignant nodules and 123 benign nodules. Preoperative ultrasound was performed using greyscale, Color Doppler Flow Imaging (CDFI), monochrome SMI (mSMI) and color SMI (cSMI). Vascular features such as flow richness, microflow distribution and microflow patterns of malignant thyroid nodules were compared with those of benign nodules.

RESULTS

Penetrating vessel ≥ 1 (82.4% in the malignant group vs. 30.9% in the benign group, P < 0.001), the crab claw-like pattern (64.0% vs. 10.6%, P < 0.001) and the root hair-like pattern (8.0% vs. 2.4%, P = 0.049) were common in malignant thyroid nodules, among which the crab claw-like pattern was an independent risk factor for malignant thyroid nodules. The wheel-like pattern (1.6% in the malignant group vs. 33.3% in the benign group, P < 0.001) and the arborescent pattern (0 vs. 19.5%, P < 0.001) were more likely to appear in benign nodules. The diagnostic specificities of the crab claw-like pattern and the root hair-like pattern for malignant thyroid nodules were 0.894, 0.976, and the positive predictive values were 0.860, 0.769. The diagnostic specificities of the wheel-like pattern and the arborescent pattern for benign thyroid nodules were 0.984, 1.000, and the positive predictive values were 0.953, 1.000.

CONCLUSIONS

The crab claw-like pattern and the root hair-like pattern were microflow characteristics of malignant thyroid nodules. The wheel-like pattern and the arborescent pattern could help exclude the diagnosis of thyroid cancer.

Comparison of artificial intelligence, elastic imaging, and the thyroid imaging reporting and data system in the differential diagnosis of suspicious nodules

Background

Ultrasound is widely used for detecting thyroid nodules in clinical practice. This retrospective study aimed to assess the diagnostic efficacy of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS), S-Detect, and elastography of the carotid artery for suspicious thyroid nodules and to determine the complementary value of artificial intelligence and elastography.

Methods

Between January 2021 and November 2021, 101 consecutive patients with 138 thyroid nodules were enrolled in The First Hospital of China Medical University. All nodules were evaluated using ACR-TIRADS categories (TR), S-Detect, and elastography, and then the diagnostic performance of the different methods and the combined assessment were compared. The inclusion criteria were the following: (I) TR3, TR4, and TR5 nodules, which were defined as "suspicious nodules"; (II) patients who had surgical or cytopathological results after ultrasound examination; and (III) voluntary enrollment in this study. Meanwhile, the exclusion criteria were the following: (I) TR1 and TR2 nodules, (II) patients who had undergone fine-needle aspiration before ultrasound examination, and (III) inconclusive cytologic findings.

Results

A total of 71 patients (12 men and 59 women) with 94 suspicious thyroid nodules (42 benign nodules and 52 malignant nodules) were finally included in this study. S-Detect had a significantly better sensitivity than did ACR-TIRADS [S-Detect: 98.1%, 95% confidence interval (CI): 89.7-100.0%; ACR-TIRADS: 84.6%, 95% CI: 71.9-93.1%; P=0.036], but its specificity was much lower (S-Detect: 19.0%; 95% CI: 8.6-34.1%; ACR-TIRADS: 40.5%, 95% CI: 25.6-56.7%; P=0.032). The accuracy was not significantly different between S-Detect (62.8%; 95% CI: 52.2-72.5%) and ACR-TIRADS (64.9%; 95% CI: 54.4-74.5%) (P=0.761). The elasticity contrast index (ECI) was not definitively useful in identifying suspicious thyroid nodules (P=0.592). Compared with the use of ACR-TIRADS and S-Detect alone, the specificity (45.2%; 95% CI: 29.8-61.3%), positive predictive value (65.2%; 95% CI: 52.4-76.5%), accuracy (66.0%; 95% CI: 55.5-75.4%), and the area under the receiver operating characteristic curve (0.640; 95% CI: 0.534-0.736) of their combination were higher but not significantly so.

Conclusions

At present, S-Detect cannot replace manual diagnosis, and the value of elastography of the carotid artery in diagnosing suspected thyroid nodules remains unclear.

Metastatic thyroid follicular carcinoma presenting as pathological left clavicle fracture: An unusual skeletal metastasis at the time of diagnosis

INTRODUCTION

Thyroid carcinoma is the most common endocrine malignancy, accounting for 3 % of recent malignancies in world wide. Differentiated thyroid carcinoma constitutes 90 % thyroid malignancies, within that follicular type constitutes 10-15 %.

CLINICAL PRESENTATION

A 55-year-old female presented with left-sided neck pain and swelling over medial end of clavicle, following normal manual work. Physical examination revealed swelling on medial end of left clavicle and palpable nodule in left thyroid lobe. Imaging studies showed two nodules in both thyroid lobes with left level IV lymphadenopathy and osteolytic lesion with pathological fracture in medial end of clavicle. Histopathological evaluation confirmed well-differentiated follicular thyroid carcinoma with clavicular metastasis. The patient underwent total thyroidectomy, followed by radioiodine therapy for medial end of left clavicle.

DISCUSSION

Follicular thyroid carcinoma (FTC) is metastasis through the bloodstream, predominantly to flat bones and upper end of long bones, but clavicular deposits are very rarely reported. FNAC only diagnosed the follicular neoplasm. Further tissue evaluation needed to confirm the malignancy. Therefore, hemithyroidectomy of the lesion side is usually carried out for histopathological diagnosis. But in this case, follicular thyroid carcinoma was confirmed through core biopsy from medial end of clavicle, leading to total thyroidectomy and left cervical block dissection, followed by radioiodine therapy for metastatic clavicular involvement.

CONCLUSION

Clavicular metastasis of follicular thyroid carcinoma is very rare. Early detection and proper management of suspicious thyroid carcinoma in uncommon skeletal sites, like the clavicle, is crucial for enhancing patient outcomes, despite the rarity of follicular carcinoma metastasis to this area.

Thyroid US-guided FNA techniques: A prospective,randomized controlled study

BACKGROUND

Thyroid nodules are common in the general population. Ultrasonography is the most efficient diagnostic approach to evaluate thyroid nodules. The US FNAC procedure can be performed using either the short axis (perpendicular), or a long axis (parallel) approach to visualize the needle as it is advanced toward the desired nodule. The main aim of this study was to compare the percentage of non-diagnostic results between the long and short axis approach.

METHODS

A prospective study that included a randomized controlled trial and was divided into two arms-the short axis and the long axis-was conducted. A total of 245 thyroid nodules were collected through the fine needle aspiration cytology, performed with ultrasound, from march 2021 to march 2022. The patient's demographic information were collected and also nodules characteristics.

RESULTS

Of 245 nodules sampled, 122 were sampled with the long axis method, while 123 with the short axis method. There is not significantly less non diagnostic approach with either method compared to the other (11.5 % vs 16.3 % respectively).

DISCUSSION

Previous studies came to the conclusion that the long axis method yields fewer non diagnostic samples. This study evaluated the two FNA approaches which were proceeded by the same physician who is expert in both techniques.

CONCLUSION

The US FNAC performed in the long axis approach will not produce more conclusive results and less non diagnostic results (Bethesda category 1) than the short axis approach one.

Thyroid nodule characterization using Spectral Detector Computed Tomography (SDCT) in comparison to ultrasound

OBJECTIVE

To characterize thyroid nodules seen on Spectral Detector computed tomography (SDCT) in respect to their Thyroid Imaging Reporting and Data System (TI-RADS) category on Ultrasound (US).

METHODS

We included patients that underwent US examination for the evaluation of thyroid nodules and contrast-enhanced SDCT examination of the neck/thorax, between the years 2018-2020. The SDCT and US were performed within 6 months of each other. Only patients with a visible thyroid nodule on SDCT were included. Attenuation measurements of the nodules in Hounsfield units (HU) were performed on the conventional CT images, virtual non-contrast (VNC) images and virtual monoenergetic images of 40 keV and 100 keV. The Iodine concentration, spectral slope and enhancement estimation results of the nodules were measured. We compared the spectral results between two groups of nodules, according to the US report: TI-RADS 2-3 and TI-RADS 4-5 groups.

RESULTS

Thirty-eight nodules were included in the study, 22 nodules in the TI-RADS 2-3 group and 16 in the TI-RADS 4-5 group. The nodules of the TI-RADS 4-5 group had significantly higher Iodine concentration measurement, 4.6 ± 1.8 mg/ml, compared to 2.3 ± 1.2 mg/ml in the TI-RADS 2-3 group; significantly higher estimated enhancement, 3.9 ± 1.5, compared to 2.2 ± 0.7; and significantly higher calculated spectral slope, 5.6 ± 2.2 compared to 2.9 ± 1.5 (p < 0.001).

CONCLUSION

Spectral results of SDCT may assist in differentiating intermediate-high risk (TI-RADS 4-5) from low risk (TI-RADS 2-3) thyroid nodules.

ADVANCES IN KNOWLEDGE

SDCT offers additional information for the characterization of thyroid nodules.

Binary subclassification scheme (AUS-Nuclear versus AUS-Other) adequately risk-stratifies thyroid fine needle aspiration specimens classified as Atypia of Undetermined Significance

INTRODUCTION

The Bethesda System for Reporting Thyroid Cytopathology previously described 4 subclasses of atypia within the Atypia of Undetermined Significance (AUS) category: nuclear (AUS-Nuc), architectural (AUS-A), oncocytic (AUS-Onc), and atypia not otherwise specified (AUS-NOS). Accumulating evidence supports a binary AUS subclassification scheme based primarily on the presence of nuclear atypia only. The purpose of this study is to compare the risk stratification of binary versus 4-tier AUS subclassification systems among AUS nodules with molecular and/or histologic follow-up.

MATERIALS AND METHODS

Thyroid aspirates classified as AUS and tested using Afirma (Veracyte, Inc.) between 6/2013 and 7/2021 were included. For resected nodules, histological classification was considered as the final outcome. For unresected nodules, benign Afirma results were considered low-risk outcomes, similar to histologically benign nodules. Suspicious or nondiagnostic Afirma results were considered indeterminate outcomes. The prevalence of outcomes warranting surgery (noninvasive follicular thyroid neoplasm with papillary-like nuclear features [NIFTP] or cancer) was calculated for each AUS subclass.

RESULTS

A total of 559 AUS nodules with Afirma testing were identified. Excluding nodules with indeterminate molecular outcomes, NIFTP/cancer prevalence for AUS-Nuc was 21% (57/266), which was higher than that for AUS-A (6%, 11/188), AUS-Onc (8%, 4/53), and AUS-NOS (0%, 0/9). A binary AUS subclassification scheme based on nuclear atypia showed a significant difference in NIFTP/cancer prevalence (21% versus 6%, P < 0.0001).

CONCLUSIONS

Binary reporting of AUS subclasses based on nuclear atypia distinguishes cases with a higher risk of NIFTP/cancer. There is a low but non-negligible prevalence of NIFTP/cancer in cases without nuclear atypia.

A knowledge-interpretable multi-task learning framework for automated thyroid nodule diagnosis in ultrasound videos

Ultrasound has become the most widely used modality for thyroid nodule diagnosis, due to its portability, real-time feedback, lack of toxicity, and low cost. Recently, the computer-aided diagnosis (CAD) of thyroid nodules has attracted significant attention. However, most existing techniques can only be applied to either static images with prominent features (manually selected from scanning videos) or rely on 'black boxes' that cannot provide interpretable results. In this study, we develop a user-friendly framework for the automated diagnosis of thyroid nodules in ultrasound videos, by simulating the typical diagnostic workflow used by radiologists. This process consists of two orderly part-to-whole tasks. The first interprets the characteristics of each image using prior knowledge, to obtain corresponding frame-wise TI-RADS scores. Associated embedded representations not only provide diagnostic information for radiologists but also reduce computational costs. The second task models temporal contextual information in an embedding vector sequence and selectively enhances important information to distinguish benign and malignant thyroid nodules, thereby improving the efficiency and generalizability of the proposed framework. Experimental results demonstrated this approach outperformed other state-of-the-art video classification methods. In addition to assisting radiologists in understanding model predictions, these CAD results could further ease diagnostic workloads and improve patient care.

Super-resolution based Nodule Localization in Thyroid Ultrasound Images through Deep Learning

BACKGROUND

Currently, it is difficult to find a solution to the inverse inappropriate problem, which involves restoring a high-resolution image from a lowresolution image contained within a single image. In nature photography, one can capture a wide variety of objects and textures, each with its own characteristics, most notably the high-frequency component. These qualities can be distinguished from each other by looking at the pictures.

OBJECTIVE

The goal is to develop an automated approach to identify thyroid nodules on ultrasound images. The aim of this research is to accurately differentiate thyroid nodules using Deep Learning Technique and to evaluate the effectiveness of different localization techniques.

METHODS

The method used in this research is to reconstruct a single super-resolution image based on segmentation and classification. The poor-quality ultrasound image is divided into several parts, and the best applicable classification is chosen for each component. Pairs of high- and lowresolution images belonging to the same class are found and used to figure out which image is high-resolution for each segment. Deep learning technology, specifically the Adam classifier, is used to identify carcinoid tumors within thyroid nodules. Measures, such as localization accuracy, sensitivity, specificity, dice loss, ROC, and area under the curve (AUC), are used to evaluate the effectiveness of the techniques.

RESULTS

The results of the proposed method are superior, both statistically and qualitatively, compared to other methods that are considered one of the latest and best technologies. The developed automated approach shows promising results in accurately identifying thyroid nodules on ultrasound images.

CONCLUSION

The research demonstrates the development of an automated approach to identify thyroid nodules within ultrasound images using super-resolution single-image reconstruction and deep learning technology. The results indicate that the proposed method is superior to the latest and best techniques in terms of accuracy and quality. This research contributes to the advancement of medical imaging and holds the potential to improve the diagnosis and treatment of thyroid nodules.

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Thyroid Nodules: Emerging Trends in Detection and Visualization based on Citespace

BACKGROUND

Thyroid nodule (TN) is a highly prevalent clinical endocrine disease. Many countries have formed guidelines on the prevention and treatment of TN based on extensive research. However, there is a scarcity of TN-related literature based on bibliometrics.

OBJECTIVES

This study aimed to evaluate the scientific achievements and progress of TN research from a global perspective by investigating the literature for 20 years through bibliometrics.

METHODS

We searched the literature on TN in the core collection of the Web of Science database from 2002 to 2021 and used the Citespace software to analyze the co-authorship, co-citation, and co-occurrence of countries, institutions, authors, keywords, and co-cited literature.

RESULTS

We retrieved 12319 documents related to TN. The literature on TN has been growing since 2002. The United States has contributed the largest proportion of TN papers (20.64%), followed by China, Italy, and South Korea. The United States ranked first in terms of centrality (0.38). Haugen BR, Gharib H, and Cibas ES are the top three most cited authors. The papers published in Thyroid were cited most frequently (7952 times). The most prominent keywords were management, cancer, fine needle aspiration, diagnosis, malignant tumor, thyroid cancer, ultrasound, biopsy, benign, surgery, ablation, and cytology. All keywords could be divided into three categories: diagnosis stratification, treatment, and cancer. As far as potential hot spots are concerned, the keywords that have recently burst strongly and are still continuing are: "Association Guideline" (2018-2021), "Radiofrequency Ablation" (2017-2021), "Classification" (2019-2021), and "Data System" (2017-2021).

CONCLUSION

Based on the current trends, the number of publications on TN will continue to increase. The United States is the most active contributor to research in this field. Previous literature focused on stratification, cancer, surgery, and ablation, and there were different opinions on the stratification of diagnosis. There were relatively few studies on pathogenesis and treatment using medicine. More focus will be placed on association guidelines, radiofrequency ablation, classification, and data system, which may be the next popular topics in TN research.

Appropriate and mindful measurement of serum calcitonin in patients with thyroid nodules. A white paper

Medullary thyroid carcinoma (MTC) is an infrequent thyroid malignancy that is often diagnosed at advanced stage with consequent poor prognosis. Thus, the earlier the diagnosis of MTC, the better the prognosis. Unfortunately, the preoperative detection of MTC remains challenging in clinical practice. In fact, while ultrasound and fine-needle aspiration cytology have suboptimal performance in this context, measuring serum calcitonin (Ctn), fully recognized as the most reliable test to detect MTC, is not universally accepted as routine test in all patients with thyroid nodule(s). The authors of this paper reappraise critically the matter of Ctn measurement in view of the recent advancements in the literature to point out the essential information to be known, and then to prepare an easy-to-use guide for clinicians to appropriately consider the measurement of serum Ctn during clinical practice.

Cancer rates and characteristics of thyroid nodules with macrocalcification

AIMS

The aim of this study was to determine the malignant potential of thyroid nodules with macrocalcifications and to evaluate the role of other sonographic findings in the diagnosis of malignancy in thyroid nodules besides macrocalcifications.

METHODS

The findings of 8250 patients who applied to our outpatient clinic and underwent thyroid ultrasonography(US) between 2008 and 2021 were retrospectively reviewed. We included a total of 296 patients with 296 macrocalcified nodules (macrocalcification group) and an age- and sex matched group of 300 patients (control group) with the cytopathologic and/or histopathologic data of fine-needle aspiration biopsy (FNAB) of thyroid nodules without calcification. Demographic characteristics of these patients, US characteristics of the nodules, and thyroid function tests were recorded. Cytopathological data of FNAB were classified according to BETHESDA.

RESULTS

The malignancy rate was 14.2% (42/296) in the macrocalcification group and 5.3% (16/300) in the control group (p < 0.001). There was no significant relationship between interrupted peripheral calcification and malignancy. Hypoechoic or markedly hypoechoic appearance, irregular border, solid structure, presence of accompanying pathological lymphadenopathy on sonographic examination and upper and middle zone localization were other sonographic features that increased the risk of malignancy of a nodule. The presence of autoimmunity was not found to be associated with the risk of malignancy. TSH and calcitonin levels of malignant nodules were higher than benign nodules. There was no significant difference between gender and malignancy. In the univariate analysis, it was found that the presence of macrocalcification increased the risk of malignancy 2.935 times. (OR:2.935, p < 0.001.95% CI for OR 1.611-5.349) In addition, being younger, being in the high TIRADS category, and being in the upper and middle zones were factors that increased the risk of malignancy. Gender, TSH level, nodule volume and structure were not associated with malignancy. However, after multivariate analysis, factors that significantly increased the risk of malignancy were younger age, higher TIRADS category, and nodule localization.

CONCLUSION

In our study, the malignancy rate was higher in the macrocalcification group than in the control group. However, no correlation was found after multivariate analysis. In the multivariate analysis, younger age, higher TIRADS category, and nodules located in the upper and middle zone were other factors associated with malignancy. There was no association between peripheral interrupted calcification and malignancy risk.

Use of ultrasound elastography in differentiating benign from malignant thyroid nodules: a prospective study

Purpose

Elastography is a real-time non-invasive ultrasonography modality wherein the tissue stiffness is evaluated by calculating the degree of tissue distortion in response to an external force. This study was undertaken to assess the diagnostic value of elastography in differentiating benign from malignant thyroid nodules.

Methodology

In this prospective comparative study, a total of 52 thyroid nodules from 44 euthyroid patients undergoing fine needle aspiration cytology were assessed. Elastography was performed by a single experienced sonologist, wherein the nodules were graded as per elastography scoring (ES), and the strain ratio (SR) for each nodule was computed. Final histopathology findings of the patients undergoing surgery were compared to elastography findings, and measures of diagnostic accuracy to differentiate between benign and malignant nodules were determined for ES and SR.

Results

Thirty (68.2%) females and 14 (31.8%) males, with a mean age of 45.18 ± 11.23 years, were assessed. Fourteen (31.8%) patients underwent thyroidectomy, and histopathology was reported for 18 (34.6%) nodules. In all, nine (17.3%) nodules were malignant, and 43 (82.7%) nodules were considered benign. ES demonstrated a sensitivity of 88.9%, specificity of 88.3%, PPV of 61.5%, NPV of 97.4%, and accuracy of 88.5% to identify benign thyroid nodules. The mean SR for benign nodules was significantly lower as compared to malignant nodules (2.72 ± 0.62 vs. 4.52 ± 0.75, P < 0.0001). The optimal cut-point value for SR to differentiate benign and malignant thyroid nodules was determined to be 3.8, with the sensitivity, specificity, PPV, NPV, and accuracy being 88.9%, 95.4%, 80%, 94.6%, and 94.2%, respectively.

Conclusion

Ultrasound elastography (ES and SR) demonstrated good diagnostic efficacy to differentiate benign thyroid nodules from the malignant ones, and can be a good supplementary tool to gray-scale ultrasonography. It can also help in reducing the rates of unnecessary fine needle-aspiration biopsy.

SDA-Net: Self-distillation driven deformable attentive aggregation network for thyroid nodule identification in ultrasound images

Early detection and accurate identification of thyroid nodules are the major challenges in controlling and treating thyroid cancer that can be difficult even for expert physicians. Currently, many computer-aided diagnosis (CAD) systems have been developed to assist this clinical process. However, most of these systems are unable to well capture geometrically diverse thyroid nodule representations from ultrasound images with subtle and various characteristic differences, resulting in suboptimal diagnosis and lack of clinical interpretability, which may affect their credibility in the clinic. In this context, a novel end-to-end network equipped with a deformable attention network and a distillation-driven interaction aggregation module (DIAM) is developed for thyroid nodule identification. The deformable attention network learns to identify discriminative features of nodules under the guidance of the deformable attention module (DAM) and an online class activation mapping (CAM) mechanism and suggests the location of diagnostic features to provide interpretable predictions. DIAM is designed to take advantage of the complementarities of adjacent layers, thus enhancing the representation capabilities of aggregated features; driven by an efficient self-distillation mechanism, the identification process is complemented with more multi-scale semantic information to calibrate the diagnosis results. Experimental results on a large dataset with varying nodule appearances show that the proposed network can achieve competitive performance in nodule diagnosis and provide interpretability suitable for clinical needs.

Drivers of Thyroid Ultrasound Use: A Retrospective Observational Study

OBJECTIVE

Excessive use of thyroid ultrasound (TUS) contributes to the overdiagnosis of thyroid nodules and thyroid cancer. In this study, we evaluated drivers of and clinical trajectories following TUS orders.

METHODS

We conducted a retrospective review of 500 adult patients who underwent an initial TUS between 2015 and 2017 at Mayo Clinic in Rochester, MN. A framework was employed to classify the indication for TUS, and it was characterized as inappropriate when ordered without a guideline-based indication. Medical records were reviewed for up to 12 months following the TUS, and clinical outcomes were evaluated.

RESULTS

The mean age mean age (SD) was 53.6 years (16.6), 63.8% female, and 86.6% white. TUS orders were triggered by incidental findings on unrelated imaging (31.6%), thyroid symptoms (20.4%), thyroid abnormalities on routine physical examination (17.2%), and thyroid dysfunction workup (11.8%). In females and males, the most common reason were incidental findings on imaging (female, 91/319, 28.5% and male, 67/181, 37.0%). In primary care practice, TUS orders were mostly triggered by symptoms (71/218, 32.5%), while thyroid dysfunction workup was the primary reason in endocrinology (28/100, 28.0%). We classified 11.2% (56/500) TUS orders as likely to have been ordered inappropriately based on current guidelines. Finally, 119 patients (119/500, 23.8%) had a thyroid biopsy with 11.8% had thyroid cancer (14/119. 11.8%).

CONCLUSIONS

Incidental findings on imaging, symptoms, and routine physical exam findings in asymptomatic patients were the most prevalent drivers of TUS. Furthermore, 1 in 10 TUS were likely inappropriately ordered based on current practice guidelines.

Modification of size cutoff for biopsy based on the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) for thyroid nodules in patients younger than 19 years

OBJECTIVES

To modify the size cutoff for biopsy for thyroid nodules in patients < 19 years based on the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) and evaluate the performance of the new criteria in two referral centers.

METHODS

Patients < 19 years with cytopathologic or surgical pathology results were retrospectively identified from two centers from May 2005 to August 2022. Patients from one center were classified as the training cohort, and those from the other center were classified as the validation cohort. The diagnostic performance, unnecessary biopsy rates, and missed malignancy rates of the TI-RADS guideline, and the new criteria (≥ 35 mm for TR3 and no threshold for TR5) were compared.

RESULTS

A total of 236 nodules from 204 patients in the training cohort and 225 nodules from 190 patients in the validation cohort were analyzed. The area under the receiver operating characteristic curve of the new criteria in identifying thyroid malignant nodules was higher (0.809 vs. 0.681, p < 0.001; 0.819 vs. 0.683, p < 0.001), and the unnecessary biopsy rates (45.0% vs. 56.8%; 42.2% vs. 56.8%) and missed malignancy rates (5.7% vs. 18.6%; 9.2% vs. 21.5%) were lower than that of the TI-RADS guideline in the training cohort and validation cohort, respectively.

CONCLUSIONS

The new criteria (≥ 35 mm for TR3 and no threshold for TR5) for biopsy based on the TI-RADS may help improve the diagnostic performance and reduce unnecessary biopsy rates and missed malignancy rates for thyroid nodules in patients < 19 years.

CLINICAL RELEVANCE STATEMENT

The study developed and validated the new criteria (≥ 35 mm for TR3 and no threshold for TR5) to indicate FNA based on the ACR TI-RADS of thyroid nodules in patients younger than 19 years.

KEY POINTS

•The AUC of the new criteria (≥ 35 mm for TR3 and no threshold for TR5) in identifying thyroid malignant nodules was higher than that of the TI-RADS guideline (0.809 vs. 0.681) in patients < 19 years. •The unnecessary biopsy rates and missed malignancy rates of the new criteria (≥ 35 mm for TR3 and no threshold for TR5) in identifying thyroid malignant nodules were lower than that of the TI-RADS guideline in patients < 19 years (45.0% vs. 56.8% and 5.7% vs. 18.6%, respectively).

Learnability of Thyroid Nodule Assessment on Ultrasonography: Using a Big Data Set

OBJECTIVE

The aims of the work described here were to evaluate the learnability of thyroid nodule assessment on ultrasonography (US) using a big data set of US images and to evaluate the diagnostic utilities of artificial intelligence computer-aided diagnosis (AI-CAD) used by readers with varying experience to differentiate benign and malignant thyroid nodules.

METHODS

Six college freshmen independently studied the "learning set" composed of images of 13,560 thyroid nodules, and their diagnostic performance was evaluated after their daily learning sessions using the "test set" composed of images of 282 thyroid nodules. The diagnostic performance of two residents and an experienced radiologist was evaluated using the same "test set." After an initial diagnosis, all readers once again evaluated the "test set" with the assistance of AI-CAD.

RESULTS

Diagnostic performance of almost all students increased after the learning program. Although the mean areas under the receiver operating characteristic curves (AUROCs) of residents and the experienced radiologist were significantly higher than those of students, the AUROCs of five of the six students did not differ significantly compared with that of the one resident. With the assistance of AI-CAD, sensitivity significantly increased in three students, specificity in one student, accuracy in four students and AUROC in four students. Diagnostic performance of the two residents and the experienced radiologist was better with the assistance of AI-CAD.

CONCLUSION

A self-learning method using a big data set of US images has potential as an ancillary tool alongside traditional training methods. With the assistance of AI-CAD, the diagnostic performance of readers with varying experience in thyroid imaging could be further improved.

Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study

BACKGROUND

Calcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and malignant thyroid calcified nodules.

METHODS

This retrospective study, conducted at two centers, involved a total of 631 thyroid nodules, all of which were pathologically confirmed. Ultrasound image sets were employed for analysis. The primary evaluation index was the area under the receiver-operator characteristic curve (AUROC). We compared the diagnostic performance of deep learning (DL) methods with that of radiologists and determined whether DL could enhance the diagnostic capabilities of radiologists.

RESULTS

The Xception classification model exhibited the highest performance, achieving an AUROC of up to 0.970, followed by the DenseNet169 model, which attained an AUROC of up to 0.959. Notably, both DL models outperformed radiologists (P < 0.05). The success of the Xception model can be attributed to its incorporation of deep separable convolution, which effectively reduces the model's parameter count. This feature enables the model to capture features more effectively during the feature extraction process, resulting in superior performance, particularly when dealing with limited data.

CONCLUSIONS

This study conclusively demonstrated that DL outperformed radiologists in differentiating between benign and malignant calcified thyroid nodules. Additionally, the diagnostic capabilities of radiologists could be enhanced with the aid of DL.