Malignant Thyroid Nodules Research Articles

Risk stratification of thyroid nodules: Assessing the suitability of ChatGPT for text-based analysis

PurposeAccurate risk stratification of thyroid nodules is essential for optimal patient management. This study aimed to assess the suitability of ChatGPT for risk stratification of thyroid nodules using a text-based evaluation. MethodsA dataset was compiled comprising 50 anonymized clinical reports and associated risk assessments for thyroid nodules. The Chat Generative Pre-trained Transformer (ChatGPT) was used to classify sonographic patterns in accordance with the Thyroid Imaging Reporting and Data System (TI-RADS). The model's performance was assessed using various criteria, including sensitivity, specificity, and accuracy. A comparative analysis was conducted, evaluating the model against investigator-based risk stratification as well as histology. ResultsWith an overall agreement rate of 42 % in comparison with examiner-based evaluation (TI-RADS 1–5), the results show that ChatGPT has moderate potential for predicting the risk of malignancy in thyroid nodules using text-based reports. The chatbot model achieved a sensitivity of 86.7 %, a specificity of 10.7 %, and an overall accuracy of 68 % when distinguishing between low-risk (TI-RADS 2 and 3) and high-risk (TI-RADS 4 and 5) categories. Interrater reliability was calculated with a Cohen's kappa of 0.686. ConclusionThis study highlights the potential of ChatGPT in assisting clinicians with risk stratification of thyroid nodules. The results suggest that ChatGPT can facilitate personalized treatment decisions, although the agreement rate is still low. Further research and validation studies are necessary to establish the clinical applicability and generalizability of ChatGPT in routine practice. The integration of ChatGPT into clinical workflows has the potential to enhance thyroid nodule risk assessment and improve patient care.

Clinical and molecular genetic analysis of cytologically uncertain thyroid nodules in patients with thyroid disease.

The current requirement is to establish the preoperative diagnosis accurately as possible and to achieve an adequate extent of surgery. The aim of this study was to define the preoperative clinical and molecular genetic risks of malignancy in indeterminate thyroid nodules (Bethesda III and IV) and to determine their impact on the surgical strategy. Prospectively retrospective analysis of 287 patients provided the basis of preoperative laboratory examination, sonographic stratification of malignancy risks and cytological findings. Molecular tests focused on pathogenic variants of genes associated with thyroid oncogenesis in cytologically indeterminate nodules (Bethesda III and IV). The evaluation included clinical risk factors: positive family history, radiation exposure and growth in size and/or number of nodules. Preoperative FNAB detected 52 cytologically indeterminate nodules (28.7%) out of 181 patients. Postoperative histopathological examination revealed malignancy in 12 cases (23.7%) and there was no significant difference between Bethesda III and IV categories (P=0.517). Clinical risk factors for malignancy were found in 32 patients (61.5%) and the presence of at least one of them resulted in a clearly higher incidence of malignancy than their absence (31.3% vs. 10.0%, respectively). Pathogenic variants of genes were detected in 12/49 patients in Bethesda III and IV, and in 4 cases (33.3%) thyroid carcinoma was revealed. The rate of malignancies was substantially higher in patients with pathogenic variants than in those without (33.3% vs. 16.2%, respectively). Our experience implies that molecular genetic testing is one of several decision factors. We will continue to monitor and enlarge our patient cohort to obtain long-term follow-up data.

DNA Methylation Status of HYAL1 in Malignant and Benign Thyroid Nodules.

Differentiation between benign and malignant thyroid nodules has been a challenge in clinical practice. Exploring a novel biomarker to determine the malignancy of thyroid nodules has important implications. We semi-quantitatively determined the DNA methylation levels of four CpG sites located at the gene body of HYAL1 in formalin-fixed paraffin-embedded (FFPE) tissue samples from 190 early-stage papillary thyroid cancer (PTC) cases and 190 age- and gender-matched subjects with benign thyroid nodule (BTN). HYAL1 expression was evaluated by immunohistochemical (IHC) staining in another cohort of 55 PTC and 55 matched BTN cases. Covariates-adjusted odds ratios (ORs) for 10% increased methylation were calculated by binary logistic regression. A 165 bp amplicon covering four CpG sites at the second exon of HYAL1 gene was designed. After adjusted for all covariates, higher methylation level of HYAL1_CpG_3,4 in the FFPE tissue was associated with PTC (OR per 10% increased methylation=1.53, p=0.025), even with stage І PTC (OR per 10% increased methylation=1.58, p=0.021). Hypermethylation of HYAL1_CpG_3,4 had a significant association with early-stage PTC in the females (OR per 10% increased methylation=1.60, p=0.028) rather than in the males. Besides, we found the higher expression of HYAL1 protein in PTC than that in BTN patients (IHC score: 2.3 vs. 0.5, p=1.00E-06). Our study suggested altered methylation and expression of HYAL1 could be a novel and potential biomarker in distinguishing malignant and benign thyroid nodules.

Deep learning diagnostic performance and visual insights in differentiating benign and malignant thyroid nodules on ultrasound images.

This study aims to construct and evaluate a deep learning model, utilizing ultrasound images, to accurately differentiate benign and malignant thyroid nodules. The objective includes visualizing the model's process for interpretability and comparing its diagnostic precision with a cohort of 80 radiologists. We employed ResNet as the classification backbone for thyroid nodule prediction. The model was trained using 2096 ultrasound images of 655 distinct thyroid nodules. For performance evaluation, an independent test set comprising 100 cases of thyroid nodules was curated. In addition, to demonstrate the superiority of the artificial intelligence (AI) model over radiologists, a Turing test was conducted with 80 radiologists of varying clinical experience. This was meant to assess which group of radiologists' conclusions were in closer alignment with AI predictions. Furthermore, to highlight the interpretability of the AI model, gradient-weighted class activation mapping (Grad-CAM) was employed to visualize the model's areas of focus during its prediction process. In this cohort, AI diagnostics demonstrated a sensitivity of 81.67%, a specificity of 60%, and an overall diagnostic accuracy of 73%. In comparison, the panel of radiologists on average exhibited a diagnostic accuracy of 62.9%. The AI's diagnostic process was significantly faster than that of the radiologists. The generated heat-maps highlighted the model's focus on areas characterized by calcification, solid echo and higher echo intensity, suggesting these areas might be indicative of malignant thyroid nodules. Our study supports the notion that deep learning can be a valuable diagnostic tool with comparable accuracy to experienced senior radiologists in the diagnosis of malignant thyroid nodules. The interpretability of the AI model's process suggests that it could be clinically meaningful. Further studies are necessary to improve diagnostic accuracy and support auxiliary diagnoses in primary care settings.

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

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. 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. 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. 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.

RUNX1 methylation as a cancer biomarker in differentiating papillary thyroid cancer from benign thyroid nodules.

Aim: It remains a challenge to accurately identify malignancy of thyroid nodules when biopsy is indeterminate. The authors aimed to investigate the abnormal DNA methylation signatures in papillary thyroid cancer (PTC) compared with benign thyroid nodules (BTNs). Methods: The authors performed genome profiling by 850K array and RNA sequencing in early-stage PTC and BTN tissue samples. The identified gene was validated in two independent case-control studies using mass spectrometry. Results: Hypomethylation of RUNX1 in PTC was identified and verified (all odds ratios: ≥1.50). RUNX1 methylation achieved good accuracy in differentiating early-stage PTC from BTNs, especially for younger women. Conclusion: The authors disclosed a significant association between RUNX1 hypomethylation and PTC, suggesting RUNX1 methylation as a potential biomarker for companion diagnosis of malignant thyroid nodules.

Application of Ultrasound Shear Wave Elastography Combined With BRAFV600E Mutation for the Diagnosis of Papillary Thyroid Carcinoma.

This prospective study aimed to investigate the combination of shear wave elastography (SWE) and BRAFV600E mutation testing for the diagnosis of papillary thyroid carcinoma (PTC). One hundred thyroid nodules with an American College of Radiology thyroid imaging reporting and data system classification of 4 or 5 were subjected to SWE measurement, BRAFV600E genotyping, fine-needle aspiration, and surgery. Nonparametric statistical tests were used to compare the differences in the elastic parameters and BRAF genotypes between benign and malignant thyroid nodules of PTC, and receiver operating characteristic curve analysis was conducted to compare the diagnostic efficacy. In addition, the correlations between elastic parameters and BRAFV600E mutation in PTC were analyzed using binary logistic regression. The SWE, BRAFV600E, and their combination exhibited sensitivities of 72.9%, 81.3%, and 85.4%, respectively, and specificities of 66.7%, 90.3%, and 86.5%, respectively, in the diagnosis of PTC ( P < 0.05). The SWE, BRAFV600E, and their combination exhibited sensitivities of 50.0%, 24.1% and 56.3%, respectively, and specificities of 89.7%, 87.5% and 82.8%, respectively, in the diagnosis of central cervical lymph node metastasis ( P < 0.05). The combined use of SWE and BRAFV600E detection had the largest area under the curve, indicating that this combination is more effective in diagnosing PTC and lymph node metastasis in the central region than either method alone. Furthermore, Emax was positively associated with the BRAFV600E genotype. In conclusion, the combination of SWE and BRAFV600E genotype detection can improve the diagnostic efficacy for PTC. Emax can predict the BRAFV600E mutation status.

Contrast Enhancement Ultrasound Improves Diagnostic Accuracy for Thyroid Nodules: A Prospective Multicenter Study

Abstract Objective To evaluate potential improvements in the diagnosis of thyroid nodules when conventional ultrasound (US) is combined with contrast-enhanced US (CEUS). Methods We recruited 515 participants with 323 malignant and 192 benign nodules, who underwent both US and CEUS examinations at 8 different medical centers in China between October 2020 and October 2021. We assessed the malignancy of thyroid nodules in US using the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TIRADS). Diagnostic criteria for US and US + CEUS were developed by investigators based on evaluations of sonographic features. Using multivariate logistic regression and receiver operating characteristic (ROC) analysis, we compared diagnostic performance between the 2 methods based on criteria identified by investigators and via statistical models. Results On the basis of diagnostic criteria identified by investigators, we measured statistically significant differences in area under the curve (AUC) values between ACR TIRADS (0.83) and CEUS TIRADS (0.87; P &amp;lt; .001). On the basis of diagnostic regression models, we found statistically significant differences in AUC values between US (0.76) and US + CEUS (0.84; P = .001). Models based on US + CEUS outperformed those based on US alone (Akaike information criterion of 347.7 and significant improvement in integrated discrimination). These results were confirmed by similar analyses applied to a validation cohort. Conclusion The accuracy of conventional US for differentiating between benign and malignant thyroid nodules can be improved by combining this approach with CEUS.

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

BackgroundCalcification 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.MethodsThis 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.ResultsThe 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.ConclusionsThis 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.

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.

Stratifying Indeterminate Cytology Thyroid Nodules by Combining Thyroid Imaging Reporting and Data Systems (TI-RADS) and The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC)

Objective: To determine the risk of malignancy of Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) indeterminate Thyroid Nodules (Bethesda III, IV and V) by combining cytologic (TSBRTC) and Thyroid Imaging Reporting and Data Systems (TI-RADS) ultrasonographic features based on final histopathology. Methods:Design: Retrospective review of recordsSetting: Tertiary Private Training HospitalParticipants: 551 recordsResults: Among 81 eligible participants, 59 out of 84 nodules (70.24%) wer malignant on histopathology. The malignancy risk of Bethesda classification was 60.87% (28 out of 46) for Bethesda III, 57.14% (8 out of 14) for Bethesda IV and 95.83% for Bethesda V. The malignancy risk for TI-RADS categories was 0 % (0/1) for TI-RADS 2, 50% (10 out of 20) for TI-RADS 3, 71.05 % for TI-RADS 4 and 91.67 % for TI-RADS 5. The highest risk of malignancy (100%) was associated with [Bethesda IV/TI-RADS 1, 2, and 3], [Bethesda V/TI-RADS 1, 2 and 3 [Bethesda IV and V/TI-RADS 1, 2 and 3] and [Bethesda IV/TI-RADS 5]. The lowest risk of malignancy (33.33%) was associated with [Bethesda III/TI-RADS1, 2 and 3]. A high Bethesda classification (Bethesda V) was almost 5x more likely to have a malignant anatomorphology compared with Bethesda III (p = .05) while a TI-RADS 4 or 5 category was almost 5x more likely to have a malignant anatomorphology compared to TI-RADS 1, 2 or 3 (p = .026). Conclusion: This study showed that TI-RADS scoring is a sensitive diagnostic classification in recognizing patients with thyroid cancer and combining Bethesda classification and TI-RADS scoring increases the sensitivity in the diagnosis of malignant thyroid nodules. A higher likelihood of malignancy is associated with higher Bethesda classification and TI-RADS scoring.

Malignancy in Thyroid Nodules with Bethesda III Category on Repeat Fine Needle Aspiration Biopsy.

This study aimed to evaluate the risk of malignancy for nodules repeatedly classified as Bethesda category III on fine needle aspiration biopsy (FNAB). A chart review on a series of 59 patients seen with thyroid nodules who underwent both initial and repeat FNAB at the Diabetes, Thyroid and Endocrine Center of St. Luke's Medical Center, Quezon City was conducted. The Thyroid Registry was utilized to collect each patient's demographic and clinical characteristics, ultrasonographic features of thyroid nodules along with the cytopathologic and histopathologic results. The subclassification of atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) were retrieved from the cytopathology reports using the institution's electronic Healthcare-Results Management System. A total of 59 adult patients with thyroid nodules were included. Nodules which were initially AUS/FLUS turned out to be malignant on repeat FNAB in 38 patients with a prevalence of 64.41% (95% CI: 50.87-76.45%). There was no significant difference with regards to clinical, ultrasonographic and cytopathologic features of malignancy between benign and malignant nodules. Findings of this study support surgical intervention as a reasonable option after a repeat Bethesda III classification on FNAB. However, the small sample size warrants confirmation in future studies with a representative sample of patients.

DIAGNOSTIC ACCURACY OF ULTRASONOGRAPHY IN DIFFERENTIATING BENIGN AND MALIGNANT THYROID NODULES

This study aimed to compare the effectiveness of ultrasonography and fine-needle aspiration cytology (FNAC) in distinguishing between benign and malignant thyroid nodules. To conduct this cross-sectional study, we obtained ethical approval from the institutional review board and collected data from KRL Hospital between December 1, 2021, and April 30, 2022. We included patients of either gender, aged 18 years or above, referred to the radiology department for diagnosis of benign or malignant thyroid nodules or FNAC of nodules, using non-probability consecutive sampling. After giving informed consent, all patients underwent high-resolution Gray ultrasonography performed by a consultant radiologist. The results showed that ultrasonography had a sensitivity of 90.62%, specificity of 88.37%, positive predictive value of 85.29%, negative predictive value of 92.68%, and an overall accuracy of 89.33% in detecting benign and malignant thyroid nodules, as compared to the gold standard, FNAC. Based on these findings, we conclude that ultrasonography is an excellent non-invasive diagnostic tool, with high accuracy and no invasiveness, making it the preferred method for identifying malignant thyroid nodules.

Value of radiomics models based on MRI diffusion weighted imaging and apparent diffusion coefficient in differentiating benign and malignant thyroid nodules

Objective: To investigate the value of radiomics models based on magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps in distinguishing benign and malignant thyroid nodules. Methods: A cross-sectional study. Clinical data of 148 thyroid nodules (50 benign, 98 malignant) from 140 patients who underwent thyroid MRI examination in Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences between January 2019 and December 2022 were retrospectively analyzed. The nodules were used as the study units, and a leave-one-out method was used to randomly divide the nodules into a training set and a test set at a 7∶3 ratio. Region of interest was segmented and radiomics features were extracted from the DWI and ADC images. In the training set, feature selection was performed using inter-observer agreement analysis, U-test, least absolute shrinkage and selection operator algorithm, and correlation analysis. Four classifiers, including support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR) were used to build models with the selected features, including the DWI models, ADC models, and combined models. The models were independently tested in the test set. The performance of the radiomics models in distinguishing benign and malignant thyroid nodules was evaluated using the receiver operating characteristic (ROC) curve, with pathological results as the gold standard. Results: Of the 140 patients, there were 40 males and 100 females, with a mean age of (38.4±12.2) years. After feature selection, 11 DWI features and 11 ADC features were used to build the models. In the training set, the AUC values of the combined models were higher than those of the corresponding DWI and ADC models. In the test set, the SVM combined model showed the best predictive performance, with an AUC of 0.873 (95%CI:0.740-0.954), accuracy of 75.6%, sensitivity of 46.7%, specificity of 90.0%, positive predictive value (PPV) of 70.0% and negative predictive value (NPV) of 77.1%, while the RF combined model had an AUC of 0.836 (95%CI:0.695-0.929), accuracy of 77.8%, sensitivity of 40.0%, specificity of 96.7%, PPV of 85.7% and NPV of 76.3%, the KNN combined model had an AUC of 0.832 (95%CI:0.691-0.927), accuracy of 77.8%, sensitivity of 33.3%, specificity of 100%, PPV of 100% and NPV of 75.0%, the LR combined model had an AUC of 0.813 (95%CI:0.669-0.914), accuracy of 77.8%, sensitivity of 60.0%, specificity of 86.7%, PPV of 69.2% and NPV of 81.3%. Conclusions: Radiomics models based on DWI and ADC image features can effectively distinguish benign and malignant thyroid nodules. The SVM combined model had the best prediction performance.

A Systematic Review and Meta-analysis on Ultrasound Detection of Thyroid Cancer in China

Background: One major drawback of using ultrasound for diagnosing thyroid nodules is its limited ability to distinguish between benign and malignant nodules. In China, the common methods for risk stratification and guiding fine needle aspiration (FNA) in diagnosing thyroid nodules are the Chinese Thyroid Imaging Reports and Data Systems (C-TIRADS) and American College of Radiology-Thyroid Imaging Reporting and Data System (ACR-TIRADS). Objectives: This review seeks to assess the effectiveness of C-TIRADS and ACR-TIRADS in accurately identifying the risk of malignancy in Chinese patients suspected of thyroid cancer. Methods: A detailed search was conducted in PubMed, Google Scholar, Medline, Embase, Web of Science, Cochrane, and China National Knowledge Infrastructure (CNKI) databases from January 2018 to December 2022. The analysis only considered original articles from China reporting the use of C-TIRADS and ACR-TIRADS confirmed by histology and FNA. Results: This review analyzed 26 studies with a total of 23,064 thyroid nodules from 19,114 patients to compare the diagnostic performance of C-TIRADS and ACR-TIRADS in predicting malignancy risk in thyroid nodules. Although the malignancy rates in each risk category were similar between the two systems, the TIRADS showed better diagnostic performance than C-TIRADS in terms of pooled specificity (95.0 % vs. 66.8 % of C-TIRADS). However, the pooled analysis showed that C-TIRADS had a better pooled sensitivity (94.6 % vs. 76.5% of ACR-TIRADS). The diagnostic odds ratio was 1.37 (95 % CI: 0.75-2.51) for ACR-TIRADS and 0.89 (95 % CI: 0.36-2.16) for C-TIRADS. Conclusion: Based on the results, both C-TIRADS and ACR-TIRADS are effective in predicting the risk of malignancy in thyroid nodules with similar overall diagnostic accuracy. The combination of both systems can be beneficial in enhancing accuracy in suspicious or uncertain cases. The long-term experience of the trained radiologists can readily help in concluding the diagnosis. As no single system or combination of systems can provide a 100% accurate prediction of the malignancy of thyroid nodules, the ultimate diagnosis relies on the concluding assessment of experienced radiologists and the medical team.

Significance of LINC02082 and LOC105369812 in differentiating papillary thyroid cancer from benign nodules.

To explore the significance of LINC02082 and LOC105369812 in the differential diagnosis of papillary thyroid carcinoma (PTC) and benign nodules. Cancer tissues and benign nodules from 8 patients were sequenced and constructed using high-throughput sequencing. Differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) with significant differences were screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the mRNAs co-expressed by DEmRNAs and DElncRNAs. LncRNAs with significant differences, good consistency, and enrichment in the PI3K-AKt signaling pathway were selected as candidate lncRNAs, and the target lncRNAs were screened by correlation analysis. Target lncRNAs and co-expressed mRNAs enriched in the PI3K-AKt signaling pathway and microRNAs (miRNAs) interacting with each other were used to construct a competing endogenous RNA (ceRNA) network. Finally, the PTC-related gene set (GSE33630) was downloaded from the GEO database and the expression of the genes obtained by sequencing was compared. Differential expression was verified using quantitative real-time PCR (qRT-PCR). Finally, the receiver operating characteristic (ROC) curve was used to evaluate the value of the target lncRNAs in diagnosis, when used alone or in combination. A total of 1113 differential RNAs (DE RNAs) were identified, of which 338 were DElncRNAs and 775 were DEmRNAs. Three lncRNAs enriched in the PI3K-AKt signaling pathway, LINC02082, LOC105369812, and LOC105375170, were used as candidate lncRNAs. After correlation analysis with known biomarkers, LINC02082 and LOC105369812 were selected as the target lncRNAs. The qRT-PCR results showed that the target lncRNAs were significantly different among the 3 tissues. The ROC curve showed that LOC105369812 could be used to differentiate PTC from benign thyroid nodules, whereas LINC02082 and its combination had lower predictive value. LOC105369812 is valuable for differentiating benign from malignant thyroid nodules, whereas LINC02082 has lower diagnostic value.

Ultrasound Image Classification of Thyroid Nodules Based on Attention Mechanism

Ultrasound imaging of the thyroid gland is currently the most common diagnostic technique, used for pre diagnosis of benign and malignant thyroid nodules. During the clinical diagnosis, the condition judgment requires the high clinical experience of physicians. The low level of medical care in remote areas has led to an increased rate of misdiagnosis of benign and malignant nodules. To improve the accuracy of diagnosis and treatment effectiveness of doctors, this work proposes a benign and malignant thyroid classification model based on mechanisms of attention. First, this paper performs data cleaning on the collected data and performs pre-processing operations such as data enhancement to construct a new dataset. Then, a network model based on Lightweight Global Attention Module (LGAM) is constructed for the benign and malignant classification of nodules. Finally, it is shown by experimental analysis and comparison that the improved LGAM-based thyroid node classification model has achieved better experimental results on the dataset and can achieve the optimal classification accuracy of 95.16% for the network model proposed in this work.

The predictive value of the thyroid nodule benign and malignant based on the ultrasound nodule-to-muscle gray-scale ratio.

To investigate the efficacy of the ultrasonic nodule to muscle gray scale ratio as a predictive tool for distinguishing between benign and malignant thyroid nodules. A retrospective study was undertaken at the First People's Hospital of Hangzhou, affiliated with the Zhejiang University School of Medicine, analyzing ultrasound and pathological data of patients with thyroid nodules between May 2020 and December 2022. The study extracted ultrasound features of nodules and employed univariate and multivariate logistic regression analyses to identify independent risk factors for malignant tumors in the nodules. Subsequently, a predictive model for distinguishing benign and malignant thyroid nodules was developed. A total of 466 patients were included in this retrospective study, of which 275 cases were malignant tumors. Univariate and multivariate logistic regression analyses showed that the nodular-muscle gray-scale ratio, nodule diameter, margin status, aspect ratio, and calcification were closely related to thyroid malignant tumors. The area under the curve (AUC) of training group was 0.832, with a sensitivity, specificity, and accuracy of 85.5%, 67.4%, and 76.6%, respectively. The AUC of the external validation group was 0.819, with a sensitivity, specificity, and accuracy of 76.4%, 74.5%, and 75.7%, respectively. The calibration and decision curves showed that the model had good diagnostic value. The research findings indicate that ratio is significantly associated with the malignant nature of thyroid nodules. The application of a line chart model based on these parameters exhibits a high level of predictive performance.

Impact of Region-of-Interest Size on the Diagnostic Performance of Shear Wave Elastography in Differentiating Thyroid Nodules.

This study investigated the impact of different region-of-interest (ROI) sizes (Max, 1 mm, and 2 mm) on shear wave elastography (SWE) in differentiating between malignant and benign thyroid nodules. The study cohort comprised 129 thyroid nodules (50 malignant, 79 benign) and 78 normal subjects. Diagnostic efficacy was assessed through pairwise comparisons of area under the curve (AUC) values in receiver operating characteristic analysis by using DeLong's test. Our results indicated significant differences in all SWE elasticity metrics between the groups, with malignant nodules exhibiting higher values than benign nodules (p < 0.05). Smaller ROIs (1 and 2 mm) were found to outperform the max ROI in terms of diagnostic accuracy, particularly for the Emax and Emin elasticity metrics. Emax(1mm) had the highest diagnostic accuracy, with an AUC of 0.883, sensitivity of 74.0%, and specificity of 86.1%. This study underscores the significant influence of ROI size selection on the diagnostic performance of SWE, offering valuable insights for future research and clinical applications in thyroid nodule assessment.

Incidental 68Ga-DOTATATE uptake in thyroid nodules: Is guideline-directed management still appropriate?

BackgroundFluorodeoxyglucose uptake on positron emission tomography imaging has been shown to be an independent risk factor for malignancy in thyroid nodules. More recently, a new positron emission tomography radiotracer—Gallium-68 DOTATATE—has gained popularity as a sensitive method to detect neuroendocrine tumors. With greater availability of this imaging, incidental Gallium-68 DOTATATE uptake in the thyroid gland has increased. It is unclear whether current guideline-directed management of thyroid nodules remains appropriate in those that are Gallium-68 DOTATATE avid. MethodsWe retrospectively reviewed Gallium-68 DOTATATE positron emission tomography scans performed at our institution from 2012 to 2022. Patients with incidental focal Gallium-68 DOTATATE uptake in the thyroid gland were included. Fine needle aspiration biopsies were characterized via the Bethesda System for Reporting Thyroid Cytopathology. Bethesda III/IV nodules underwent molecular testing (ThyroSeq v3), and malignancy risk ≥50% was considered positive. ResultsIn total, 1,176 Gallium-68 DOTATATE PET scans were reviewed across 837 unique patients. Fifty-three (6.3%) patients demonstrated focal Gallium-68 DOTATATE thyroid uptake. Nine patients were imaged for known medullary thyroid cancer. Forty-four patients had incidental radiotracer uptake in the thyroid and were included in our study. Patients included in the study were predominantly female sex (75%), with an average age of 62.9 ± 13.9 years and a maximum standardized uptake value in the thyroid of 7.3 ± 5.3. Frequent indications for imaging included neuroendocrine tumors of the small bowel (n = 17), lung (n = 8), and pancreas (n = 7). Thirty-three patients underwent subsequent thyroid ultrasound. Sonographic findings warranted biopsy in 24 patients, of which 3 were lost to follow-up. Cytopathology and molecular testing results are as follows: 12 Bethesda II (57.1%), 6 Bethesda III/ThyroSeq-negative (28.6%), 1 Bethesda III/ThyroSeq-positive (4.8%), 2 Bethesda V/VI (9.5%). Four nodules were resected, revealing 2 papillary thyroid cancers, 1 neoplasm with papillary-like nuclear features, and 1 follicular adenoma. There was no difference in maximum standardized uptake value between benign and malignant nodules (7.0 ± 4.6 vs 13.1 ± 5.7, P = .106). Overall, the malignancy rate among patients with sonography and appropriate follow-up was 6.7% (2/30). Among patients with cyto- or histopathology, the malignancy rate was 9.5% (2/21). There were no incidental cases of medullary thyroid cancer. ConclusionThe malignancy rate among thyroid nodules with incidental Gallium-68 DOTATATE uptake is comparable to rates reported among thyroid nodules in the general population. Guideline-directed management of thyroid nodules remains appropriate in those with incidental Gallium-68 DOTATATE uptake.