Malignant Thyroid Nodules Research Articles

Proteomics Reveals mRNA Regulation and the Action of Annexins in Thyroid Cancer.

Differentiated thyroid cancer is the most common malignancy of the endocrine system. Although most thyroid nodules are benign, given the high incidence of thyroid nodules in the population, it is important to understand the differences between benign and malignant thyroid cancer and the molecular alterations associated with malignancy to improve detection and signal potential diagnostic, prognostic, and therapeutic targets. Proteomics analysis of benign and malignant human thyroid tissue largely revealed changes indicating modifications in RNA regulation, a common cancer characteristic. In addition, changes in the immune system and cell membrane/endocytic processes were also suggested to be involved. Annexin A1 was considered a potential malignancy biomarker and, similarly to other annexins, it was found to increase in the malignant group. Furthermore, a bioinformatics approach points to the transcription factor Sp1 as being potentially involved in most of the alterations seen in the malignant thyroid nodules.

Correlation analysis of serum thyroglobulin, thyroid-stimulating hormone levels, and thyroid-cancer risk in thyroid nodule surgery.

Thyroglobulin (Tg) is one of the markers of thyroid cancer, and its concentration may be elevated in patients with malignant thyroid tumors. Thyroid-stimulating hormone (TSH) is secreted by the pituitary gland, which has a significant impact on thyroid gland function. Excessively high or low TSH levels may be associated with an increased risk of thyroid cancer. Thus, in-depth studies on the association of serum Tg and TSH levels with thyroid cancer risk in patients with thyroid nodules are warranted. This can help determine whether Tg and TSH levels can predict the degree of malignancy of thyroid nodules, which can in turn guide doctors in making accurate diagnoses and treatment decisions. Furthermore, such studies can provide more accurate diagnostic methods for thyroid nodules and help patients become aware of the presence of thyroid cancer as early as possible, thereby improving the success rate of treatment and prognosis. To investigate the association of serum Tg and TSH levels with the risk of thyroid cancer in patients undergoing thyroid nodule surgery. The clinical data and laboratory examination results of 130 patients who underwent thyroid nodule surgery were retrospectively analyzed. Furthermore, their preoperative serum Tg and TSH levels were recorded. Histopathological examination conducted during follow-up revealed the presence of thyroid cancer. Correlation analysis were used to analyze the association of Tg and TSH levels with the risk of thyroid cancer. Of the 130 patients, 60 were diagnosed with thyroid cancer. Statistical analysis revealed a significant positive correlation between serum Tg levels and the risk of thyroid cancer (P < 0.05). This suggests that high serum Tg levels are associated with an increased risk of thyroid cancer. However, no significant correlation was observed between serum TSH levels and the risk of thyroid cancer (P > 0.05). In patients who underwent thyroid nodule surgery, serum Tg levels exhibited a significant correlation with the risk of thyroid cancer but serum TSH levels did not. These findings suggest that serum Tg can serve as an important biomarker for assessing the risk of thyroid cancer in these patients.

Fully automated thyroid ultrasound screening utilizing multi-modality image and anatomical prior

There is a high prevalence of thyroid nodules in the general population. Early detection is essential for the treatment of malignant thyroid nodules. Ultrasound has the advantage of being non-invasive and radiation-free, and is sufficient for early diagnosis of malignant nodules. However, ultrasound image acquisition relies on manual manipulation of the probe by the sonographer, making it difficult to screen populations for malignant thyroid nodules. Robotic ultrasound systems (RUS) are expected to replace sonographers in ultrasound scanning and improve the standardization and reproducibility of ultrasound examinations. However, there is currently no RUS that can fully autonomously screen for thyroid nodules. In this paper, we propose a fully automated two-step search method that mimics a physician's thyroid scanning protocol. First, using the body surface structure observed by the RGBD camera, we use a bimodal detection network for the initial localization of the human neck. The bimodal detection network achieves an average accuracy of 0.986. Second, we designed a tracking strategy based on the anatomical prior of the human neck to navigate the probe for thyroid ultrasound acquisition. An network-based visual servoing method enables shadow prevention and target tracking to be performed uniformly. In vitro experiments demonstrated the effectiveness of the protocol.

A Comparative Analysis of Liquid-Based Cytology and Conventional Smears in Fine-Needle Aspirates of Thyroid Lesions.

Background Palpable nodules in the thyroid are present in 4-7% of the general population. Fine-needle aspiration cytology is a safe and cost-effective method of choice for evaluating thyroid nodules. Aspirated samples can be manually spread directly onto the slide and stained in the conventional smear method. The liquid-cased cytology method has been recently introduced, which is an automated machine-based method, yielding a single slide with a clean background and greater preservation of cells and consuming less time for screening. This study aimed to compare the cytomorphological features and diagnostic accuracy of conventional smears and liquid-based cytologysmears. Methodology This prospective study comprised 250 cases of thyroid lesions. Fine-needle aspiration cytology using conventional smears and liquid-based cytology smears was reported per the Bethesda system of reporting thyroid cytopathology. Detailed cytomorphological features were evaluated and compared in both techniques. Results The cellularity of conventional smears was significantly higher for scores 2+ and 3+ than paired liquid-based cytology smears (paired t-test, p < 0.001). The overall diagnostic efficacy of conventional smears and liquid-based cytology smears was equivalent in the majority of cases (n = 171, 68.4%). Conventional smears were better than liquid-cased cytology smears in 34 (13.6%) cases, and liquid-based cytology smears were better than conventional smears in eight (3.2%) cases. Liquid-based cytology smears showed a higher unsatisfactory rate compared to conventional Smears (15.6% vs. 5.2%). The sensitivity and specificity of conventional smears were 84.6% and 94.4%, respectively, compared to 68.7% and 92.4%, respectively, of liquid-based cytology smears. Conclusions Conventional smears are a cost-effective and easy method for diagnosing thyroid nodules. Liquid-based cytology smears can be used in association with conventional smears to enhance the accuracy of the evaluation of malignant thyroid nodules.

Clinical value of grayscale ultrasound combined with real-time shear wave elastography nomogram in risk prediction of thyroid cancer

ObjectivesThis study constructed a nomogram based on grayscale ultrasound features and real-time shear wave elastography (SWE) parameters to predict thyroid cancer.MethodsClinical data of 217 thyroid nodules of 201 patients who underwent grayscale ultrasound, real-time SWE, and thyroid function laboratory examination in Ma’anshan People’s Hospital from January 2019 to December 2020 were retrospectively analyzed. The subjects were divided into a benign nodule group (106 nodules) and a malignant nodule group (111 nodules). The differences in grayscale ultrasound features, quantitative parameters of real-time SWE, and laboratory results of thyroid function between benign and malignant thyroid nodules were analyzed. We used a chi-square test for categorical variables and a t-test for continuous variables. Then, the independent risk factors for thyroid cancer were analyzed using multivariate logistic regression. Based on the independent risk factors, a nomogram for predicting thyroid cancer risk was constructed using the RMS package of the R software.ResultsMultivariate logistic regression showed that the grayscale ultrasound features of thyroid nodules were the shape, margin, echogenicity, and echogenic foci of the nodules,the maximum Young’s modulus (SWE-max) of thyroid nodules, and the ratio of thyroid nodule and peripheral gland (SWE-ratio) measured by real-time SWE were independent risk factors for thyroid cancer (all p < 0.05), and the other variables had no statistical difference (p > 0.05). Based on the shape (OR = 5.160, 95% CI: 2.252–11.825), the margin (OR = 9.647, 95% CI: 2.048–45.443), the echogenicity (OR = 6.512, 95% CI: 1.729–24.524), the echogenic foci (OR = 2.049, 95% CI: 1.118–3.756), and the maximum Young’s modulus (SWE-max) (OR = 1.296, 95% CI: 1.140–1.473), the SWE-ratio (OR = 2.001, 95% CI: 1.403–2.854) of the thyroid nodule to peripheral gland was used to establish the related nomogram prediction model. The bootstrap self-sampling method was used to verify the model. The consistency index (C-index) was 0.979, ROC curve was used to analyze the nomogram scores of all patients, and the AUC of nomogram prediction of thyroid cancer was 0.976, indicating that the nomogram model had high accuracy in the risk prediction of thyroid cancer.ConclusionsThe nomogram model of grayscale ultrasound features combined with SWE parameters can accurately predict thyroid cancer.

Thyroid Cancer Polygenic Risk Score Improves Classification of Thyroid Nodules as Benign or Malignant.

Thyroid nodule ultrasound-based risk stratification schemas rely on the presence of high-risk sonographic features. However, some malignant thyroid nodules have benign appearance on thyroid ultrasound. New methods for thyroid nodule risk assessment are needed. We investigated polygenic risk score (PRS) accounting for inherited thyroid cancer risk combined with ultrasound-based analysis for improved thyroid nodule risk assessment. The convolutional neural network classifier was trained on thyroid ultrasound still images and cine clips from 621 thyroid nodules. Phenome-wide association study (PheWAS) and PRS PheWAS were used to optimize PRS for distinguishing benign and malignant nodules. PRS was evaluated in 73 346 participants in the Colorado Center for Personalized Medicine Biobank. When the deep learning model output was combined with thyroid cancer PRS and genetic ancestry estimates, the area under the receiver operating characteristic curve (AUROC) of the benign vs malignant thyroid nodule classifier increased from 0.83 to 0.89 (DeLong, P value = .007). The combined deep learning and genetic classifier achieved a clinically relevant sensitivity of 0.95, 95% CI [0.88-0.99], specificity of 0.63 [0.55-0.70], and positive and negative predictive values of 0.47 [0.41-0.58] and 0.97 [0.92-0.99], respectively. AUROC improvement was consistent in European ancestry-stratified analysis (0.83 and 0.87 for deep learning and deep learning combined with PRS classifiers, respectively). Elevated PRS was associated with a greater risk of thyroid cancer structural disease recurrence (ordinal logistic regression, P value = .002). Augmenting ultrasound-based risk assessment with PRS improves diagnostic accuracy.

Do large thyroid nodules (≥4 cm) without suspicious cytology need surgery?

Fine-needle aspiration biopsy (FNAB) is a good diagnostic tool for thyroid nodules; however, its high false-negative rate for giant nodules remains controversial. Many clinicians recommend surgical resection for nodules >4 cm owing to an increased risk of malignancy and an increased false-negative rate. This study aimed to examine the feasibility of this approach and investigate the incidence of malignancy in thyroid nodules >4 cm without suspicious cytology based on medical records in our center. This was a retrospective analysis of 453 patients that underwent preoperative FNAB for nodules measuring >4 cm between January 2017 and August 2022 at Severance Hospital, Seoul. Among the 453 patients, 140 nodules were benign and 119 were indeterminate. Among 259 patients, the final pathology results were divided into benign (149) and cancerous (110) groups, and the prevalence of malignancy was 38.9% in the benign group and 55.5% in the indeterminate group. Among the malignancies, follicular carcinoma and follicular variants of papillary carcinoma were observed in 83% of the cytologically benign group and 62.8% of the indeterminate group. Preoperative FNAB had high false-negative rates and low diagnostic accuracy in patients with thyroid nodules >4 cm without suspicious cytologic features; therefore, diagnostic surgery may be considered a treatment option.

ADEQUACY OF FINE NEEDLE ASPIRATION CYTOLOGY FOR CONFIRMATION OF TYPE OF THYROID LESIONS

Thyroid nodules commonly arise within an otherwise normal thyroid gland. Nodular thyroid lesions are so common that they are present as solitary or multiple nodules. Fine-needle aspiration and capillary sampling are the 2 most commonly used diagnostic techniques for assessing cytopathology of thyroid nodules. Objectives: To determine the frequency of adequacy of fine needle aspiration cytology for detecting thyroid lesions in patients with thyroid lesions. Materials and Methods: 75 patients with thyroid lesions were enrolled after meeting the inclusion criteria. Informed consent and demographic information were taken. The patient's TSH level was noted. Then, FNAC was done after collecting the specimen through FNAC, and the specimen was sent to the hospital laboratory for cytology. The diagnosis was made. All the collected information was noted and entered in SPSS version 20 for analysis. Results: In our study, the mean age of the patients was 43.20±9.35 years, male to female ratio of the patients was 0.7:1. Out of 75 patients on FNAC, 50(66.7%) samples were adequate. In this study, a malignant thyroid nodule on FNAC was noted in 21(28%) patients, and a benign thyroid nodule on FNAC was noted in 54(72%) patients. Conclusion: The adequacy of fine needle aspiration cytology for detecting thyroid lesions in patients with thyroid lesions was 66.7%.

Comparison of diagnostic performance of two ultrasound risk stratification systems for thyroid nodules: a systematic review and meta-analysis.

To assume the ideal cut-off values and diagnostic performance of two thyroid imaging reporting and data systems published by the Korean Thyroid Association/Korean Society of Thyroid Radiology (Korean TI-RADS) and the American Thyroid Association (ATA TI-RADS). Eighteen studies with 25,422 patients from PubMed, SCOPUS, Embase, Web of Science, and Cochrane Library databases up to August 2022. True and false positive and negative values with characteristics were extracted. The highest area under the receiver operating characteristic curve (AUC) was 0.893 and 0.887 for Korean and ATA TI-RADS. High suspicion was judged as the best cut-off value with the highest AUC based on optimal sensitivity and specificity. In determining the risk of malignant thyroid nodules, high suspicion in Korean and ATA TI-RADS showed sensitivity as 71.3% and 73.5%, specificity as 7.9% and 86.4%, diagnostic odds ratios as 20.0289 and 20.9076, AUC as 0.893 and 0.887. There was no significant difference when directly comparing the diagnostic accuracy of both TI-RADS. The two risk stratification systems had good diagnostic performance with high AUC and no significant differences. The ideal cut-off can depend on the medical condition or thyroid nodules, because the changes of cut-off point may reciprocally alter sensitivity and specificity.

Gene Expression Alterations, Assist Players of Driver Mutations Toward Malignancy in Thyroid Nodules?

Gene Expression Alterations, Assist Players of Driver Mutations Toward Malignancy in Thyroid Nodules?

Aided diagnosis of thyroid nodules based on an all-optical diffraction neural network.

Thyroid cancer is the most common malignancy in the endocrine system, with its early manifestation being the presence of thyroid nodules. With the advantages of convenience, noninvasiveness, and a lack of radiation, ultrasound is currently the first-line screening tool for the clinical diagnosis of thyroid nodules. The use of artificial intelligence to assist diagnosis is an emerging technology. This paper proposes the use optical neural networks for potential application in the auxiliary diagnosis of thyroid nodules. Ultrasound images obtained from January 2013 to December 2018 at the Institute and Hospital of Oncology, Tianjin Medical University, were included in a dataset. Patients who consecutively underwent thyroid ultrasound diagnosis and follow-up procedures were included. We developed an all-optical diffraction neural network to assist in the diagnosis of thyroid nodules. The network is composed of 5 diffraction layers and 1 detection plane. The input image is placed 10 mm away from the first diffraction layer. The input of the diffractive neural network is light at a wavelength of 632.8 nm, and the output of this network is determined by the amplitude and light intensity obtained from the detection region. The all-optical neural network was used to assist in the diagnosis of thyroid nodules. In the classification task of benign and malignant thyroid nodules, the accuracy of classification on the test set was 97.79%, with an area under the curve value of 99.8%. In the task of detecting thyroid nodules, we first trained the model to determine whether any nodules were present and achieved an accuracy of 84.92% on the test set. Our study demonstrates the potential of all-optical neural networks in the field of medical image processing. The performance of the models based on optical neural networks is comparable to other widely used network models in the field of image classification.

Correlation of the ultrasound thyroid imaging reporting and data system with cytology findings among patients in Uganda

BackgroundUltrasonography is a noninvasive modality for the initial assessment of thyroid nodules. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) has demonstrated good performance in differentiating malignant thyroid nodules. However, the combination of ACR TI-RADS categories and cytology has not been studied extensively, in Uganda. The study aims to correlate ACR TI-RADS with cytology among patients referred for US-guided fine-needle aspiration at Mulago National Referral Hospital.MethodsThis was a hospital-based cross-sectional study that recruited 132 patients with thyroid nodules. Spearman’s correlation was used to establish a relationship between TI-RADS and cytology findings. The diagnostic accuracy of TI-RADS was assessed using sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios.ResultsOf 132 study participants, 90% (n = 117) were females, and the mean age was 41 ± 13 years. One hundred sixty-one thyroid nodules were analyzed. More than half of the thyroid nodules (54.7%, n = 87) were solid or almost solid, 96.9% (n = 154) were shaped wider than tall, 57.2% (n = 91) had smooth margins, 83.7% (n = 133) were hyperechoic or isoechoic, and 88.7% (n = 141) had no echogenic foci. TI-RADS 3 was the most common at 42.9% (n = 69). The proportions of malignancy for TI-RADS 4 and TI-RADS 5 were 73.3% and 85.7%, respectively. The correlation between ACR TI-RADS and the Bethesda system of thyroid classification scores was r = 0.577. The sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios of ACR TI-RADS were 90.9%, 98.5%, 90%, 99.3%, 62.3, and 0.1, respectively.ConclusionWe found that ACR TI-RADS classification is an appropriate and noninvasive method for assessing thyroid nodules in routine practice. It can safely reduce the number of unnecessary fine-needle aspiration in a significant proportion of benign thyroid lesions. Thyroid nodules classified as TI-RADS 3 should be followed routinely. ACR TI-RADS should be standardized as the screening tool in resource-limited areas.

The diagnostic value of ultrasound on different-sized thyroid nodules based on ACR TI-RADS.

The thyroid nodule is one of the most common endocrine system diseases. Risk classification models based on ultrasonic features have been created by multiple professional societies, including the American College of Radiology (ACR), which published the Thyroid Imaging Reporting and Data System (TI-RADS) in 2017. The effect of the size in the diagnostic value of ultrasound remains not well defined. The purposes of our study aims to explore diagnostic value of the ACR TI-RADS on different-sized thyroid nodules. A total of 1183 thyroid nodules were selected from 952 patients with thyroid nodules confirmed by surgical pathology from January 2021 to October 2022. Based on the maximum diameters of the nodules, they were stratified into groups A ( ≤ 10 mm), B ( > 10 mm, < 20 mm) and C ( ≥ 20 mm). The ultrasonic features of the thyroid nodules in each group were evaluated and scored based on ACR TI-RADS, and the receiver operating characteristic curve (ROC) was plotted to determine the optimal cut-off value for the ACR TI-RADS scores and categories in each group. Finally, the diagnostic efficacy of ACR TI-RADS on different-sized thyroid nodules was analyzed. Among the 1183 thyroid nodules, 340 were benign, 10 were low-risk and 833 were malignant. For the convenience of statistical analysis, low-risk thyroid nodules were classified as malignant in this study. The ACR TI-RADS scores and categorical levels of malignant thyroid nodules in each group were higher than those of benign ones (p < 0.05). The areas under the ROCs (AUCs) plotted based on scores were 0.741, 0.907, and 0.904 respectively in the three groups, and the corresponding optimal cut-off values were > 6 points, > 5 points and > 4 points respectively. While the AUCs of the ACR TI-RADS categories were 0.668, 0.855, and 0.887 respectively in each group, with the optimal cut-off values were all > TR4. Besides, for thyroid nodules of larger sizes, ACR TI-RADS exhibited weaker sensitivity with lower positive prediction value (PPV), but the specificity and negative prediction value (NPV) were both higher, presenting with statistically significant differences (p < 0.05). For thyroid nodules of different sizes, the diagnostic efficacy of ACR TI-RADS varies as well. The system shows better diagnostic efficacy on thyroid nodules of > 10 mm than on those ≤ 10 mm. Considering the favorable prognosis of thyroid microcarcinoma and the low diagnostic efficacy of ACR TI-RADS on it, the scoring and classification of thyroid micro-nodules can be left out in appropriate cases, so as to avoid the over-diagnosis and over-treatment of thyroid microcarcinoma to a certain extent.

Comparative analysis of accuracy between fine-needle aspiration biopsy and postoperative histopathology for detecting large thyroid nodules: A retrospective observational study.

To avoid unnecessary surgeries, ultrasound-guided fine-needle aspiration biopsy (FNAB) is an effective and reliable procedure for the preoperative evaluation of thyroid nodules. However, there have been only a limited number of studies exploring the ability of preoperative FNAB to distinguish malignancy compared to postoperative histopathology in thyroid nodules larger than 4 cm. The aim of this study was to investigate the diagnostic accuracy of FNAB compared to postoperative histopathology in distinguishing malignancy in thyroid nodules larger than 4 cm. A single-center retrospective observational study was conducted at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia, between January 2014 and December 2018. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were calculated. A total of 83 patients were included in the study. The results showed that preoperative FNAB may have the ability to distinguish malignancy compared to postoperative histopathology. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 42.85%, 98.38%, 90.00%, 83.56%, and 84.33%, respectively. These data suggested that ultrasound-guided preoperative FNAB is a reliable diagnostic tool in the preoperative evaluation of thyroid nodules larger than 4 cm, but it has limited capability in distinguishing malignancies. In conclusion, although FNAB may be useful in reducing unnecessary surgeries, histopathology remains the preferred method for confirming malignancy in thyroid nodules.

Exploring the Diagnostic Role of Spectral Doppler as a Predictor of Malignancy Within Thyroid Nodules

Objective: In recent years, spectral Doppler has become a valuable diagnostic tool for assessing suspected thyroid nodules. The aim of this study was to assess the clinical use of spectral Doppler in managing thyroid nodules. Materials and Methods: This prospective cohort study included 153 patients who underwent thyroid nodule evaluation, using sonography. Peak systolic velocity, end-diastolic velocity (EDV), resistive index (RI), and pulsatility index (PI) were measured, within the suspected lesions. Results: Among the 209 identified thyroid nodules, 193 (92%) were benign and 16 (7.6%) were malignant. The RI and PI values of malignant nodules were significantly higher than measured within benign nodules. There was also a significant difference in the EDV between benign and malignant nodules ( P = .012). The RI had the highest diagnostic accuracy, with an area under the receiver operating characteristic, area under the curve, of 0.839 (95% confidence interval, 0.748–0.930). The optimal cutoff value for RI was 0.73. The results indicate that the RI with an odds ratio (OR) of 2.64, in the univariate analysis, and an OR of 2.31, in the multivariate analysis, might be successful in predicting a malignant thyroid nodule. Conclusion: This cohort study demonstrated a high sensitivity and specificity for RI and PI as predictors of malignancy in thyroid nodules, with diagnostic cutoff points of 0.73 and 1.37, respectively.

Predictive value of us doppler and microvascular imaging in differentiating benign and malignant thyroid nodules

Predictive value of us doppler and microvascular imaging in differentiating benign and malignant thyroid nodules

Role of Dynamic Magnetic Resonance Imaging in Differentiating Benign from Malignant Thyroid Nodule

Abstract Objective To determine the diagnostic accuracy of Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) in differentiating benign from malignant thyroid nodules Study Design A cross-sectional analytical study. Methodology Thirty-four patients who determined to have suspicious thyroid nodules by ultrasonography who attend endocrinology outpatient clinic at Ain-Shams university hospital, fulfilling the inclusion and exclusion criteria, were included in the study. They were evaluated on 1.5 Tesla MRI machine with T1- and T2- weighted imaging. DWI was done using b-values of 0 and 1000 s/mm2 and ADC values were calculated for the thyroid nodules. All of these patients were subjected to full history taking, general and local clinical examination, thyroid profile, TPO Abs, ultrasound, fine needle biopsy and histopathology results were correlated with ADC values. Results The benign nodules showed facilitated diffusion while malignant nodules showed restricted diffusion. T-test was used to assess the difference in mean ADC values between benign and malignant nodules. The mean ADC value of the malignant thyroid nodules (0.8±0.1 x 10-3 mm2/s) was significantly lower than that of the benign thyroid nodules (1.3±0.2 x 10-3 mm2/s) (p-value &amp;lt; 0.001). ADC value of 1.15 x 10-3 mm2/s was used as a cut-off, for differentiating benign from malignant thyroid nodules. The sensitivity, specificity, PPV, NPV and diagnostic accuracy of DWI and ADC values in differentiating benign from malignant thyroid nodules were 88.2%, 92.3%, 80%, 100% and, respectively. Conclusion FNAC is gold standard diagnostic modality but has limitations. However, DWI and (ADC) is a non-invasive diagnostic tool for characterization and differentiation between benign and malignant thyroid nodules. It decreases the burden of unnecessary surgeries when pre-operative FNAC and biopsy are inconclusive

Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System.

The current ultrasound scan classification system for thyroid nodules is time-consuming, labor-intensive, and subjective. Artificial intelligence (AI) has been shown to increase the accuracy of predicting the malignancy rate of thyroid nodules. This study aims to demonstrate the state-of-the-art Swin Transformer to classify thyroid nodules. Ultrasound images were collected prospectively from patients who received fine needle aspiration biopsy for thyroid nodules from January 2016 to June 2021. One hundred thirty-nine patients with malignant thyroid nodules were enrolled, while 235 patients with benign nodules served as controls. Images were fed to Swin-T and ResNeSt50 models to classify the thyroid nodules. Patients with malignant nodules were younger and more likely male compared to those with benign nodules. The average sensitivity and specificity of Swin-T were 82.46% and 84.29%, respectively. The average sensitivity and specificity of ResNeSt50 were 72.51% and 77.14%, respectively. Receiver operating characteristics analysis revealed that the area under the curve of Swin-T was higher (AUC=0.91) than that of ResNeSt50 (AUC=0.82). The McNemar test evaluating the performance of these models showed that Swin-T had significantly better performance than ResNeSt50.Swin-T classifier can be a useful tool in helping shared decision-making between physicians and patients with thyroid nodules, particularly in those with high-risk characteristics of sonographic patterns.

An adaptive multi-modal hybrid model for classifying thyroid nodules by combining ultrasound and infrared thermal images

BackgroundTwo types of non-invasive, radiation-free, and inexpensive imaging technologies that are widely employed in medical applications are ultrasound (US) and infrared thermography (IRT). The ultrasound image obtained by ultrasound imaging primarily expresses the size, shape, contour boundary, echo, and other morphological information of the lesion, while the infrared thermal image obtained by infrared thermography imaging primarily describes its thermodynamic function information. Although distinguishing between benign and malignant thyroid nodules requires both morphological and functional information, present deep learning models are only based on US images, making it possible that some malignant nodules with insignificant morphological changes but significant functional changes will go undetected.ResultsGiven the US and IRT images present thyroid nodules through distinct modalities, we proposed an Adaptive multi-modal Hybrid (AmmH) classification model that can leverage the amalgamation of these two image types to achieve superior classification performance. The AmmH approach involves the construction of a hybrid single-modal encoder module for each modal data, which facilitates the extraction of both local and global features by integrating a CNN module and a Transformer module. The extracted features from the two modalities are then weighted adaptively using an adaptive modality-weight generation network and fused using an adaptive cross-modal encoder module. The fused features are subsequently utilized for the classification of thyroid nodules through the use of MLP. On the collected dataset, our AmmH model respectively achieved 97.17% and 97.38% of F1 and F2 scores, which significantly outperformed the single-modal models. The results of four ablation experiments further show the superiority of our proposed method.ConclusionsThe proposed multi-modal model extracts features from various modal images, thereby enhancing the comprehensiveness of thyroid nodules descriptions. The adaptive modality-weight generation network enables adaptive attention to different modalities, facilitating the fusion of features using adaptive weights through the adaptive cross-modal encoder. Consequently, the model has demonstrated promising classification performance, indicating its potential as a non-invasive, radiation-free, and cost-effective screening tool for distinguishing between benign and malignant thyroid nodules. The source code is available at https://github.com/wuliZN2020/AmmH.

Can Routine Laboratory Tests Be Suggestive in Determining Suspicions of Malignancy in the Case of Thyroid Nodules?

Background and objectives: Thyroid nodules are a common finding in clinical practice and can be either benign or malignant. The aim of this study was to compare laboratory parameters between patients with malignant thyroid nodules and those with benign thyroid nodules. Materials and methods: A total of 845 patients were included, with 251 in the study group (malignant thyroid nodules) and 594 in the control group (benign thyroid nodules). Results: Our results show that there were statistically significant differences in several laboratory parameters, including FT3, FT4, ESR, fibrinogen, WBC, and lymphocyte percentage, between the two patient groups (p < 0.05). Conclusions: These findings suggest that certain laboratory parameters may be useful in differentiating between benign and malignant thyroid nodules and could aid in the diagnosis and treatment of thyroid cancer. However, further diagnostic tests such as fine-needle aspiration biopsy and imaging studies are typically required for an accurate diagnosis. Routine laboratory tests prove most effective when combined with other diagnostic methods to identify thyroid cancer. Although not conclusive on their own, these tests significantly suggest and guide physicians to suspect malignancy in thyroid nodules. This affirmative answer to our question, "Can routine laboratory tests be suggestive in determining suspicions of malignancy in the case of thyroid nodules?" aligns with the results of our study.