Classification Of Thyroid Nodules Research Articles

A segmentation-based algorithm for classification of benign and malignancy Thyroid nodules with multi-feature information

A segmentation-based algorithm for classification of benign and malignancy Thyroid nodules with multi-feature information

Assessment of Thyroid Gland in Children with Point-of-Care Ultrasound (POCUS): Radiological Performance and Feasibility of Handheld Ultrasound in Clinical Practice.

Point-of-Care Ultrasound (POCUS) refers to the use of portable ultrasound machines to perform quick and focused ultrasound examinations at a patient's bedside or point-of-care. POCUS can be performed by all health workers with specific training to use POCUS. This study aimed to investigate the radiological performance and feasibility of POCUS using a handheld ultrasound (HHUSD) system in children from the perspective of the thyroid gland perspective. A pediatric endocrinologist performed thyroid imaging in children referred to our hospital with suspected thyroid disease using an HHUSD system. The same children underwent US imaging using the same device by the first radiologist, and a second radiologist performed thyroid US using an advanced high-range Ultrasound Device (AHUSD) (defined as the gold-standard method) within two hours. The data obtained by the three researchers were compared with each other. This study included 105 patients (68.6% girls [n=72)]; mean age 12.8±3.6 years]. When the thyroid volume was evaluated, a strong correlation was found between the measurements of the three researchers (AA vs. MG: r=0.963, AA vs. GT: r=0.969, MG vs. GT: r=0.963, p<0.001). According to the Bland-Altman analysis, for total thyroid volume, AA measured 0.43 cc (%95 CI: -0.89-0.03) smaller than MG, and 0.11 cc (%95 CI: -0.30-0.52) larger than GT, whereas MG measured 0.52 cc (%95 CI: 0.09-0.94) larger than GT. When evaluated for the presence of goiter and nodules, a near-perfect agreement was found between the results of the three researchers (AA vs GT; κ=0.863, MG vs GT; κ=0.887, p<0.001, and AA vs GT; κ=1.000, MG vs GT; κ=0.972, p<0.001, respectively). When evaluated in terms of the longest axis of nodules, a high correlation was found between the measurements of the three researchers (AA vs MG; r=0.993, AA vs GT; r=0.996, MG vs GT; r=0.996, p<0.001). When evaluated in terms of the final diagnosis, the evaluations of the three researchers showed excellent agreement with each other (AA vs GT; κ=0.893, MG vs GT; κ=0.863, p<0.001, Accuracy rate AA vs GT: 93.3%; MG vs GT: 91.4%). A pediatric endocrinologist, equipped with sufficient training in thyroid US evaluation, incorporates the HHUSD as a routine tool for clinical examinations in outpatient settings, they can effectively assess normal thyroid tissue in pediatric patients. Moreover, the HHUSD system proves to be useful in detecting thyroid pathologies. However, it is crucial to note that for a more comprehensive evaluation of thyroid nodules, including detailed assessment and Thyroid Imaging Reporting and Data System "TIRADS" classification, patients should be referred to radiology departments equipped with AHUSD systems. These specialized devices, along with the expertise of radiologists, are essential for in-depth evaluations and accurate classification of thyroid nodules.

Clinical Evaluation of an Artificial Intelligence-Based Decision Support System for the Diagnosis and American College of Radiology Thyroid Imaging Reporting and Data System Classification of Thyroid Nodules.

Background: This study aimed to evaluate the clinical impact of an artificial intelligence (AI)-based decision support system (DSS), Koios DS, on the analysis of ultrasound imaging and suspicious characteristics for thyroid nodule risk stratification. Methods: A retrospective ultrasound study was conducted on all thyroid nodules with histological findings from June 2021 to December 2022 in a thyroid nodule clinic. The diagnostic performance of ultrasound imaging was evaluated by six readers on the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) before and after the use of the AI-based DSS and by AI itself. Results: A total of 172 patients (83.1% women) with a mean age of 52.3 ± 15.3 years were evaluated. The mean maximum nodular diameter was 2.9 ± 1.2 cm, with 11.0% being differentiated thyroid carcinomas. Among the nodules initially classified as ACR TI-RADS 3 and 4, AI reclassified 81.4% and 24.5% into lower risk categories, respectively. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the readers and the AI-based DSS versus histological diagnosis. There was an increase in the area under the ROC curve (AUROC) after the use of AI (0.776 vs. 0.817, p < 0.001). The AI-based DSS improved the mean sensitivity (Sens) (82.3% vs. 86.5%) and specificity (Spe) (38.3% vs. 54.8%), produced a high negative predictive value (94.5% vs. 96.4%), and increased the positive predictive value (PPV) (14.0% vs. 16.1%) and diagnostic precision (43.0% vs. 49.3%). Based on the ACR TI-RADS score, there was significant improvement in interobserver agreement after the use of AI (r = 0.741 for ultrasound imaging alone vs. 0.981 for ultrasound imaging and the AI-based DSS, p < 0.001). Conclusions: The use of an AI-based DSS was associated with overall improvement in the diagnostic efficacy of ultrasound imaging, based on the AUROC, as well as an increase in Sens, Spe, negative and PPVs, and diagnostic accuracy. There was also a reduction in interobserver variability and an increase in the degree of concordance with the use of AI. AI reclassified more than half of the nodules with intermediate ACR TI-RADS scores into lower risk categories.

Residual U-Net approach for thyroid nodule detection and classification from thyroid ultrasound images

With so many thyroid knobs (nodules) discovered by accident, it is critical to recognize as many aberrant knobs (nodules) as possible from fine-needle aspiration (FNA) biopsies or other medical procedures while excluding those that are virtually certainly benign. Thyroid ultrasonography, on the other hand, is prone to interobserver variability and subjective translations. An effective deep learning model for segmenting and categorizing thyroid nodules in this study follows the stages below: data collection from a well-known archive, The Thyroid Digital Image Database (TDID), which comprises ultrasound pictures from 298 patients, preprocessing using anisotropic diffusion filter (ADF) for removing noise and enhancing the images, segmentation using a bilateral filter for segmenting images, feature extraction using grey level occurrence matrix (GLCM), feature selection using Multi-objective Particle Swarm with Random Forest Optimization (MbPSRA) and finally classification happens were Residual U-Net will be used. Experiment evaluation states the proposed model outperforms well than other state-of-art models.

An Improved k-Nearest Neighbor Algorithm for Recognition and Classification of Thyroid Nodules.

To complete the task of automatic recognition and classification of thyroid nodules and solve the problem of high classification error rates when the samples are imbalanced. An improved k-nearest neighbor (KNN) algorithm is proposed and a method for automatic thyroid nodule classification based on the improved KNN algorithm is established. In the improved KNN algorithm, we consider not only the number of class labels for various classes of data in KNNs, but also the corresponding weights. And we use the Minkowski distance measure instead of the Euclidean distance measure. A total of 508 ultrasound images of thyroid nodules, including 415 benign nodules and 93 malignant nodules, were used in the paper. Experimental results show the improved KNN has 0.872549 accuracy, 0.867347 precision, 1 recall, and 0.928962 F1-score. At the same time, we also considered the influence of different distance weights, the value of k, different distance measures on the classification results. A comparison result shows that our method has a better performance than the traditional KNN and other classical machine learning methods.

Deep-GAN: an improved model for thyroid nodule identification and classification

Deep-GAN: an improved model for thyroid nodule identification and classification

Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training

Thyroid nodule classification and segmentation in ultrasound images are crucial for computer-aided diagnosis; however, they face limitations owing to insufficient labeled data. In this study, we proposed a multi-view contrastive self-supervised method to improve thyroid nodule classification and segmentation performance with limited manual labels. Our method aligns the transverse and longitudinal views of the same nodule, thereby enabling the model to focus more on the nodule area. We designed an adaptive loss function that eliminates the limitations of the paired data. Additionally, we adopted a two-stage pre-training to exploit the pre-training on ImageNet and thyroid ultrasound images. Extensive experiments were conducted on a large-scale dataset collected from multiple centers. The results showed that the proposed method significantly improves nodule classification and segmentation performance with limited manual labels and outperforms state-of-the-art self-supervised methods. The two-stage pre-training also significantly exceeded ImageNet pre-training.

SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images

With the advancement of deep learning technology, computer-aided diagnosis (CAD) is playing an increasing role in the field of medical diagnosis. In particular, the emergence of Transformer-based models has led to a wider application of computer vision technology in the field of medical image processing. In the diagnosis of thyroid diseases, the diagnosis of benign and malignant thyroid nodules based on the TI-RADS classification is greatly influenced by the subjective judgment of ultrasonographers, and at the same time, it also brings an extremely heavy workload to ultrasonographers. To address this, we propose Swin-Residual Transformer (SRT) in this paper, which incorporates residual blocks and triplet loss into Swin Transformer (SwinT). It improves the sensitivity to global and localized features of thyroid nodules and better distinguishes small feature differences. In our exploratory experiments, SRT model achieves an accuracy of 0.8832 with an AUC of 0.8660, outperforming state-of-the-art convolutional neural network (CNN) and Transformer models. Also, ablation experiments have demonstrated the improved performance in the thyroid nodule classification task after introducing residual blocks and triple loss. These results validate the potential of the proposed SRT model to improve the diagnosis of thyroid nodules' ultrasound images. It also provides a feasible guarantee to avoid excessive puncture sampling of thyroid nodules in future clinical diagnosis.

ThyroidNet: A Deep Learning Network for Localization and Classification of Thyroid Nodules.

This study aims to establish an artificial intelligence model, ThyroidNet, to diagnose thyroid nodules using deep learning techniques accurately. A novel method, ThyroidNet, is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules. First, we propose the multitask TransUnet, which combines the TransUnet encoder and decoder with multitask learning. Second, we propose the DualLoss function, tailored to the thyroid nodule localization and classification tasks. It balances the learning of the localization and classification tasks to help improve the model's generalization ability. Third, we introduce strategies for augmenting the data. Finally, we submit a novel deep learning model, ThyroidNet, to accurately detect thyroid nodules. ThyroidNet was evaluated on private datasets and was comparable to other existing methods, including U-Net and TransUnet. Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules. It achieved improved accuracy of 3.9% and 1.5%, respectively. ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks. Future research directions include optimization of the model structure, expansion of the dataset size, reduction of computational complexity and memory requirements, and exploration of additional applications of ThyroidNet in medical image analysis.

Pathological and Clinical Correlation European Union-Thyroid Imaging Reporting and Data System (EU-TIRADS) Classification of Thyroid Nodules in Two University Hospitals in Cotonou

Pathological and Clinical Correlation European Union-Thyroid Imaging Reporting and Data System (EU-TIRADS) Classification of Thyroid Nodules in Two University Hospitals in Cotonou

Classifying thyroid nodules with the American College of Radiology Thyroid Imaging Reporting and Data System: A simple and cost-effective approach for assessing malignancy risk

ABSTRACT Background: Thyroid nodules are common, with up to 68% of adults having them. Ultrasonography is commonly used to examine them, but cannot determine if they are benign or malignant. High-res ultrasonography can detect disease before diagnosis but confirmation requires an invasive procedure, fine-needle aspiration cytology (FNAC), which has risks. Thyroid Imaging Reporting and Data System (TI-RADS) classifies nodules based on ultrasound findings, providing objectivity and guidance. Aim: The aim of this study is to evaluate the ultrasound characteristics of thyroid nodules &amp; categorize them using the ACR-TIRADS category based on suspicious ultrasound findings, assess the malignancy risk and co-relate with the pathological(FNAC) findings. Materials and Methods: A study involving 50 participants was conducted in the department of radiodiagnosis at our institute to assess thyroid nodules using ultrasound. The patients underwent ultrasound examination and FNAC, and the results were analysed using Microsoft Excel 2016. Results: Out of the 50 cases, 88% had single nodules and 72% were classified as TI-RADS 3, 20% as TI-RADS 4 and 8% as TI-RADS 5. The FNAC results revealed benign and malignant nodules. All patients with TI-RADS 3 criteria were benign, 80% of those with TI-RADS 4 criteria were benign and all patients with TI-RADS 5 criteria were malignant. Conclusion: The study concludes that the American College of Radiology (ACR) TI-RADS scoring system is an effective and practical way to categorise thyroid nodules using ultrasound. It provides a level of certainty about the likelihood of malignancy and helps healthcare providers make informed decisions about which nodules require biopsy, monitoring or no further action based on their ultrasound characteristics. This system reduces the need for unnecessary biopsies and surgeries, making it a cost-effective solution.

QuCNet: Quantum-Inspired Convolutional Neural Networks for Optimized Thyroid Nodule Classification

QuCNet: Quantum-Inspired Convolutional Neural Networks for Optimized Thyroid Nodule Classification

Surgical Management of Goiter with Intrathoracic Extension at the Philippine General Hospital Department of Otolaryngology Head and Neck Surgery

Objective: To describe the demographic and clinical characteristics of patients diagnosed with intrathoracic thyroid masses managed surgically in our institution, determine associated factors affecting eventual operative approaches for these patients, and assess postoperative outcomes and complications associated with surgical intervention. Methods: Design: Retrospective descriptive case series Setting:Tertiary National University Hospital Participants: 24 patients Results: The mean age of patients diagnosed with intrathoracic goiters was 55.71 years old, with a 1:1.4 male to female ratio; with most having an intrathoracic extent of Huins Grade 1 (67%) compared to others having Huins Grade 2-3. Majority of patients pre-operatively had a Fine Needle Aspiration (FNA) Bethesda Thyroid Nodule Classification of Category II (benign); 79% of total patients underwent excision of thyroid mass utilizing a transcervical approach alone. As Intrathoracic Extension (ITE) grade increased, additional transthoracic approaches were performed; duration of operation, average estimated blood loss, length of hospital stay was also noted to increase. Majority of post operative surgical histopathology results revealed malignant thyroid masses, in contrast to pre-operative FNA. Post-operative transient hypocalcemia was the most reported immediate complication. Conclusions: Management of intrathoracic goiter is often multidisciplinary. Referral to the thoracic vascular service is warranted for access to the thoracic inlet. Classification by grade of intrathoracic goiters is helpful to determine the most appropriate operative approach and maybe predictive of intraoperative and postoperative outcomes. Postoperative histopathology across all ITE grades mostly yield malignant results; hence, preoperative FNA results should be used with caution.

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

Background and objectivesEarly detection and diagnosis of thyroid nodule types are important because they can be treated more effectively in their early stages. The types of thyroid nodules are generally stated as atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), benign follicular, and papillary follicular. The risk of malignancy for AUS/FLUS is typically stated to be between 5 and 15 %, while some studies indicate a risk as high as 25 %. Without complete histology, it is difficult to classify nodules and these diagnostic operations are pricey and risky. To minimize laborious workload and misdiagnosis, recently various AI-based decision support systems have been developed. MethodsIn this study, a novel AI-based decision support system has been developed for the automated segmentation and classification of the types of thyroid nodules. This system is based on a hybrid deep-learning procedure that makes both an automatic thyroid nodule segmentation and classification tasks, respectively. In this framework, the segmentation is executed with some U-Net architectures such as ResUNet and ResUNet++ integrating with the feature extraction and upsampling with dropout operations to prevent overfitting. The nodule classification task is achieved by various deep nets architecture such as VGG-16, DenseNet121, ResNet-50, and Inception ResNet-v2 considering some accurate classification criteria such as Intersection over Union (IOU), Dice coefficient, accuracy, precision, and recall. ResultsIn analysis, a total of 880 patients with ages ranging from 10 to 90 years were included by taking the ultrasound images and demographics. The experimental evaluations showed that ResUNet++ demonstrated excellent segmentation outcomes, attaining remarkable evaluation scores including a dice coefficient of 92.4 % and a mean IOU of 89.7 %. ResNet-50 and Inception ResNet-v2 trained over the images segmented with UNets have shown better performance in terms of achieving high evaluation scores for the classification accuracy such as 96.6 % and 95.0 %, respectively. In addition, ResNet-50 and Inception ResNet-v2 classified AUS/FLUS from the images segmented with UNets with AUC=97.0 % and 96.0 %, respectively. ConclusionsThe proposed AI-based decision support system improves the automatic segmentation performance of AUS/FLUS and it has shown better performance than available approaches in the literature with respect to ACC, Jaccard and DICE losses. This system has great potential for clinical use by both radiologists and surgeons as well.

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.

Thyroid Nodule Image Joint Segmentation and Classification Based on Deep Learning

Thyroid Nodule Image Joint Segmentation and Classification Based on Deep Learning

A multi-task model for reliable classification of thyroid nodules in ultrasound images.

Thyroid nodules are common, and patients with potential malignant lesions are usually diagnosed using ultrasound imaging to determine further treatment options. This study aims to propose a computer-aided diagnosis method for benign and malignant classification of thyroid nodules in ultrasound images. We propose a novel multi-task framework that combines the advantages of dense connectivity, Squeeze-and-Excitation (SE) connectivity, and Atrous Spatial Pyramid Pooling (ASPP) layer to enhance feature extraction. The Dense connectivity is used to optimize feature reuse, the SE connectivity to optimize feature weights, the ASPP layer to fuse feature information, and a multi-task learning framework to adjust the attention of the network. We evaluate our model using a 10-fold cross-validation approach based on our established Thyroid dataset. We assess the performance of our method using six average metrics: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC, which are 93.49, 95.54, 91.52, 91.63, 95.47, and 96.84%, respectively. Our proposed method outperforms other classification networks in all metrics, achieving optimal performance. We propose a multi-task model, DSMA-Net, for distinguishing thyroid nodules in ultrasound images. This method can further enhance the diagnostic ability of doctors for suspected cancer patients and holds promise for clinical applications.

Diagnostic performance of a new two-dimensional shear wave elastography expression using siemens ultrasound system combined with ACR TI-RADS for classification of benign and malignant thyroid nodules: A prospective multi-center study

ObjectiveThe present study aimed to evaluate the efficacy of a new two-dimensional shear wave elastography (2D-SWE) method using a Siemens ultrasound system and its combination with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for the differential diagnosis of benign and malignant thyroid nodules. MethodsConventional ultrasound images and 2D-SWE (E-whole-mean and E-stiffest-mean) were prospectively analyzed in 593 thyroid nodules from 543 patients. Nodules were divided into diameter (D) ≤10 mm and D > 10 mm groups and graded using ACR TI-RADS. The receiver operating characteristic curve was plotted using pathological findings as the gold standard. Diagnostic performance was compared among 2D-SWE, ACR TI-RADS, and their combination. ResultsThe area under the curve (AUC) for E-whole-mean was higher than that for E-stiffest-mean (0.858 vs. 0.790, P < 0.001), which indicated that it was the better 2D-SWE parameter for differentiating malignant nodules from benign nodules with an optimal cut-off point of 11.36 kPa. In the all-sizes group, the AUC for E-whole-mean was higher than that for ACR TI-RADS (0.858 vs. 0.808, P < 0.001). The combination of E-whole-mean and ACR TI-RADS resulted in a higher AUC (0.929 vs. 0.858 vs. 0.808, P < 0.001), sensitivity (87.0% vs. 80.3% vs. 85.2%), specificity (85.1% vs. 74.0% vs. 73.6%), accuracy (86.3% vs. 78.1% vs. 81.1%), positive predictive value (91.5% vs. 85.1% vs. 85.6%), and negative predictive value (78.0% vs. 67.0% vs. 72.9%) compared to E-whole-mean or ACR TI-RADS alone. The AUC for the combination of 2D-SWE and ACR TI-RADS was superior to that for E-whole-mean or ACR TI-RADS alone in both D ≤ 10 mm and D > 10 mm groups (P < 0.001). ConclusionAs the better 2D-SWE parameter, E-whole-mean had a higher diagnostic power than ACR TI-RADS and enhanced the diagnostic performance of ACR TI-RADS when identifying benign and malignant thyroid nodules. The combination of E-whole-mean and ACR TI-RADS improved the diagnostic performance compared to using ACR TI-RADS alone, providing a new and reliable method for the clinical diagnosis of thyroid nodules.

B-200 Preoperative Classification of Thyroid Nodules by Desorption Electrospray Ionization Mass Spectrometry Imaging of Fine Needle Aspiration Biopsies

Abstract Background Preoperative diagnosis of thyroid lesions by fine-needle aspiration (FNA) biopsy cytology can be challenging, and in up to 20% of cases is unachievable. Patients with an indeterminate preoperative diagnosis are often recommended for diagnostic surgery, with the majority receiving a benign diagnosis, rendering the surgery unnecessary. Consequently, new technologies that can provide accurate preoperative diagnosis of thyroid lesions are needed. Here, we have employed DESI-MS imaging along with statistical modeling to determine molecular signatures of benign and malignant thyroid lesions using banked thyroid tissue samples with known histopathology diagnosis. We then applied this methodology for analysis and classification of preoperatively collected FNA smears. Methods A total of 199 fresh-frozen thyroid tissues including 50 normal, 55 follicular adenoma (FTA), 58 follicular carcinoma (FTC), and 36 papillary carcinoma (PTC) were sectioned at a thickness of 10 µm and stored at −80 °C until analysis. FNA biopsies were prospectively collected at Baylor College of Medicine. DESI-MS imaging was performed in the negative ion mode using a Waters Xevo G2-XS mass spectrometer fitted with a DESI-XS source. Samples were H&amp;E stained and pathologically evaluated after analysis. Molecular profiles from tissue regions of clear histology were used to build classification models. For the FNA smears, mass spectra corresponding to clusters of thyroid cells were extracted for statistical prediction, and the predictive performance of the models on FNA smears was assessed in correlation with pathology. Results DESI-MS analysis of thyroid tissue sections generated 151 317 individual mass spectra, comprised of hundreds of lipid and metabolite features, that were used to build and validate two classification models: PTC vs benign thyroid, comprised of normal thyroid and FTA, and FTC vs benign thyroid. For each of these models, the data was randomly split with two-thirds used as a training set to generate the model and one-third used as an independent validation set to assess the performance of the model. For the PTC vs benign thyroid model, a prediction accuracy of 97.7% was achieved for the training set with an accuracy of 96.6% for the withheld validation dataset. For the FTC vs benign model, an accuracy of 78.4% was achieved for both the training and validation datasets. We are currently refining statistical workflows to improve the performance of the FTC model and to generate a model discriminating benign thyroid from thyroid cancer (FTC and PTC combined). When testing the prediction of the classifiers built from tissue imaging data on an initial sample set of 19 FNAs, overall per-sample prediction accuracies of 79% and 89% were obtained using the PTC vs benign thyroid model and the FTC vs benign thyroid model, respectively. We are currently working to collect, analyze, and validate the performance of the classification models on additional FNA biopsies, with a focus on samples with indeterminate cytology. Conclusions Overall prediction accuracies of 79% and 89% were achieved for the benign vs PTC model and benign vs FTC model, respectively, for thyroid FNA classification. With the addition of DESI-MS, unnecessary diagnostic surgeries could be prevented by providing improved preoperative specificity.

B-392 Standardization of Nucleic acid Extraction Method From Thyroid Fine Needle Aspiration (FNA) Cells on Stained and Fixed Slides for Next-Generation Sequencing Protocols

Abstract Background Thyroid Fine Needle Aspiration (FNA) cytopathology is the best method for histological classification of thyroid nodules. However, between 20%–30% of thyroid nodules show indeterminate classification and are often referred for surgery due to the malignancy risk. Post-surgical histological reports indicate that 84% of these cases are benign, highlighting a high rate of unnecessary surgeries. In this sense, molecular approaches using next-generation sequencing (NGS) have been useful to confirm benignity or malignancy. As important as the choice of a powerful methodology to evaluate different genes simultaneously such as NGS, is the standardization of nucleic acid extraction methods to allow sufficient quality recovery of these molecules. Pre-analytical steps involved in FNA slide preparation include fixation and stain methods that contribute to the low quality and poor recovery of nucleic acid, representing a great challenge to NGS analysis. Thus, this work aims to describe a standardization of nucleic acid extraction method from FNA slides for NGS protocols. Methodology Nucleic acid isolation from 30 FNA samples collected between 2016–2022 were performed by Trizol or RecoverAll kits, according to the manufacturer’s instructions (ThermoScientific). Initially, cells’ slides were counted in an optical microscope, considering cell clusters &amp;gt;10 (equivalent to 1 counter unit). Before extractions with Trizol, the slides were submerged in xylene for 15 min. For RecoverAll extractions, the varnished slides were previously exposed to dry ice for 5 min, while the slides containing coverslip were also exposed to dry ice and submerged in xylene for 15 min. In all cases, the slides were hydrated before scraping and then extracted. Subsequently, the extraction of whole slides and split slides was tested. To split slides, one-half of the slide was separated for RNA extraction, while the remaining half was for DNA extraction. Then, the DNA/RNA were quantified using the Qubit™ 4 Fluorometer equipment (ThermoScientific) and concentrated using Amicon® Ultra filters (Millipore). Results The DNA and RNA quantifications range from 0.0720–1.77 ng/µL and 0.508–7.43 ng/µL, respectively. Among samples, 12% (DNA) and 62.5% (RNA) presented concentration below the quantification limit (too low). Four samples extracted with Trizol did not obtain recovery of nucleic acid and this methodology was discontinued. The use of Amicon® filter improved all samples’ yield. The number of thyroid cell clusters was not proportional to the amount of DNA/RNA recovered. The split slides showed worst RNA/DNA recovery, being ideal to use at least two slides for each patient since co-extraction using the RecoverAll kit was not possible. Conclusion The isolations of nucleic acid from FNA slides are essential to performing subsequent molecular tests, such as NGS. However, specific protocols for nucleic acid extraction from FNA slides are hardly found in the literature. This work showed the combination of methodologies that allowed the scraping of material from FNA slides by exposure to dry ice in samples with varnish or coverslip. The nucleic acid recovery capacity described for this type of sample indicates a way of possibilities to improve the FNA extraction methodology.