Neural network analysis for evaluating cancer risk in thyroid nodules with an indeterminate diagnosis at aspiration cytology: identification of a low-risk subgroup

Artificial neural network-based gene screening and immune cell infiltration analysis of osteosarcoma feature

BACKGROUND

The present study aimed to construct an artificial neural network (ANN) model that leverages characteristic genes associated with osteosarcoma (OS) to enable accurate prognostication for OS patients.

METHODS

Our research revealed 467 differentially expressed genes (DEGs) via gene expression contrast analysis, consisting of 345 downregulated genes and 122 upregulated genes. Gene Ontology (GO) enrichment analysis illuminated functions primarily encompassing T-cell activation, secretory granule lumen and antioxidant activity, among others. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we discovered significant correlations between the DEGs and certain pathways, including phagosome, Staphylococcus aureus infection and human T-cell leukemia virus 1 infection. We then screened out 30 characteristic DEGs (CDEGs) based on random forest analysis and constructed the ANN model using the gene score matrix. To verify the credibility and accuracy of the ANN model, we performed internal and external validation processes, which affirmed our model's predictive capabilities.

RESULTS

The study further delved into the analysis of immune cell infiltration and its correlation with the target CDEGs, revealing disparities in the infiltration of 22 types of immune cells across different groups and their interrelationships. Moreover, we probed the expression of the two foremost CDEGs (YES1 and MFNG) in OS and normal tissues. We noted a positive relationship between the expression of YES1 and MFNG in OS tissues and the clinicopathological characteristics of OS patients.

CONCLUSIONS

Collectively, the findings of the present study validate the effectiveness of the CDEGs-based ANN model in predicting OS patients, which might facilitate early diagnosis and treatment of OS.

Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis

Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.

Applications of machine and deep learning to thyroid cytology and histopathology: a review

This review synthesises past research into how machine and deep learning can improve the cyto- and histopathology processing pipelines for thyroid cancer diagnosis. The current gold-standard preoperative technique of fine-needle aspiration cytology has high interobserver variability, often returns indeterminate samples and cannot reliably identify some pathologies; histopathology analysis addresses these issues to an extent, but it requires surgical resection of the suspicious lesions so cannot influence preoperative decisions. Motivated by these issues, as well as by the chronic shortage of trained pathologists, much research has been conducted into how artificial intelligence could improve current pipelines and reduce the pressure on clinicians. Many past studies have indicated the significant potential of automated image analysis in classifying thyroid lesions, particularly for those of papillary thyroid carcinoma, but these have generally been retrospective, so questions remain about both the practical efficacy of these automated tools and the realities of integrating them into clinical workflows. Furthermore, the nature of thyroid lesion classification is significantly more nuanced in practice than many current studies have addressed, and this, along with the heterogeneous nature of processing pipelines in different laboratories, means that no solution has proven itself robust enough for clinical adoption. There are, therefore, multiple avenues for future research: examine the practical implementation of these algorithms as pathologist decision-support systems; improve interpretability, which is necessary for developing trust with clinicians and regulators; and investigate multiclassification on diverse multicentre datasets, aiming for methods that demonstrate high performance in a process- and equipment-agnostic manner.

An artificial neural network for the prediction of the risk of malignancy in category III Bethesda thyroid lesions

BACKGROUND

The diagnosis of cases of atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) by fine needle aspiration cytology (FNAC) is challenging for both cytopathologists and clinicians. It is extremely difficult to predict the risk of malignancy based on cytological features alone.

AIMS AND OBJECTIVES

In this study, we attempted to construct an artificial neural network (ANN) model to predict the risk of malignancy in FNAC cases of AUS/FLUS in thyroid lesions based on cytological features.

MATERIALS AND METHODS

We included two groups of AUS/FLUS cases: (1) 29 cases of histopathologically proven malignancy, and (2) 32 cases that had either been histopathologically proven to be benign, or for which no progress of malignancy on follow-up had been observed in the last 2 years. Cytological characteristics were analysed semi-quantitatively by two independent observers (TS and PD). Based on these data, we tried to generate an artificial neural network (ANN) model to differentiate between malignant and benign cases. The performance of the ANN was assessed using the confusion matrix and receiving operator curve.

RESULTS

There were 29 malignant cases of AUS/FLUS (histopathologically proven) and 32 benign/follow-up cases in this study. There were 41 cases in the training set, 9 cases in the validation set and 11 cases in the test set. In the test group, the ANN model successfully distinguished between all benign (5/5) and malignant cases (6/6). The area under the receiver operating curve was 1.

CONCLUSION

The present ANN model is well structured and coherent to distinguish malignant from benign outcomes in AUS/FLUS cases on cytology smears with no error. This is an open-ended ANN model, and additional parameters and more cases could be included to make the model more robust.

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

Simple Summary

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

Abstract

Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.

Artificial neural network in diagnostic cytology

The artificial neural network (ANN) is a computer software design or model that simulates the biological neural network of the human brain. Instead of biological neurons, ANN is composed of many layers of nodes that carry the signal and process it to make the final decision. ANN is a modern technology that is widely used in different fields of science. The ANN is reshaping the medical system and the various areas of pathology. In this paper, the basic concept and applications of ANN in cytology have been discussed. In this paper, the various articles published on ANN in the field of cytology have been systemically reviewed. The ANN is relatively less used in cytology. After introducing convolutional neural network and whole slide scanners in the commercial market, it is now essential to have thorough knowledge in this field to start diagnostic application of ANN.

Deep learning for intelligent diagnosis in thyroid scintigraphy

OBJECTIVE

To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy.

METHODS

We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model's performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents.

RESULTS

The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided "diagnostic assistance" to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents.

CONCLUSION

DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves' disease and subacute thyroiditis.

Artificial Intelligence in Thyroid Fine Needle Aspiration Biopsies

BACKGROUND

From cell phones to aerospace, artificial intelligence (AI) has wide-reaching influence in the modern age. In this review, we discuss the application of AI solutions to an equally ubiquitous problem in cytopathology - thyroid fine needle aspiration biopsy (FNAB). Thyroid nodules are common in the general population, and FNAB is the sampling modality of choice. The resulting prevalence in the practicing pathologist's daily workload makes thyroid FNAB an appealing target for the application of AI solutions.

SUMMARY

This review summarizes all available literature on the application of AI to thyroid cytopathology. We follow the evolution from morphometric analysis to convolutional neural networks. We explore the application of AI technology to different questions in thyroid cytopathology, including distinguishing papillary carcinoma from benign, distinguishing follicular adenoma from carcinoma and identifying non-invasive follicular thyroid neoplasm with papillary-like nuclear features by key words and phrases. Key Messages: The current literature shows promise towards the application of AI technology to thyroid fine needle aspiration biopsy. Much work is needed to define how this powerful technology will be of best use to the future of cytopathology practice.

Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Objective

This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant.

Results

The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed.

Conclusion

AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.

Artificial intelligence may offer insight into factors determining individual TSH level

The factors that determine Serum Thyrotropin (TSH) levels have been examined through different methods, using different covariates. However, the use of machine learning methods has so far not been studied in population databases like NHANES (National Health and Nutritional Examination Survey) to predict TSH. In this study, we performed a comparative analysis of different machine learning methods like Linear regression, Random forest, Support vector machine, multilayer perceptron and stacking regression to predict TSH and classify individuals with normal, low and high TSH levels. We considered Free T4, Anti-TPO antibodies, T3, Body Mass Index (BMI), Age and Ethnicity as the predictor variables. A total of 9818 subjects were included in this comparative analysis. We used coefficient of determination (r²) value to compare the results for predicting the TSH and show that the Random Forest, Gradient Boosting and Stacking Regression perform equally well in predicting TSH and achieve the highest r² value = 0.13, with mean absolute error of 0.78. Moreover, we found that Anti-TPO is the most important feature in predicting TSH followed by Age, BMI, T3 and Free-T4 for the regression analysis. While classifying TSH into normal, high or low levels, our comparative analysis also shows that Random forest performs the best in the classification study, performed with individuals with normal, high and low levels of TSH. We found the following Areas Under Curve (AUC); for low TSH, AUC = 0.61, normal TSH, AUC = 0.61 and elevated TSH AUC = 0.69. Additionally, we found that Anti-TPO was the most important feature in classifying TSH. In this study, we suggest that artificial intelligence and machine learning methods might offer an insight into the complex hypothalamic-pituitary -thyroid axis and may be an invaluable tool that guides us in making appropriate therapeutic decisions (thyroid hormone dosing) for the individual patient.

Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape

Artificial intelligence (AI) has made impressive strides recently in interpreting complex images, thanks to improvements in deep learning techniques and increasing computational power. Researchers have started applying these advanced techniques to pathology images, although most efforts have been focused on histopathology. Cytopathology, however, remains the original field of pathology for which AI models for clinical use were successfully commercialized, to assist with automating Papanicolaou test screening. Recent AI efforts have focused on whole slide images of both gynecologic and non-gynecologic cytopathology. This review summarizes the literature and commercial landscape of AI as applied to cytopathology.

Digital Medicine in Thyroidology: A New Era of Managing Thyroid Disease

Digital medicine has the capacity to affect all aspects of medicine, including disease prediction, prevention, diagnosis, treatment, and post-treatment management. In the field of thyroidology, researchers are also investigating potential applications of digital technology for the thyroid disease. Recent studies using artificial intelligence (AI)/machine learning (ML) have reported reasonable performance for the classification of thyroid nodules based on ultrasonographic (US) images. AI/ML-based methods have also shown good diagnostic accuracy for distinguishing between benign and malignant thyroid lesions based on cytopathologic findings. Assistance from AI/ML methods could overcome the limitations of conventional thyroid US and fine-needle aspiration cytology. A web-based database has been developed for thyroid cancer care. In addition to its role as a nationwide registry of thyroid cancer, it is expected to serve as a clinical platform to facilitate better thyroid cancer care and as a research platform providing comprehensive disease-specific big data. Evidence has been found that biosignal monitoring with wearable devices may predict thyroid dysfunction. This real-world thyroid function monitoring could aid in the management and early detection of thyroid dysfunction. In the thyroidology field, research involving the range of digital medicine technologies and their clinical applications is expected to be even more active in the future.

Evaluation and Management of Indeterminate Thyroid Nodules: The Revolution of Risk Stratification Beyond Cytological Diagnosis

In accordance with National Guidelines, we currently follow a linear approach to the diagnosis of thyroid nodules, with management decision based primarily on a cytological diagnosis following fine-needle aspiration biopsy. However, 25% of these biopsies render an indeterminate cytology, leaving uncertainty regarding appropriate management. Individualizing the risk of malignancy of these nodules could improve their management significantly. We summarize the current evidence on the relevance of clinical information, radiological features, cytological features, and molecular markers tests results and describe how these can be integrated to personalize the management of thyroid nodules with indeterminate cytology. Several factors can be used to stratify the risk of malignancy in thyroid nodules with indeterminate cytology. Male gender, large tumors (>4 cm), suspicious sonographic patterns, and the presence of nuclear atypia on the cytology are all associated with an increased cancer prevalence. The added value of current molecular markers in the risk stratification process needs further study because their performance seems compromised in some clinical settings and remains to be validated in others. Risk stratification is possible in thyroid nodules with indeterminate cytology using data that are often underused by current guidelines. Future guidelines should integrate these factors and personalize the recommended diagnostic and therapeutic approaches accordingly.

Nodular Thyroid Disease and Thyroid Cancer in the Era of Precision Medicine

The management of thyroid nodules, one of the main clinical challenges in endocrine clinical practice, is usually straightforward. Although the most important concern is ruling out malignancy, there are grey areas where uncertainty is frequently present: the nodules labelled as indeterminate by cytology and the extent of therapy when thyroid cancer is diagnosed pathologically. There is evidence that the current available precision medicine tools (from all the "-omics" to molecular analysis, fine-tuning imaging or artificial intelligence) may help to fill present gaps in the future. We present here a commentary on some of the current challenges faced by endocrinologists in the field of thyroid nodules and cancer, and illustrate how precision medicine may improve their diagnostic and therapeutic capabilities in the future.

Evaluation of an Artificial Neural Network to Predict Urea Nitrogen Appearance for Critically Ill Multiple-Trauma Patients

BACKGROUND

Computer-based simulated biologic neural network models have made significant strides in clinical medicine.

METHODS

To determine the predictive performance of a conventional regression model and an artificial neural network for estimating urea nitrogen appearance (UNA) during critical illness, 125 adult patients admitted to the trauma intensive care unit who required specialized nutrition support were studied. The first 100 consecutive patients were used to develop the 2 models. The first model used stepwise multivariate regression analysis. The second model entailed the use of a feeding-forward, back-propagation, supervised neural network. Bias and precision of both methods were evaluated in 25 separate patients.

RESULTS

Multivariate regression analysis revealed a significant highly correlative relationship (r(2) = .918, p < or = .01): Predicted UNA (g/d) = (0.29 x WT) + (1.20 x WBC) + (0.44 x SUN) with WT as current body weight in kg, WBC as white blood cell count in cells/mm(3), and SUN as serum urea nitrogen concentration (mg/dL). The regression method was biased toward overestimating measured UNA, whereas the neural network was unbiased. Precision (95% confidence interval) of the neural network was significantly better than the regression (3.3-7.2 g vs 7.3-11.6 g, respectively, p < .01). Regression analysis successfully predicted UNA within 3 g of measured UNA in 16% (4 of 25) of patients, whereas the neural network successfully predicted UNA in 44% (11 out of 25) of patients (p < .06).

CONCLUSIONS

These preliminary data indicate that use of an artificial neural network may be superior to conventional regression modeling techniques for estimating UNA in critically ill adult multiple-trauma patients receiving specialized nutrition support.

Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future

OBJECTIVE

This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics.

STUDY DESIGN

A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted.

RESULTS

The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material.

CONCLUSIONS

Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.

Artificial neural network in diagnosis of urothelial cell carcinoma in urine cytology

AIMS AND OBJECTIVE

To build up an artificial neural network (ANN) model in the diagnosis of urothelial cell carcinoma (UCC) in urine cytology smears.

MATERIAL AND METHODS

We randomly selected a total of 115 urine cytology samples, out of which 59 were histopathology proven UCC cases and remaining 56 were benign cases from routine cytology samples. All the carcinoma cases were proven on histopathology. Image morphometric analysis was performed on Papanicolaou's stained smears to study nuclear area, diameter, perimeter, standard deviation of nuclear area, and integrated gray density. Detailed cytological features were also studied in each case by two independent observers and were semi-quantitatively graded. The back propagation ANN model was designed as 17-11-3 with the help of heuristic search. The cases were randomly partitioned as training, validation, and testing sets by the program. There were 79 cases for training set, 18 cases for validation set and 18 cases for test set.

RESULT

In the training set, ANN was able to diagnose all the malignant and benign cases. In the test set, all the benign and malignant cases were diagnosed correctly. However, one of the low grade cases was diagnosed as high grade UCC by ANN model.

CONCLUSIONS

We successfully built an ANN model in urine from the visual and morphometric data to identify the benign and malignant cases. In addition, the system can also identify the low grade and high grade UCC cases.

Malignancy risk assessment in patients with thyroid nodules using classification and regression trees

Purpose. We sought to investigate the utility of classification and regression trees (CART) classifier to differentiate benign from malignant nodules in patients referred for thyroid surgery. Methods. Clinical and demographic data of 271 patients referred to the Sadoughi Hospital during 2006–2011 were collected. In a two-step approach, a CART classifier was employed to differentiate patients with a high versus low risk of thyroid malignancy. The first step served as the screening procedure and was tailored to produce as few false negatives as possible. The second step identified those with the lowest risk of malignancy, chosen from a high risk population. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) of the optimal tree were calculated. Results. In the first step, age, sex, and nodule size contributed to the optimal tree. Ultrasonographic features were employed in the second step with hypoechogenicity and/or microcalcifications yielding the highest discriminatory ability. The combined tree produced a sensitivity and specificity of 80.0% (95% CI: 29.9–98.9) and 94.1% (95% CI: 78.9–99.0), respectively. NPV and PPV were 66.7% (41.1–85.6) and 97.0% (82.5–99.8), respectively. Conclusion. CART classifier reliably identifies patients with a low risk of malignancy who can avoid unnecessary surgery.

Thyroid nodule guidelines: agreement, disagreement and need for future research

This article reviews agreement, disagreement and need for future research of the thyroid nodule guidelines published by the British Thyroid Association, National Cancer Institute, American Thyroid Association and the joint, transatlantic effort of three large societies, the American Society of Clinical Endocrinologists, Associazione Medici Endocrinologi and the European Thyroid Association, published in 2010. Consensus exists for most topics in the various guidelines. A few areas of disagreement, such as the use of scintigraphy, are mostly due to differences in disease prevalence in different countries. Most of the discordance, for example, on the use of calcitonin screening or fine-needle aspiration cytology classification, could probably be resolved by further expert discussions, as the basis is the same published evidence. Importantly, owing to a current lack of evidence in many areas, clinically very relevant areas of uncertainty need to be addressed by further research. This situation applies, for instance, to better definition of ultrasound malignancy criteria and the evaluation of emerging new diagnostic and therapeutic techniques, including molecular markers. For clinicians who advise individual patients, these areas of uncertainty can currently only be resolved by sound management on the basis of clinical judgment, experience and patient preference.

Approach to the patient with a cytologically indeterminate thyroid nodule

BACKGROUND

Fine-needle aspiration remains the primary diagnostic intervention for the evaluation of most thyroid nodules larger than 1-1.5 cm. Although most aspirates provide diagnostic cytology, approximately 15-25% will be classified indeterminate (often referred to as follicular neoplasm, suspicious for carcinoma, or atypical). In such cases, abnormal cellular findings preclude interpretation of benignity, although only a minority will prove cancerous upon final histopathology. Nonetheless, patients with indeterminate aspirates are commonly referred for consideration of hemi- or near-total thyroidectomy. Recently, improved understanding and novel investigation of clinical, radiological, cytological, and molecular factors has allowed improved stratification of cancer risk.

CONCLUSION

Although surgery continues to be commonly recommended, strategies for such patients should increasingly seek to define treatment based on the estimation of an individual's thyroid cancer risk in comparison with associated operative risk and morbidity. In doing so, the rate of unnecessary surgical procedures and associated complications can be reduced.

Detection of RET/PTC, TRK and BRAF mutations in preoperative diagnosis of thyroid nodules with indeterminate cytological findings

BACKGROUND

Fine-needle aspiration biopsy (FNAB) is the primary means to distinguish benign from malignant nodules and select patients for surgery. However, adjunctive diagnostic tests are needed because in 20-40% of cases the FNAB result is uncertain.

OBJECTIVE

We investigated whether a search for the oncogenes RET/PTC, TRK and BRAF(V600E) in thyroid aspirates could refine an uncertain diagnosis.

PATIENTS AND METHODS

A total of 132 thyroid aspirates, including colloid nodules, inadequate samplings, indeterminate and suspicious for malignancy were analysed by reverse transcription polymerase chain reaction (RT-PCR) and mutant allele-specific amplification techniques for the presence of oncogenes.

RESULTS

No oncogenes were detected in 48 colloid nodules, 46 inadequate and 19 indeterminate FNABs, then confirmed to be benign at histology. No oncogenes were detected in one follicular thyroid cancer (FTC) with indeterminate cytology. Five out of six papillary thyroid cancers (83%) with FNAB suspicious for malignancy were correctly diagnosed by the presence of oncogenes. Among these, four (67%) contained the BRAF mutation and one (17%) contained RET/PTC-3. On final analysis, no false-positive results were reported in 131 samples and five out of seven carcinomas (71%) were correctly diagnosed. The finding of oncogenes in FNAB specimens suspicious for malignancy guided the extent of surgical resection, changing the surgery from diagnostic to therapeutic in five cases.

CONCLUSIONS

Detection of RET/PTC, TRK and BRAF(V600E) in FNAB specimens is proposed as a diagnostic adjunctive tool in the evaluation of thyroid nodules with suspicious cytological findings.