Development of a clinical decision model for thyroid nodules

<p>Abstract</p> <p>Background</p> <p>Thyroid nodules represent a common problem brought to medical attention. Four to seven percent of the United States adult population (10–18 million people) has a palpable thyroid nodule, however the majority (>95%) of thyroid nodules are benign. While, fine needle aspiration remains the most cost effective and accurate diagnostic tool for thyroid nodules in current practice, over 20% of patients undergoing FNA of a thyroid nodule have indeterminate cytology (follicular neoplasm) with associated malignancy risk prevalence of 20–30%. These patients require thyroid lobectomy/isthmusectomy purely for the purpose of attaining a definitive diagnosis. Given that the majority (70–80%) of these patients have benign surgical pathology, thyroidectomy in these patients is conducted principally with diagnostic intent. Clinical models predictive of malignancy risk are needed to support treatment decisions in patients with thyroid nodules in order to reduce morbidity associated with unnecessary diagnostic surgery.</p> <p>Methods</p> <p>Data were analyzed from a completed prospective cohort trial conducted over a 4-year period involving 216 patients with thyroid nodules undergoing ultrasound (US), electrical impedance scanning (EIS) and fine needle aspiration cytology (FNA) prior to thyroidectomy. A Bayesian model was designed to predict malignancy in thyroid nodules based on multivariate dependence relationships between independent covariates. Ten-fold cross-validation was performed to estimate classifier error wherein the data set was randomized into ten separate and unique train and test sets consisting of a training set (90% of records) and a test set (10% of records). A receiver-operating-characteristics (ROC) curve of these predictions and area under the curve (AUC) were calculated to determine model robustness for predicting malignancy in thyroid nodules.</p> <p>Results</p> <p>Thyroid nodule size, FNA cytology, US and EIS characteristics were highly predictive of malignancy. Cross validation of the model created with Bayesian Network Analysis effectively predicted malignancy [AUC = 0.88 (95%CI: 0.82–0.94)] in thyroid nodules. The positive and negative predictive values of the model are 83% (95%CI: 76%–91%) and 79% (95%CI: 72%–86%), respectively.</p> <p>Conclusion</p> <p>An integrated predictive decision model using Bayesian inference incorporating readily obtainable thyroid nodule measures is clinically relevant, as it effectively predicts malignancy in thyroid nodules. This model warrants further validation testing in prospective clinical trials.</p

Differentiation of thyroid nodules on US using features learned and extracted from various convolutional neural networks

Thyroid nodules are a common clinical problem. Ultrasonography (US) is the main tool used to sensitively diagnose thyroid cancer. Although US is non-invasive and can accurately differentiate benign and malignant thyroid nodules, it is subjective and its results inevitably lack reproducibility. Therefore, to provide objective and reliable information for US assessment, we developed a CADx system that utilizes convolutional neural networks and the machine learning technique. The diagnostic performances of 6 radiologists and 3 representative results obtained from the proposed CADx system were compared and analyzed.ope

Diagnosis of Thyroid Nodules: Performance of a Deep Learning Convolutional Neural Network Model vs. Radiologists

Computer-aided diagnosis (CAD) systems hold potential to improve the diagnostic accuracy of thyroid ultrasound (US). We aimed to develop a deep learning-based US CAD system (dCAD) for the diagnosis of thyroid nodules and compare its performance with those of a support vector machine (SVM)-based US CAD system (sCAD) and radiologists. dCAD was developed by using US images of 4919 thyroid nodules from three institutions. Its diagnostic performance was prospectively evaluated between June 2016 and February 2017 in 286 nodules, and was compared with those of sCAD and radiologists, using logistic regression with the generalized estimating equation. Subgroup analyses were performed according to experience level and separately for small thyroid nodules 1-2 cm. There was no difference in overall sensitivity, specificity, positive predictive value (PPV), negative predictive value and accuracy (all p > 0.05) between radiologists and dCAD. Radiologists and dCAD showed higher specificity, PPV, and accuracy than sCAD (all p < 0.001). In small nodules, experienced radiologists showed higher specificity, PPV and accuracy than sCAD (all p < 0.05). In conclusion, dCAD showed overall comparable diagnostic performance with radiologists and assessed thyroid nodules more effectively than sCAD, without loss of sensitivity.ope

Molecular marker combinations preoperatively differentiate benign from malignant thyroid tumors

ABSTRACT Background. The initial presentation of thyroid carcinoma is through a nodule and the best way nowadays to evaluate it is by fine-needle aspiration (FNA). However many thyroid FNAs are not definitively benign or malignant, yielding an indeterminate or suspicious diagnosis which ranges from 10 to 25% of FNAs. The development of molecular initial diagnostic tests for evaluating a thyroid nodule is needed in order to define optimal surgical approach for patients with uncertain diagnosis pre- and intra-operatively. A large amount of information has been collected on the molecular tumorigenesis of thyroid cancer. A low expression of KIT gene has been reported during the transformation of normal thyroid epithelium to papillary carcinoma suggesting a possible role of the gene in the differentiation of thyroid tissue rather than in the proliferation. Moreover, several gene expression studies have shown differential gene expression signatures between malignant and benign thyroid tumors. The aim of the current study was to determine the diagnostic utility of a molecular assay based on the gene expression of a panel of molecular markers (KIT, SYNGR2, C21orf4, Hs.296031, DDI2, CDH1, LSM7, TC1, NATH) plus BRAF mutational status to distinguish benign from malignant thyroid neoplasm. Methods. The mRNA expression level of 9 genes (KIT, SYNGR2, C21orf4, Hs.296031, DDI2, CDH1, LSM7, TC1, NATH) was analyzed by quantitative Real-Time PCR (qPCR) in 93 FNA cytological samples. To evaluate the diagnostic utility of all the genes analyzed, we assessed the area under the curve (AUC) for each gene individually and in combination. BRAF exon 15 status was determined by capillary sequencing. A gene expression computational model (Neural Network Bayesian Classifier) was built and a multiple-variable analysis was then performed to analyze the correlation between the markers. Results. While looking at KIT expression, we have found a highly preferential decrease rather than increase in transcript of KIT in malignant thyroid lesions compared to the benign ones. To explore the diagnostic utility of KIT expression in thyroid nodules, its expression values were divided in four arbitrarily defined classes, with class I characterized by the complete silencing of the gene. Class I and IV represented the two most informative groups, with 100% of the samples found malignant or benign respectively. The molecular analysis was proven by ROC (receiver operating characteristic) analysis to be highly specific and sensitive improving the cytological diagnostic accuracy of 15%. The AUC for each significant marker was further assessed and ranged between 0.625 and 0.900, thus all the significant markers, alone and in combination, can be used to distinguish between malignant and benign FNA samples. The classifier made up of KIT, CDH1, LSM7, C21orf4, DDI2, TC1, Hs.296031 and BRAF had a predictive power of 88.8%. It proved to be useful for risk stratification of the most critical cytological group of the indeterminate lesions for which there is the greatest need for accurate diagnostic markers. Conclusion. The genetic classification obtained with such a model is highly accurate and may provide a tool to overcome the difficulties in today’s pre-operative diagnosis of thyroid malignancies

The role of different molecular markers in thyroid epithelium transformation: functional studies and possible clinical implications.

ABSTRACT Background. Papillary thyroid cancer (PTC) is the most common (~90%) endocrine malignancy. The first manifestation of the thyroid cancer is through thyroid nodules and the most sensitive and specific diagnostic tool to detect malignancy in patients with thyroid nodules is fine-needle aspiration biopsy (FNAB). Nevertheless, sometimes it is not efficient enough to give a specific diagnosis leading to the so called diagnoses of indeterminate or suspicious lesions for PTC which ranges from 20 to 30% of cases. BRAF mutational analysis is commonly used to assess the malignancy of thyroid nodules but unfortunately it still leaves indeterminate diagnoses. Recent studies conducted in our laboratories have shown a significant highly decrease rather than increase in transcript of c-KIT in malignant thyroid lesions compared to the benign ones, and it was demonstrated to be effective as a new biomarker in the preoperative diagnosis of thyroid tumors. Aim: The aim of the present study is mainly to investigate thoroughly the role of the c-KIT gene in thyroid cancerogenesis, and to characterize in details the c-KIT signaling pathway and the cause of its down-regulation in thyroid cancer. Another aim of this present study is to identify other molecular markers in order to improve the cytological diagnosis and to better understand the mechanisms underlying thyroid epithelium transformation. Methods: We have collected 169 pre-operative thyroid Fine Needle Aspirate (FNA) sample. All 169 FNA samples analyzed in this study were molecularly characterized for the presence of the V600E BRAF mutation in exon 15. SNP analysis, methylation analysis and various gene expression analyses were conducted in order to clarify c-Kit role in thyroid neoplastic trasformation. Gene expression computational models (Neural Network Bayesian Classifier, Discrimination Analysis) were built, together with ROC curves and PCA (Principal Component Analysis) to distinguish a malignant/benign status and BRAF status. Finally a panel of 84 Human Tyrosine Kinases gene array was amplified on 8 benign samples and 12 malignant samples. Results: 64/103 malignant samples carried the V600E mutation while all 66 benign samples were wild type for BRAF exon15. The results of the analysis related to c-KIT function support our hypothesis that this receptor controls a differentiation pathway in thyrocytes. Methylation biochemal process and 146b/222 miRNA expression account for part of the c-KIT dowregulation. The Bayesian Artificial Neural Network and Discriminant Analysis, made of 4 gene (KIT, TC1, miRNA222, miRNA146b) showed a very strong predictive value (94.12% and 92.16% 7 respectively) in discriminating malignant from benign patients and it is interesting to notice that Discriminant Analysis showed a correct classification of 100.00 % of the samples in the malignant group, and 95.00 % by BNN. This same model defines two clearly different genetic background related to BRAF mutational status. In the panel of 84 Human Tyrosine Kinases gene array we found in three (malignant vs benign; V600E vs benign; WT malignant vs benign) of the four conducted comparisons, four genes (ALK, CSK, HCK e MSTR1) in common that had a significantly altered expression. Conclusion: The results of this research support the idea that c-KIT is driving a thyroid cell differentiation pathway, which results altered in thyroid neoplasm transformation. In the same study a 4 gene model was build able to discriminate with high probability between benign and malignant FNAs. The model is proposed to be added to the routinely BRAF diagnostic test in order to improve FNA diagnostic accuracy solving the problems of the nodules that otherwise would remain suspicious. Moreover the present study shows clearly how the presence of the BRAF V600E mutation is accompanied by a unique genetic scenario in which sets of genes specifically discriminate the mutational and wild-type status. Several tyrosine kinase genes showed statistically significant differential expression between malignant and benign thyroid nodules

Classification of breast lesions in ultrasonography using sparse logistic regression and morphology‐based texture features

Purpose: This work proposes a new reliable computer‐aided diagnostic (CAD) system for the diagnosis of breast cancer from breast ultrasound (BUS) images. The system can be useful to reduce the number of biopsies and pathological tests, which are invasive, costly, and often unnecessary. Methods: The proposed CAD system classifies breast tumors into benign and malignant classes using morphological and textural features extracted from breast ultrasound (BUS) images. The images are first preprocessed to enhance the edges and filter the speckles. The tumor is then segmented semiautomatically using the watershed method. Having the tumor contour, a set of 855 features including 21 shape‐based, 810 contour‐based, and 24 textural features are extracted from each tumor. Then, a Bayesian Automatic Relevance Detection (ARD) mechanism is used for computing the discrimination power of different features and dimensionality reduction. Finally, a logistic regression classifier computed the posterior probabilities of malignant vs benign tumors using the reduced set of features. Results: A dataset of 104 BUS images of breast tumors, including 72 benign and 32 malignant tumors, was used for evaluation using an eightfold cross‐validation. The algorithm outperformed six state‐of‐the‐art methods for BUS image classification with large margins by achieving 97.12% accuracy, 93.75% sensitivity, and 98.61% specificity rates. Conclusions: Using ARD, the proposed CAD system selects five new features for breast tumor classification and outperforms state‐of‐the‐art, making a reliable and complementary tool to help clinicians diagnose breast cancer

Characterization of alar ligament on 3.0T MRI: a cross-sectional study in IIUM Medical Centre, Kuantan

INTRODUCTION: The main purpose of the study is to compare the normal anatomy of alar ligament on MRI between male and female. The specific objectives are to assess the prevalence of alar ligament visualized on MRI, to describe its characteristics in term of its course, shape and signal homogeneity and to find differences in alar ligament signal intensity between male and female. This study also aims to determine the association between the heights of respondents with alar ligament signal intensity and dimensions. MATERIALS & METHODS: 50 healthy volunteers were studied on 3.0T MR scanner Siemens Magnetom Spectra using 2-mm proton density, T2 and fat-suppression sequences. Alar ligament is depicted in 3 planes and the visualization and variability of the ligament courses, shapes and signal intensity characteristics were determined. The alar ligament dimensions were also measured. RESULTS: Alar ligament was best depicted in coronal plane, followed by sagittal and axial planes. The orientations were laterally ascending in most of the subjects (60%), predominantly oval in shaped (54%) and 67% showed inhomogenous signal. No significant difference of alar ligament signal intensity between male and female respondents. No significant association was found between the heights of the respondents with alar ligament signal intensity and dimensions. CONCLUSION: Employing a 3.0T MR scanner, the alar ligament is best portrayed on coronal plane, followed by sagittal and axial planes. However, tremendous variability of alar ligament as depicted in our data shows that caution needs to be exercised when evaluating alar ligament, especially during circumstances of injury

Case series of breast fillers and how things may go wrong: radiology point of view

INTRODUCTION: Breast augmentation is a procedure opted by women to overcome sagging breast due to breastfeeding or aging as well as small breast size. Recent years have shown the emergence of a variety of injectable materials on market as breast fillers. These injectable breast fillers have swiftly gained popularity among women, considering the minimal invasiveness of the procedure, nullifying the need for terrifying surgery. Little do they know that the procedure may pose detrimental complications, while visualization of breast parenchyma infiltrated by these fillers is also deemed substandard; posing diagnostic challenges. We present a case series of three patients with prior history of hyaluronic acid and collagen breast injections. REPORT: The first patient is a 37-year-old lady who presented to casualty with worsening shortness of breath, non-productive cough, central chest pain; associated with fever and chills for 2-weeks duration. The second patient is a 34-year-old lady who complained of cough, fever and haemoptysis; associated with shortness of breath for 1-week duration. CT in these cases revealed non thrombotic wedge-shaped peripheral air-space densities. The third patient is a 37‐year‐old female with right breast pain, swelling and redness for 2- weeks duration. Previous collagen breast injection performed 1 year ago had impeded sonographic visualization of the breast parenchyma. MRI breasts showed multiple non- enhancing round and oval shaped lesions exhibiting fat intensity. CONCLUSION: Radiologists should be familiar with the potential risks and hazards as well as limitations of imaging posed by breast fillers such that MRI is required as problem-solving tool