Comparative analysis of diagnostic performance, feasibility and cost of different test-methods for thyroid nodules with indeterminate cytology

Since it is impossible to recognize malignancy at fine needle aspiration (FNA) cytology in indeterminate thyroid nodules, surgery is recommended for all of them. However, cancer rate at final histology is < 30%. Many different test-methods have been proposed to increase diagnostic accuracy in such lesions, including Galectin-3-ICC (GAL-3-ICC), BRAF mutation analysis (BRAF), Gene Expression Classifier (GEC) alone and GEC+BRAF, mutation/fusion (M/F) panel, alone, M/F panel+miRNA GEC, and M/F panel by next generation sequencing (NGS), FDG-PET/CT, MIBI-Scan and TSHR mRNA blood assay. We performed systematic reviews and meta-analyses to compare their features, feasibility, diagnostic performance and cost. GEC, GEC+BRAF, M/F panel+miRNA GEC and M/F panel by NGS were the best in ruling-out malignancy (sensitivity = 90%, 89%, 89% and 90% respectively). BRAF and M/F panel alone and by NGS were the best in ruling-in malignancy (specificity = 100%, 93% and 93%). The M/F by NGS showed the highest accuracy (92%) and BRAF the highest diagnostic odds ratio (DOR) (247). GAL-3-ICC performed well as rule-out (sensitivity = 83%) and rule-in test (specificity = 85%), with good accuracy (84%) and high DOR (27) and is one of the cheapest (113 USD) and easiest one to be performed in different clinical settings. In conclusion, the more accurate molecular-based test-methods are still expensive and restricted to few, highly specialized and centralized laboratories. GAL-3-ICC, although limited by some false negatives, represents the most suitable screening test-method to be applied on a large-scale basis in the diagnostic algorithm of indeterminate thyroid lesions

Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date

A Testability Analysis Framework for Non-Functional Properties

This paper presents background, the basic steps and an example for a testability analysis framework for non-functional properties

Familial hypercholesterolemia: a systematic review of guidelines on genetic testing and patient management

BACKGROUND: Familial hypercholesterolemia (FH) is an autosomal-dominant hereditary disorder of lipid metabolism that causes lifelong exposure to increased LDL levels resulting in premature coronary heart disease and, if untreated, death. Recent studies have shown its prevalence to be higher than previously considered, which has important implications for the mortality and morbidity of associated cardiovascular disease (CVD). Several clinical tools are used worldwide to help physicians diagnose FH, but nevertheless most patients remain undetected. This systematic review of guidelines aims to assess the role of genetic testing in the screening, diagnosis, and management of patients affected by heterozygous or homozygous FH and to identify related health-care pathways. METHODS: We performed a systematic review of the literature; inclusion criteria were English or Italian guidelines focusing on genetic testing. The guidelines were included and evaluated for their content and development process using the Appraisal of Guidelines for Research and Evaluation II instrument. RESULTS: Ten guidelines were considered eligible, and all were judged to be of good quality, with slight differences among them. The most common indications for performing genetic tests were high levels of cholesterol, or physical findings consistent with lipid disorder, in the subject or in the family history. Subsequent screening of family members was indicated when a mutation had been identified in the index patient. Regarding patient management, the various guidelines agreed that intensive treatment with lipid-lowering medications should begin as quickly as possible and that lifestyle modifications should be an integral part of the therapy. CONCLUSION: Since the early detection of affected patients is beneficial for effective prevention of CVD, genetic testing is particularly useful for identifying family members via cascade screening and for distinguishing between heterozygous and homozygous individuals, the latter of which require more extreme therapeutic intervention

Metamodel Instance Generation: A systematic literature review

Modelling and thus metamodelling have become increasingly important in Software Engineering through the use of Model Driven Engineering. In this paper we present a systematic literature review of instance generation techniques for metamodels, i.e. the process of automatically generating models from a given metamodel. We start by presenting a set of research questions that our review is intended to answer. We then identify the main topics that are related to metamodel instance generation techniques, and use these to initiate our literature search. This search resulted in the identification of 34 key papers in the area, and each of these is reviewed here and discussed in detail. The outcome is that we are able to identify a knowledge gap in this field, and we offer suggestions as to some potential directions for future research.Comment: 25 page