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

Expressing Bayesian Fusion as a Product of Distributions: Application to Randomized Hough Transform

Data fusion is a common issue of mobile robotics, computer assisted medical diagnosis or behavioral control of simulated character for instance. However data sources are often noisy, opinion for experts are not known with absolute precision, and motor commands do not act in the same exact manner on the environment. In these cases, classic logic fails to manage efficiently the fusion process. Confronting different knowledge in an uncertain environment can therefore be adequately formalized in the bayesian framework. Besides, bayesian fusion can be expensive in terms of memory usage and processing time. This paper precisely aims at expressing any bayesian fusion process as a product of probability distributions in order to reduce its complexity. We first study both direct and inverse fusion schemes. We show that contrary to direct models, inverse local models need a specific prior in order to allow the fusion to be computed as a product. We therefore propose to add a consistency variable to each local model and we show that these additional variables allow the use of a product of the local distributions in order to compute the global probability distribution over the fused variable. Finally, we take the example of the Randomized Hough Transform. We rewrite it in the bayesian framework, considering that it is a fusion process to extract lines from couples of dots in a picture. As expected, we can find back the expression of the Randomized Hough Transform from the literature with the appropriate assumptions

A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation

Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that is understandable by human operators who make the final maintenance decision. The main idea of the method is to generate a very large number of binary indicators based on parametric anomaly scores designed by experts, complemented by simple aggregations of those scores. The best indicators are selected via a classical forward scheme, leading to a much reduced number of indicators that are tuned to a data set. We illustrate the interest of the method on simulated data which contain realistic early signs of anomalies.Comment: Proceedings of the 14th Industrial Conference, ICDM 2014, St. Petersburg : Russian Federation (2014

Simplified Cooperative Detection for Multi-Receiver Molecular Communication

Diffusion-based molecular communication (MC) systems experience significant reliability losses. To boost the reliability, an MC scheme where multiple receivers (RXs) work cooperatively to decide the signal of a transmitter (TX) by sending the same type of molecules to a fusion center (FC) is proposed in this paper. The FC observes the total number of molecules received and compares this number with a threshold to determine the TX's signal. The proposed scheme is more bio-realistic and requires relatively low computational complexity compared to existing cooperative schemes where the RXs send and the FC recognizes different types of molecules. Asymmetric and symmetric topologies are considered, and closed-form expressions are derived for the global error probability for both topologies. Results show that the trade-off for simplified computations leads to a slight reduction in error performance, compared to the existing cooperative schemes.Comment: 5 pages, 4 figures, Will be presented as an invited paper at the 2017 IEEE Information Theory Workshop in November 2017 in Kaohsiung, Taiwa