Automated recognition of lung diseases in CT images based on the optimum-path forest classifier

The World Health Organization estimated that around 300 million people have asthma, and 210 million people are affected by Chronic Obstructive Pulmonary Disease (COPD). Also, it is estimated that the number of deaths from COPD increased 30% in 2015 and COPD will become the third major cause of death worldwide by 2030. These statistics about lung diseases get worse when one considers fibrosis, calcifications and other diseases. For the public health system, the early and accurate diagnosis of any pulmonary disease is mandatory for effective treatments and prevention of further deaths. In this sense, this work consists in using information from lung images to identify and classify lung diseases. Two steps are required to achieve these goals: automatically extraction of representative image features of the lungs and recognition of the possible disease using a computational classifier. As to the first step, this work proposes an approach that combines Spatial Interdependence Matrix (SIM) and Visual Information Fidelity (VIF). Concerning the second step, we propose to employ a Gaussian-based distance to be used together with the optimum-path forest (OPF) classifier to classify the lungs under study as normal or with fibrosis, or even affected by COPD. Moreover, to confirm the robustness of OPF in this classification problem, we also considered Support Vector Machines and a Multilayer Perceptron Neural Network for comparison purposes. Overall, the results confirmed the good performance of the OPF configured with the Gaussian distance when applied to SIM- and VIF-based features. The performance scores achieved by the OPF classifier were as follows: average accuracy of 98.2%, total processing time of 117 microseconds in a common personal laptop, and F-score of 95.2% for the three classification classes. These results showed that OPF is a very competitive classifier, and suitable to be used for lung disease classification

Measurements of CFTR-Mediated Cl- Secretion in Human Rectal Biopsies Constitute a Robust Biomarker for Cystic Fibrosis Diagnosis and Prognosis

BACKGROUND: Cystic Fibrosis (CF) is caused by ∼1,900 mutations in the CF transmembrane conductance regulator (CFTR) gene encoding for a cAMP-regulated chloride (Cl(-)) channel expressed in several epithelia. Clinical features are dominated by respiratory symptoms, but there is variable organ involvement thus causing diagnostic dilemmas, especially for non-classic cases. METHODOLOGY/PRINCIPAL FINDINGS: To further establish measurement of CFTR function as a sensitive and robust biomarker for diagnosis and prognosis of CF, we herein assessed cholinergic and cAMP-CFTR-mediated Cl(-) secretion in 524 freshly excised rectal biopsies from 118 individuals, including patients with confirmed CF clinical diagnosis (n=51), individuals with clinical CF suspicion (n=49) and age-matched non-CF controls (n=18). Conclusive measurements were obtained for 96% of cases. Patients with "Classic CF", presenting earlier onset of symptoms, pancreatic insufficiency, severe lung disease and low Shwachman-Kulczycki scores were found to lack CFTR-mediated Cl(-) secretion (<5%). Individuals with milder CF disease presented residual CFTR-mediated Cl(-) secretion (10-57%) and non-CF controls show CFTR-mediated Cl(-) secretion ≥ 30-35% and data evidenced good correlations with various clinical parameters. Finally, comparison of these values with those in "CF suspicion" individuals allowed to confirm CF in 16/49 individuals (33%) and exclude it in 28/49 (57%). Statistical discriminant analyses showed that colonic measurements of CFTR-mediated Cl(-) secretion are the best discriminator among Classic/Non-Classic CF and non-CF groups. CONCLUSIONS/SIGNIFICANCE: Determination of CFTR-mediated Cl(-) secretion in rectal biopsies is demonstrated here to be a sensitive, reproducible and robust predictive biomarker for the diagnosis and prognosis of CF. The method also has very high potential for (pre-)clinical trials of CFTR-modulator therapies.This work was supported by grants TargetScreen2 (EU/FP6/LSH/2005/037365), PIC/IC/83103/2007; PTDC/MAT/118335/2010; PEstOE/BIA/UI4046/2011 (to BioFIG) and PEstOE/MAT/UI0006/2011 (to CEAUL) from FCT (Portugal); and FAPESP (SPRF, Brazil), CNPq (40.8924/2006/3, Brazil) and Mukoviszidose e.V. S02/10 (Germany). MS and IU are recipients of SFRH/BD/35936/2007 and SFRH/BD/69180/2010 PhD fellowships (FCT, Portugal), respectively. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript