A lung graph model for the radiological assessment of chronic thromboembolic pulmonary hypertension in CT. (October 2020)

• Record Type: Journal Article
• Authors: Jimenez-del-Toro, Oscar; Dicente Cid, Yashin; Platon, Alexandra; Hachulla, Anne-Lise; Lador, Frédéric; Poletti, Pierre-Alexandre; Müller, Henning
• Is Part Of: Computers in biology and medicine. Volume 125(2020)
• Publication Date: 2020-10
• Subjects: Pulmonary hypertension -- Texture analysis -- Pulmonary embolism -- Lung graph model -- Radiomics; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103962
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Chronic thromboembolic pulmonary hypertension (CTEPH) is a possible complication of pulmonary embolism (PE), with poor prognosis if left untreated. Surgical curative treatment is available, particularly in the early stages of the disease. However, most cases are not diagnosed until specific symptoms become evident. A small number of computed tomography (CT) findings, such as a widened pulmonary artery and mosaicism in the lung parenchyma, have been correlated with pulmonary hypertension (PH). Quantitative texture analysis in the CT scans of these patients could provide complementary sub-visual information of the vascular changes taking place in the lungs. For this task, a lung graph model was developed with texture descriptors from 37 CT scans with confirmed CTEPH diagnosis and 48 CT scans from PE patients who did not develop PH. The probability of presenting CTEPH, computed with the graph model, outperformed a convolutional neural network approach using 10 different train/test splits of the data set. An accuracy of 0.76 was obtained with the proposed texture analysis, and was then compared to the visual assessment of CT findings, manually identified by a team of three expert radiologists, commonly associated with pulmonary hypertension. This graph-based score combined with the information attained from the radiological findings resulted in a Cohen's kappa coefficient of 0.47 when differentiating patients with confirmed CTEPH from those with PE who did not develop the disease. The proposed texture quantification could be an objective measurement, complementary to the current analysis of radiologists for the early detection of CTEPH and thus improve patient outcome. Graphical abstract: Highlights: Lung graph model detects chronic thromboembolic pulmonary hypertension (CTEPH) in CT. Model measures local 3D texture to perform a global analysis of the lung parenchyma. Differentiates CTEPH from pulmonary embolism in 85 manually annotated CT scans. Lung graph had a better performance than a state-of-the-art deep learning approach. Obtained CTEPH probability was compared and combined with radiological CT findings.