Dataset Information

Automated prediction of emphysema visual score using homology-based quantification of low-attenuation lung region.

ABSTRACT: OBJECTIVE:The purpose of this study was to investigate the relationship between visual score of emphysema and homology-based emphysema quantification (HEQ) and evaluate whether visual score was accurately predicted by machine learning and HEQ. MATERIALS AND METHODS:A total of 115 anonymized computed tomography images from 39 patients were obtained from a public database. Emphysema quantification of these images was performed by measuring the percentage of low-attenuation lung area (LAA%). The following values related to HEQ were obtained: nb0 and nb1. LAA% and HEQ were calculated at various threshold levels ranging from -1000 HU to -700 HU. Spearman's correlation coefficients between emphysema quantification and visual score were calculated at the various threshold levels. Visual score was predicted by machine learning and emphysema quantification (LAA% or HEQ). Random Forest was used as a machine learning algorithm, and accuracy of prediction was evaluated by leave-one-patient-out cross validation. The difference in the accuracy was assessed using McNemar's test. RESULTS:The correlation coefficients between emphysema quantification and visual score were as follows: LAA% (-950 HU), 0.567; LAA% (-910 HU), 0.654; LAA% (-875 HU), 0.704; nb0 (-950 HU), 0.552; nb0 (-910 HU), 0.629; nb0 (-875 HU), 0.473; nb1 (-950 HU), 0.149; nb1 (-910 HU), 0.519; and nb1 (-875 HU), 0.716. The accuracy of prediction was as follows: LAA%, 55.7% and HEQ, 66.1%. The difference in accuracy was statistically significant (p = 0.0290). CONCLUSION:LAA% and HEQ at -875 HU showed a stronger correlation with visual score than those at -910 or -950 HU. HEQ was more useful than LAA% for predicting visual score.

SUBMITTER: Nishio M

PROVIDER: S-EPMC5444793 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Automated prediction of emphysema visual score using homology-based quantification of low-attenuation lung region.

Nishio Mizuho M Nakane Kazuaki K Kubo Takeshi T Yakami Masahiro M Emoto Yutaka Y Nishio Mari M Togashi Kaori K

PloS one 20170525 5

<h4>Objective</h4>The purpose of this study was to investigate the relationship between visual score of emphysema and homology-based emphysema quantification (HEQ) and evaluate whether visual score was accurately predicted by machine learning and HEQ.<h4>Materials and methods</h4>A total of 115 anonymized computed tomography images from 39 patients were obtained from a public database. Emphysema quantification of these images was performed by measuring the percentage of low-attenuation lung area ...[more]

PMID: 28542398

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Project description:BackgroundIn vivo high-resolution micro-computed tomography allows for longitudinal image-based measurements in animal models of lung disease. The combination of repetitive high resolution imaging with fully automated quantitative image analysis in mouse models of lung fibrosis lung benefits preclinical research. This study aimed to develop and validate such an automated micro-computed tomography analysis algorithm for quantification of aerated lung volume in mice; an indicator of pulmonary fibrosis and emphysema severity.MethodologyMice received an intratracheal instillation of bleomycin (n = 8), elastase (0.25 U elastase n = 9, 0.5 U elastase n = 8) or saline control (n = 6 for fibrosis, n = 5 for emphysema). A subset of mice was scanned without intervention, to evaluate potential radiation-induced toxicity (n = 4). Some bleomycin-instilled mice were treated with imatinib for proof of concept (n = 8). Mice were scanned weekly, until four weeks after induction, when they underwent pulmonary function testing, lung histology and collagen quantification. Aerated lung volumes were calculated with our automated algorithm.Principal findingsOur automated image-based aerated lung volume quantification method is reproducible with low intra-subject variability. Bleomycin-treated mice had significantly lower scan-derived aerated lung volumes, compared to controls. Aerated lung volume correlated with the histopathological fibrosis score and total lung collagen content. Inversely, a dose-dependent increase in lung volume was observed in elastase-treated mice. Serial scanning of individual mice is feasible and visualized dynamic disease progression. No radiation-induced toxicity was observed. Three-dimensional images provided critical topographical information.ConclusionsWe report on a high resolution in vivo micro-computed tomography image analysis algorithm that runs fully automated and allows quantification of aerated lung volume in mice. This method is reproducible with low inherent measurement variability. We show that it is a reliable quantitative tool to investigate experimental lung fibrosis and emphysema in mice. Its non-invasive nature has the unique benefit to allow dynamic 4D evaluation of disease processes and therapeutic interventions.

Project description:Rationale and objectivesEmphysema is characterized by lung tissue destruction and trapped gas. On computed tomographic (CT) images, this may be expressed by widespread areas with high specific gas volume (SV(g)). SV(g) is highly sensitive to very low attenuation values, which frequently occur in the CT images of patients with severe emphysema. The purpose of the present work was to study if and how different reconstruction settings and different scanners significantly influence SV(g) distribution, particularly in the very low attenuation range.Materials and methodsTwo sets of CT images taken from two different CT scanners at two different lung volumes in 10 healthy volunteers and 18 subjects with severe emphysema were analyzed. Images were reconstructed using two different settings of reconstruction parameters: (1) thin slice thickness and sharp filter and (2) thick slice thickness and smooth filter. For each set of images, average values of SV(g) and their variation (?SV(g)) from total lung capacity to residual volume were calculated in the whole lung.ResultsVery low attenuation values are always present in CT images when reconstructed with thin slice thickness and a sharp filter and in very large numbers in patients with severe emphysema. SV(g) values were in general significantly higher in patients with emphysema than in healthy subjects, at both total lung capacity and residual volume (P < .001), and were significantly influenced by the reconstruction filter (P < .001) and CT scanner (P < .001). ?SV(g) was lower in patients with emphysema than in healthy subjects (P < .001) and was significantly affected by the reconstruction setting but not by the CT scanner.ConclusionsThe disproportionate effect of low-attenuation pixels on SV(g) likely causes overestimation of the severity of emphysema and trapped gas. This can be significantly reduced, however, by using thick slices and a smooth filter for image reconstruction. ?SV(g) is generally robust for quantifying the functional impairment of the lung in severe emphysema.

Dataset Information

Automated prediction of emphysema visual score using homology-based quantification of low-attenuation lung region.

Publications

Automated prediction of emphysema visual score using homology-based quantification of low-attenuation lung region.

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