Dataset Information

Robust and Reproducible Quantification of the Extent of Chest Radiographic Abnormalities (And It's Free!).

ABSTRACT:

Rationale

Objective, reproducible quantification of the extent of abnormalities seen on a chest radiograph would improve the user-friendliness of a previously proposed severity scoring system for pulmonary tuberculosis and could be helpful in monitoring response to therapy, including in clinical trials.

Methods

In this study we report the development and evaluation of a simple tool using free image editing software (GIMP) to accurately and reproducibly quantify the area of affected lung on the chest radiograph of tuberculosis patients. As part of a pharmacokinetic study in Lima, Peru, a chest radiograph was performed on patients with pulmonary tuberculosis and this was subsequently photographed using a digital camera. The GIMP software was used by two independent and trained readers to estimate the extent of affected lung (expressed as a percentage of total lung area) in each radiograph and the resulting radiographic SCORE.

Results

56 chest radiographs were included in the reading analysis. The Intraclass correlation coefficient (ICC) between the 2 observers was 0.977 (p<0.001) for the area of lung affected and was 0.955 (p<0.001) for the final score; and the kappa coefficient of Interobserver agreement for both the area of lung affected and the score were 0.9 (p<0.001) and 0.86 (p<0.001) respectively.

Conclusions

This high level of between-observer agreement suggests that this freely available software could constitute a simple and useful tool for robust evaluation of individual and serial chest radiographs.

SUBMITTER: Requena-Mendez A

PROVIDER: S-EPMC4440724 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Robust and Reproducible Quantification of the Extent of Chest Radiographic Abnormalities (And It's Free!).

Requena-Méndez Ana A Aldasoro Edelweiss E Muñoz Jose J Moore David A J DA

PloS one 20150521 5

<h4>Rationale</h4>Objective, reproducible quantification of the extent of abnormalities seen on a chest radiograph would improve the user-friendliness of a previously proposed severity scoring system for pulmonary tuberculosis and could be helpful in monitoring response to therapy, including in clinical trials.<h4>Methods</h4>In this study we report the development and evaluation of a simple tool using free image editing software (GIMP) to accurately and reproducibly quantify the area of affecte ...[more]

PMID: 25996917

Similar Datasets

Project description:PurposeTo study the extent of pulmonary involvement in coronavirus 19 (COVID-19) with quantitative CT and to assess the impact of disease burden on opacity visibility on chest radiographs.Materials and methodsThis retrospective study included 20 pairs of CT scans and same-day chest radiographs from 17 patients with COVID-19, along with 20 chest radiographs of controls. All pulmonary opacities were semiautomatically segmented on CT images, producing an anteroposterior projection image to match the corresponding frontal chest radiograph. The quantitative CT lung opacification mass (QCTmass) was defined as (opacity attenuation value + 1000 HU)/1000 × 1.065 (g/mL) × combined volume (cm3) of the individual opacities. Eight thoracic radiologists reviewed the 40 radiographs, and a receiver operating characteristic curve analysis was performed for the detection of lung opacities. Logistic regression analysis was performed to identify factors affecting opacity visibility on chest radiographs.ResultsThe mean QCTmass per patient was 72.4 g ± 120.8 (range, 0.7-420.7 g), and opacities occupied 3.2% ± 5.8 (range, 0.1%-19.8%) and 13.9% ± 18.0 (range, 0.5%-57.8%) of the lung area on the CT images and projected images, respectively. The radiographs had a median sensitivity of 25% and specificity of 90% among radiologists. Nineteen of 186 opacities were visible on chest radiographs, and a median area of 55.8% of the projected images was identifiable on radiographs. Logistic regression analysis showed that QCTmass (P < .001) and combined opacity volume (P < .001) significantly affected opacity visibility on radiographs.ConclusionQCTmass varied among patients with COVID-19. Chest radiographs had high specificity for detecting lung opacities in COVID-19 but a low sensitivity. QCTmass and combined opacity volume were significant determinants of opacity visibility on radiographs.Earlier incorrect version appeared online. This article was corrected on April 6, 2020 and December 14, 2020.Supplemental material is available for this article.© RSNA, 2020.

Dataset Information

Robust and Reproducible Quantification of the Extent of Chest Radiographic Abnormalities (And It's Free!).

Rationale

Methods

Results

Conclusions

Publications

Robust and Reproducible Quantification of the Extent of Chest Radiographic Abnormalities (And It's Free!).

Similar Datasets

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