ArticleMedicine2014
Objective CT-based quantification of lung sequelae in treated patients with paracoccidioidomycosis.
Article in Medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Quantification of Pulmonary Inflammatory Processes Using Chest Radiography: Tuberculosis as the Motivating Application.Medicine · 2015Trial
- Diagnosis and Treatment of Pulmonary Coccidioidomycosis and Paracoccidioidomycosis.Journal of fungi (Basel, Switzerland) · 2023Review
- A scoping study of pulmonary paracoccidioidomycosis: severity classification based on radiographic and tomographic evaluation.The journal of venomous animals and toxins including tropical diseases · 2022Review
- Article
- Inflammatory lesions and brain tumors: is it possible to differentiate them based on texture features in magnetic resonance imaging?The journal of venomous animals and toxins including tropical diseases · 2020Article
- The case for paracoccidioidomycosis to be accepted as a neglected tropical (fungal) disease.PLoS neglected tropical diseases · 2019Article
- Computed tomography-based volumetric tool for standardized measurement of the maxillary sinus.PloS one · 2018Article
- Paracoccidioidomycosis: level of pulmonary sequelae in high resolution computed tomography images from patients of two endemic regions of Brazil.Quantitative imaging in medicine and surgery · 2017Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents methodology for objectively quantifying the pulmonary region affected by emphysemic and fibrotic sequelae in treated patients with paracoccidioidomycosis. This methodology may also be applied to any other disease that results in these sequelae in the lungs.Pulmonary high-resolution computed tomography examinations of 30 treated paracoccidioidomycosis patients were used in the study. The distribution of voxel attenuation coefficients was analyzed to determine the percentage of lung volume that consisted of emphysemic, fibrotic, and normal tissue. Algorithm outputs were compared with subjective evaluations by radiologists using a scale that is currently used for clinical diagnosis.Affected regions in the patient images were determined by computational analysis and compared with estimates by radiologists, revealing mean (± standard deviation) differences in the scores for fibrotic and emphysemic regions of 0.1% ± 1.2% and -0.2% ± 1.0%, respectively.The computational results showed a strong correlation with the radiologist estimates, but the computation results were more reproducible, objective, and reliable.
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