Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients

Autor(es)

Fattori Alves, Allan Felipe ^[1] ; Arruda Miranda, Jose Ricardo ^[2] ; Reis, Fabiano ^[3] ; Oliveira, Abner Alves ^[2] ; Santana Souza, Sergio Augusto ^[2] ; Castelo Branco Fortaleza, Carlos Magno ^[4] ; Tanni, Suzana Erico ^[4] ; Souza Castro, Jose Thiago ^[3] ; Pina, Diana Rodrigues ^[4]

Afiliação do(s) autor(es)

^[1] Botucatu Med Sch, Clin Hosp, Med Phys & Radioprotect Nucleus, Botucatu, SP - Brazil

^[2] Sao Paulo State Univ Julio de Mesquita Filho, Inst Biosci, Botucatu, SP - Brazil

^[3] Univ Estadual Campinas, Radiol & Med Imaging, Campinas, SP - Brazil

^[4] Sao Paulo State Univ Julio De Mesquita Filho, Med Sch, Botucatu, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PLoS One; v. 16, n. 6 JUN 10 2021.
• Citações Web of Science: 0

Resumo

In this work, we aimed to develop an automatic algorithm for the quantification of total volume and lung impairments in four different diseases. The quantification was completely automatic based upon high resolution computed tomography exams. The algorithm was capable of measuring volume and differentiating pulmonary involvement including inflammatory process and fibrosis, emphysema, and ground-glass opacities. The algorithm classifies the percentage of each pulmonary involvement when compared to the entire lung volume. Our algorithm was applied to four different patients groups: no lung disease patients, patients diagnosed with SARS-CoV-2, patients with chronic obstructive pulmonary disease, and patients with paracoccidioidomycosis. The quantification results were compared with a semi-automatic algorithm previously validated. Results confirmed that the automatic approach has a good agreement with the semi-automatic. Bland-Altman (B\&A) demonstrated a low dispersion when comparing total lung volume, and also when comparing each lung impairment individually. Linear regression adjustment achieved an R value of 0.81 when comparing total lung volume between both methods. Our approach provides a reliable quantification process for physicians, thus impairments measurements contributes to support prognostic decisions in important lung diseases including the infection of SARS-CoV-2. (AU)

• Processo FAPESP: 20/05539-9 - Quantificações de estruturas pulmonares radiológica da COVID-19 (infecção por Coronavirus)
• Beneficiário: Diana Rodrigues de Pina Miranda
• Modalidade de apoio: Auxílio à Pesquisa - Regular