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(a) principal: Alves, Allan Felipe Fattori [UNESP]
• Data de Publicação: 2021
• Outros Autores: Miranda, José Ricardo Arruda [UNESP], Reis, Fabiano, Oliveira, Abner Alves [UNESP], Souza, Sérgio Augusto Santana [UNESP], Fortaleza, Carlos Magno Castelo Branco [UNESP], Tanni, Suzana Erico [UNESP], Castro, José Thiago Souza, Pina, Diana Rodrigues [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1371/journal.pone.0251783; http://hdl.handle.net/11449/228960

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.