Novel Chest Radiographic Biomarkers for COVID-19 Using Radiomic Features Associated with Diagnostics and Outcomes

• Published in: Journal of digital imaging 2021-04, Vol.34 (2), p.297-307
• Main Authors: Ferreira Junior, José Raniery, Cardona Cardenas, Diego Armando, Moreno, Ramon Alfredo, de Sá Rebelo, Marina de Fátima, Krieger, José Eduardo, Gutierrez, Marco Antonio
• Format: Article
• Language: English
• Subjects: Biomarkers; Chest; Coronaviruses; COVID-19; Etiology; Histograms; Image segmentation; Imaging; Infectious diseases; Medicine; Medicine & Public Health; Nonuniformity; Pneumonia; Radiology; Radiomics; Rank tests; Respiratory diseases; Statistical analysis; Survival; Viral diseases; Wavelet transforms
• ISSN: 0897-1889; 1618-727X
• DOI: 10.1007/s10278-021-00421-w

COVID-19 is a highly contagious disease that can cause severe pneumonia. Patients with pneumonia undergo chest X-rays (XR) to assess infiltrates that identify the infection. However, the radiographic characteristics of COVID-19 are similar to the other acute respiratory syndromes, hindering the imaging diagnosis. In this work, we proposed identifying quantitative/radiomic biomarkers for COVID-19 to support XR assessment of acute respiratory diseases. This retrospective study used different cohorts of 227 patients diagnosed with pneumonia; 49 of them had COVID-19. Automatically segmented images were characterized by 558 quantitative features, including gray-level histogram and matrices of co-occurrence, run-length, size zone, dependence, and neighboring gray-tone difference. Higher-order features were also calculated after applying square and wavelet transforms. Mann–Whitney U test assessed the diagnostic performance of the features, and the log-rank test assessed the prognostic value to predict Kaplan–Meier curves of overall and deterioration-free survival. Statistical analysis identified 51 independently validated radiomic features associated with COVID-19. Most of them were wavelet-transformed features; the highest performance was the small dependence matrix feature of “low gray-level emphasis” (area under the curve of 0.87, sensitivity of 0.85, p < 0.001 ). Six features presented short-term prognostic value to predict overall and deterioration-free survival. The features of histogram “mean absolute deviation” and size zone matrix “non-uniformity” yielded the highest differences on Kaplan–Meier curves with a hazard ratio of 3.20 ( p < 0.05 ). The radiomic markers showed potential as quantitative measures correlated with the etiologic agent of acute infectious diseases and to stratify short-term risk of COVID-19 patients.