Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease

• Published in: Scientific reports Vol. 11; no. 1; pp. 17237 - 9
• Main Authors: Delli Pizzi, Andrea, Chiarelli, Antonio Maria, Chiacchiaretta, Piero, Valdesi, Cristina, Croce, Pierpaolo, Mastrodicasa, Domenico, Villani, Michela, Trebeschi, Stefano, Serafini, Francesco Lorenzo, Rosa, Consuelo, Cocco, Giulio, Luberti, Riccardo, Conte, Sabrina, Mazzamurro, Lucia, Mereu, Manuela, Patea, Rosa Lucia, Panara, Valentina, Marinari, Stefano, Vecchiet, Jacopo, Caulo, Massimo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.08.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/250/255; 692/53/2423; 692/700/1421; 692/700/478; Aged; Aged, 80 and over; Computed tomography; Coronaviruses; COVID-19; COVID-19 - diagnosis; COVID-19 Nucleic Acid Testing; COVID-19 Testing - methods; Differential diagnosis; Female; Humanities and Social Sciences; Humans; Learning algorithms; Lung; Lung diseases; Machine Learning; Male; Middle Aged; multidisciplinary; Patients; Pneumonia; Polymerase chain reaction; Prediction models; Radiometry - methods; Radiomics; Retrospective Studies; ROC Curve; SARS-CoV-2 - physiology; Science; Science (multidisciplinary); Sensitivity analysis; Sensitivity and Specificity; Tomography, X-Ray Computed

Ground-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we...