Radiomics in COVID-19: The Time for (R)evolution Has Came

Abstract

The pandemic caused by the new coronavirus in 2019, now called SARS-CoV-2 or COVID-19 disease, has become a major public health problem worldwide. The main method of diagnosing SARS-CoV-2 infection is RT-PCR, but medical imaging brings important quantitative and qualitative information that complements the data for diagnosis and prediction of the clinical course of the disease, even if chest X-rays and CT scans are not routinely recommended for screening and diagnosis of COVID-19 infections. Identifying characteristics of medical images, such as GGO, crazy paving, and consolidation as those of COVID-19 can guide the diagnosis, and can help clinicians in decisions in patient treatment if an RT-PCR result is not available rapidly. Chest radiographs and CT also bring information about the severity and unfavorable evolution potential of the disease. Radiomics, a new research subdomain of A.I. based on the extraction and analysis of shape and texture characteristics from medical images, along with deep learning, another A.I. method that uses neural networks, can offer new horizons in the development of models with diagnostic and predictive value for COVID-19 disease management. Standardizing the methods and creating multivariable models that include etiological, biological, and clinical data may increase the value and impact of using radiomics in routine COVID-19 evaluation. Recently, proposed complex models that may include radiological features or clinical variables have appeared to add value to the accuracy of CT diagnosis by radiomix and are likely to underlie the routine use of radiomic in COVID-19 management.