Computational cost of CT Radiomics workflow: a case study on COVID-19

Abstract

Background and objective: The images produced by radiological exams are in DICOM format, the medical imaging standard that includes a large amount of information about the exam and the patient. In a Computed Tomography exam there can be more than 300 DICOM images per patient: this means that Radiomics could addresses the problem of being time and resource-consuming. The research question is: reducing images can lead to improved diagnostic performance?Methods: The study was focused on the classification of healthy, COVID-19 and Lung disease patients. For each patient, a few central images from the Computed Tomography exam are selected, where it is assumed that there is the greatest likelihood of seeing the disease marks. Consequently, this study provides the use of formal and mathematical techniques in order to obtain an early and robust automated diagnosis of COVID-19. In the first phase, the classification is focused on healthy patients, while for COVID-19 patients there is a second phase. The verification will be done on the entire CT examination and then on the CT examination with central selected slices.Results: Results on the original exam are no different from those on the reduced exam on the first phase, and this could an advantage due to the robustness of Formal Methods. In the second phase of the study, almost all indexes become underperforming.Conclusions: A reduced quantity of medical images can be an advantage for medical doctors, but it is not beneficial for diagnosis, as performance remains unchanged or worsens. Indeed, it can be said that disease-relevant information is also taken in the first or last parts of the radiological exam, leading to the claim that Radiomics "sees what the human eye does not see".

Ähnliche Arbeiten