Next Article in Journal
Interoperability of COVID-19 Clinical Phenotype Data with Host and Viral Genetics Data
Previous Article in Journal
Cognitive Function and Neuropsychiatric Disorders after COVID-19: A Long Term Social and Clinical Problem?
 
 
Review

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

1
Oral Pathology Department, Faculty of Dentistry, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iaşi, Romania
2
Clinical Laboratory Department, “St. Spiridon” Emergency Hospital, 700111 Iaşi, Romania
3
Radiation Oncology Department, Regional Institute of Oncology, 700483 Iaşi, Romania
4
Faculty of Physics, “Alexandru Ioan Cuza” University, 700506 Iaşi, Romania
5
Oncology and Radiotherapy Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
6
Surgery Department, Railways Clinical Hospital, 700506 Iaşi, Romania
7
Oncology and Radiotherapy Department, Faculty of Medicine, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iaşi, Romania
*
Author to whom correspondence should be addressed.
Academic Editor: Marco Rengo
BioMed 2022, 2(1), 60-68; https://doi.org/10.3390/biomed2010006
Received: 27 December 2021 / Revised: 15 January 2022 / Accepted: 18 January 2022 / Published: 24 January 2022
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. View Full-Text
Keywords: radiomics; COVID-19; SARS-CoV-2; deep learning; imagistics; CT radiomics; COVID-19; SARS-CoV-2; deep learning; imagistics; CT
MDPI and ACS Style

Iancu, R.I.; Zară, A.D.; Mireștean, C.C.; Iancu, D.P.T. Radiomics in COVID-19: The Time for (R)evolution Has Came. BioMed 2022, 2, 60-68. https://doi.org/10.3390/biomed2010006

AMA Style

Iancu RI, Zară AD, Mireștean CC, Iancu DPT. Radiomics in COVID-19: The Time for (R)evolution Has Came. BioMed. 2022; 2(1):60-68. https://doi.org/10.3390/biomed2010006

Chicago/Turabian Style

Iancu, Roxana Irina, Alexandru Dumitru Zară, Camil Ciprian Mireștean, and Dragoș Petru Teodor Iancu. 2022. "Radiomics in COVID-19: The Time for (R)evolution Has Came" BioMed 2, no. 1: 60-68. https://doi.org/10.3390/biomed2010006

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop