2021/07/02 by Camilla Scapicchio, Michela Gabelloni, Andrea Barucci +3
Computer Science · Engineering · Medicine · #AI in cancer detection #Advanced X-ray and CT Imaging #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.1007/s11547-021-01389-x
crossref issued 2021/07/02 · crossref published 2021/07/02 · crossref published-online 2021/07/02 · openalex publication_date 2021/07/02 · crossref created 2021/07/02 · crossref published-print 2021/10/01 · crossref deposited 2024/09/03 · openalex created_date 2025/10/10 · crossref indexed 2026/07/27 · openalex updated_date 2026/08/01
Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.