| Literature DB >> 31538516 |
Ulrike Schick1,2,3, François Lucia1,2, Gurvan Dissaux1,2,3, Dimitris Visvikis2, Bogdan Badic2,4, Ingrid Masson2, Olivier Pradier1,2,3, Vincent Bourbonne1,2, Mathieu Hatt2.
Abstract
Personalized medicine aims at offering optimized treatment options and improved survival for cancer patients based on individual variability. The success of precision medicine depends on robust biomarkers. Recently, the requirement for improved non-biologic biomarkers that reflect tumor biology has emerged and there has been a growing interest in the automatic extraction of quantitative features from medical images, denoted as radiomics. Radiomics as a methodological approach can be applied to any image and most studies have focused on PET, CT, ultrasound, and MRI. Here, we aim to present an overview of the radiomics workflow as well as the major challenges with special emphasis on the use of multiparametric MRI datasets. We then reviewed recent studies on radiomics in the field of pelvic oncology including prostate, cervical, and colorectal cancer.Entities:
Mesh:
Year: 2019 PMID: 31538516 DOI: 10.1259/bjr.20190105
Source DB: PubMed Journal: Br J Radiol ISSN: 0007-1285 Impact factor: 3.039