Year: 2022
Type: review
Abstract: Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.
DOI: 10.2214/ajr.22.27695
Institutions: University Health Network, University of Toronto, Bridgeport Hospital, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, University of Alabama at Birmingham, University of Arizona, Louisiana State University Health Sciences Center New Orleans, National Institutes of Health, National Cancer Institute, The University of Texas MD Anderson Cancer Center, Mercy Catholic Medical Center, Memorial Sloan Kettering Cancer Center
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: National Cancer Institute
Award: Cancer Center Support Grant
OpenAlex ID: W4283716728
