TY - JOUR
T1 - Radiomics
T2 - the bridge between medical imaging and personalized medicine
AU - Lambin, Philippe
AU - Leijenaar, Ralph T. H.
AU - Deist, Timo M.
AU - Peerlings, Jurgen
AU - de Jong, Evelyn E. C.
AU - van Timmeren, Janita
AU - Sanduleanu, Sebastian
AU - Larue, Ruben T. H. M.
AU - Even, Aniek J. G.
AU - Jochems, Arthur
AU - van Wijk, Yvonka
AU - Woodruff, Henry
AU - van Soest, Johan
AU - Lustberg, Tim
AU - Roelofs, Erik
AU - van Elmpt, Wouter
AU - Dekker, Andre
AU - Mottaghy, Felix M.
AU - Wildberger, Joachim E.
AU - Walsh, Sean
PY - 2017/12
Y1 - 2017/12
N2 - Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
AB - Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
KW - CELL LUNG-CANCER
KW - LEARNING HEALTH-CARE
KW - DECISION-SUPPORT-SYSTEMS
KW - BODY RADIATION-THERAPY
KW - GENE-EXPRESSION
KW - BREAST-CANCER
KW - INTRINSIC RADIOSENSITIVITY
KW - F-18-FDG PET
KW - RADIOTHERAPY RESEARCH
KW - PROGNOSTIC-FACTOR
U2 - 10.1038/nrclinonc.2017.141
DO - 10.1038/nrclinonc.2017.141
M3 - (Systematic) Review article
SN - 1759-4774
VL - 14
SP - 749
EP - 762
JO - Nature Reviews Clinical Oncology
JF - Nature Reviews Clinical Oncology
IS - 12
ER -