@article {73, title = {Radiomics Digital Phantom}, year = {2016}, publisher = {CancerData}, abstract = {

The rise of radiomics, the high-throughput mining of quantitative image features from (standard-of-care) medical imaging for knowledge extraction and application within clinical decision support systems to improve diagnostic, prognostic, and predictive accuracy, has significant and substantial implications for the medical community. Radiomic analysis exploits sophisticated image analysis tools and the exponential growth of medical imaging data to develop and validate powerful image-based signatures/models for precision diagnosis and treatment in medicine.

This review describes the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making (presently primarily in the care of patients with cancer, however, all imaged patients may benefit from quantitative radiology).

Finally, the field of radiomics is emerging rapidly; however, the field lacks standardized evaluation of both the scientific integrity and the clinical significance of the numerous published radiomics investigations resulting from this growth. There is a clear and present need for rigorous evaluation criteria and reporting guidelines in order for radiomics to mature as a discipline. We therefore provide guidance together with a novel metric, the radiomics quality score (RQS) and an online digital phantom (DOI:10.17195/candat.2016.08.1), to meet this urgent need for both past and future investigations in the field of radiomics.\ \ \ 

}, keywords = {CT, phantom, quantitative features, Radiomics, standardization}, doi = {10.17195/candat.2016.08.1}, author = {Philippe Lambin} }