title: The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review.
abstract: OBJECTIVE
  Advances in genomics have led to the recognition that tumors are populated by distinct genotypic subgroups that drive tumor development and progression. The spatial and temporal heterogeneity of solid tumors has been a critical barrier to the development of precision medicine approaches because the standard approach to tumor sampling, often invasive needle biopsy, is unable to fully capture the spatial state of the tumor. Image-based phenotyping, which represents quantification of the tumor phenotype through medical imaging, is a promising development for precision medicine.
  
  
  METHODS
  Medical imaging can provide a comprehensive macroscopic picture of the tumor phenotype and its environment that is ideally suited to quantifying the development of the tumor phenotype before, during, and after treatment. As a noninvasive technique, medical imaging can be performed at low risk and inconvenience to the patient. The semantic features approach to tumor phenotyping, accomplished by visual assessment of radiologists, is compared with a computational radiomic approach that relies on automated processing of imaging assays. Together, these approaches capture important information for diagnostic, prognostic, and predictive purposes.
  
  
  CONCLUSIONS
  Although imaging technology is already embedded in clinical practice for diagnosis, staging, treatment planning, and response assessment, the transition of these computational methods to the clinic has been surprisingly slow. This review outlines the promise of these novel technologies for precision medicine and the obstacles to clinical application.
authors.affiliation: Departments of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
authors.forename: Hugo J W L
authors.initials: HJ
authors.lastname: Aerts
mesh_terms: D003952:Diagnostic Imaging; D023281:Genomics; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000235:genetics* / Q000473:pathology; D009683:Nuclear Medicine / Q000379:methods; D010641:Phenotype; D057285:Precision Medicine; D011379:Prognosis; D014057:Tomography, X-Ray Computed; D059016:Tumor Microenvironment / Q000235:genetics