The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review
Abstract
IMPORTANCE: 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. OBSERVATIONS: 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 AND RELEVANCE: 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.
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CAD tools were initially developed to aid interpretation of radiological findings and early identification of diseases, especially breast and lung cancer.
“Tools of CAD were initially developed to aid in the interpretation of radiological findings and early identification of diseases, especially breast and lung cancer(7,10).”
From Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine (2019) · cites this paper as [10]
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Interventional Radiology Ex-Machina: Impact of Artificial Intelligence on Practice
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- Authors
- Aerts, H. J. W. L.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Aerts, Hugo J. W. L.
- ORCID
- Aerts, Hugo J. W. L.
- Venue
- JAMA Oncology
- Publisher
- American Medical Association (AMA)
- Published
- 2016-12-01
- Volume
- 2
- Issue
- 12
- Pages
- 1636
- ISSN
- 2374-2437 (ISSN-L)
- OpenAlex
- W2517065464
- Cited by
- 669
- References
- 72
- Citations by year
- 2026: 43 · 2025: 66 · 2024: 76 · 2023: 98 · 2022: 86 · 2021: 94 · 2020: 74 · 2019: 57 · 2018: 46 · 2017: 24 · 2016: 4 · 2015: 1
- FWCI
- 37.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA Oncology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- radiomics, tumor phenotyping, precision medicine, solid tumors, tumor heterogeneity, diagnostic imaging, semantic features, tumor subtypes, noninvasive tumor assessment, clinical translation
- MeSH terms
- Diagnostic Imaging, Humans, Image Processing, Computer-Assisted, Neoplasms, Nuclear Medicine, Phenotype, Prognosis, Tomography, X-Ray Computed, Genomics, Precision Medicine, Tumor Microenvironment
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