Machine Learning and Imaging Informatics in Oncology
Open accessAbstract
In the era of personalized and precision medicine, informatics technologies utilizing machine learning (ML) and quantitative imaging are witnessing a rapidly increasing role in medicine in general and in oncology in particular. This expanding role ranges from computer-aided diagnosis to decision support of treatments with the potential to transform the current landscape of cancer management. In this review, we aim to provide an overview of ML methodologies and imaging informatics techniques and their recent application in modern oncology. We will review example applications of ML in oncology from the literature, identify current challenges and highlight future potentials.
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Traditional machine learning models have supported tumor classification, survival prediction, and risk stratification using structured clinical or genomic data.
“traditional ML models, such as support vector machines (SVM), decision trees, and random forests, have supported tumor classification, survival prediction, and risk stratification using structured clinical or genomic data [6,12,13,14].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [14]
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References 60 · Cited by 64
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- Authors
- Tseng, H.-H., Wei, L., Cui, S., Luo, Y., Ten Haken, R. K., & El Naqa, I.
- Affiliations
- University of Michigan (US)
- Venue
- Oncology
- Publisher
- S. Karger AG
- Published
- 2018-11-23
- Volume
- 98
- Issue
- 6
- Pages
- 344-362
- ISSN
- 0030-2414 (ISSN-L)
- 1423-0232
- PMC
- PMC6533165
- OpenAlex
- W2901933545
- Cited by
- 64
- References
- 60
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 7 · 2023: 8 · 2022: 11 · 2021: 17 · 2020: 8 · 2019: 4
- FWCI
- 0.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Oncology (publishedVersion, cc-by-sa)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute (R37CA222215, R01CA233487, P01CA059827)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- machine learning, imaging informatics, computer-aided diagnosis, treatment decision support, quantitative imaging, precision medicine, cancer management
- MeSH terms
- Machine Learning, Animals, Humans, Medical Oncology, Neoplasms, Precision Medicine
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