From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.
Open accessAbstract
Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.
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ML and AI techniques have been applied to predict cancer prognosis and estimate treatment response based on genomic and clinical data.
“These techniques have been applied to predict cancer prognosis and estimate treatment response based on genomic and clinical data [8–14].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [12]
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- Authors
- Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A., & Zou, J.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Corresponding author
- Zou, James
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2023-04
- Volume
- 186
- Issue
- 8
- Pages
- 1772-1791
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W4323865263
- Cited by
- 597
- References
- 21
- Citations by year
- 2026: 143 · 2025: 256 · 2024: 152 · 2023: 45 · 2020: 1
- FWCI
- 12.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 417 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (F30HL156478)
- 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 in healthcare, cancer diagnosis, cancer prognosis, treatment planning, diagnostic imaging, liquid biopsy, molecular data, clinical implementation of AI, clinical workflow
- MeSH terms
- Machine Learning, Diagnostic Imaging, Humans, Medical Oncology, Neoplasms
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