A machine learning-based analysis of nationwide cancer comprehensive genomic profiling data across cancer types to identify features associated with recommendation of genome-matched therapy
Several factors predicting the identification of genome-matched therapies through CGP were identified for the overall cancer population and cancer type-specific subpopulations. Expedited CGP is recommended for patients who match the identified profile to facilitate early targeted therapy.
Authors
- Katsutoshi Oda (ORCID: https://orcid.org/0000-0002-2468-9573)
- Hidenori Kage (ORCID: https://orcid.org/0000-0003-3658-4442)
- Kiyotaka Watanabe (ORCID: https://orcid.org/0000-0001-9332-8328)
- Aya Shinozaki‐Ushiku (ORCID: https://orcid.org/0000-0002-0196-5643)
- Hiroaki Ikushima (ORCID: https://orcid.org/0000-0002-7053-2841)
Institutions
- The University of Tokyo (JP)
Publication Details
- Journal
- ESMO Open
- Published
- 2024-11-25
- DOI
- https://doi.org/10.1016/j.esmoop.2024.103998
- Citations
- 6
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 1.71
Funders
- Cabinet Office, Government of Japan
- Japan Society for the Promotion of Science