Predictive model of castration resistance in advanced prostate cancer by machine learning using genetic and clinical data: KYUCOG-1401-A study
The results demonstrated that the SNPs models created by ML produced excellent prediction of castration resistance and prognosis in primary ADT for advanced prostate cancer, and will be helpful in treatment choice.
Authors
- Masatoshi Eto (ORCID: https://orcid.org/0000-0002-3312-9930)
- Toshiyuki Kamoto (ORCID: https://orcid.org/0000-0002-7728-289X)
- Masaki Shiota (ORCID: https://orcid.org/0000-0002-3306-4858)
- Naohiro Fujimoto (ORCID: https://orcid.org/0000-0002-5256-6415)
- Shota Nemoto (ORCID: https://orcid.org/0009-0004-3301-6807)
- Shuichi Tatarano (ORCID: https://orcid.org/0000-0003-4224-5084)
- Akira Yokomizo (ORCID: https://orcid.org/0000-0002-5741-6280)
- Ryo Ikegami (ORCID: https://orcid.org/0000-0002-8773-8790)
- Keita Kobayashi (ORCID: https://orcid.org/0000-0001-9279-3156)
- Tomomi Kamba (ORCID: https://orcid.org/0000-0002-5255-6479)
- Seiji Naito (ORCID: https://orcid.org/0000-0003-4272-9724)
- Tsukasa Igawa (ORCID: https://orcid.org/0000-0002-4359-4473)
- Hideki Sakai
Institutions
- Kurume University (JP)
- Kagoshima University (JP)
- University of Miyazaki (JP)
- Kyushu University (JP)
- Yamaguchi University (JP)
- University of Occupational and Environmental Health Japan (JP)
- Harasanshin Hospital (JP)
- Nagasaki University (JP)
- Hitachi (Japan) (JP)
- Kumamoto University (JP)
Publication Details
- Journal
- BJC Reports
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1038/s44276-024-00093-3
- Citations
- 3
- Primary Topic
- Prostate Cancer Treatment and Research
- Type
- article
- Field-Weighted Citation Impact
- 1.26
Funders
- Astellas Pharma