Time-dependent personalized prognostic analysis by machine learning in biochemical recurrence after radical prostatectomy: a retrospective cohort study
Our prognostic model analyzed the time-dependent relationship between the timing of recurrence and prognostic factors. Our study achieved personalized prognosis analysis and its rationale after radical prostatectomy by employing machine learning prognostic model. This prognostic model contributes to the early detection of recurrence by enabling clinicians to conduct appropriate follow-ups for high-risk patients.
Authors
- Hiroki Shibata (ORCID: https://orcid.org/0000-0001-7623-7969)
- Shinichi Sakamoto (ORCID: https://orcid.org/0000-0002-0578-5121)
- Yusuke Imamura (ORCID: https://orcid.org/0009-0006-7207-1520)
- Yasutaka Yamada (ORCID: https://orcid.org/0000-0002-0070-1590)
- Nobuyoshi Takeuchi (ORCID: https://orcid.org/0000-0003-1761-2380)
- Yusuke Goto (ORCID: https://orcid.org/0000-0001-7720-9740)
- Kodai Sato (ORCID: https://orcid.org/0000-0001-6914-4991)
- Shinpei Saito (ORCID: https://orcid.org/0000-0002-9346-0553)
- Tomohiko Ichikawa
- Eiryo Kawakami
- Sazuka Tomokazu
Institutions
- Chiba University (JP)
- Chiba University Hospital (JP)
Publication Details
- Journal
- BMC Cancer
- Published
- 2024-11-25
- DOI
- https://doi.org/10.1186/s12885-024-13203-8
- Citations
- 3
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.26
Funders
- Chiba University
- Japan Science and Technology Agency
- Core Research for Evolutional Science and Technology