Personalized prognostic model for colorectal cancer in the era of precision medicine: a dynamic approach based on real-world data
The Joint model may accurately predict personalized prognosis that reflects changes in longitudinal tumor marker values.
Authors
- Keita Fukuyama (ORCID: https://orcid.org/0000-0001-8176-6961)
- Hiroto Nishino (ORCID: https://orcid.org/0000-0002-7377-9997)
- Norihiro Shimoike (ORCID: https://orcid.org/0000-0002-4547-2293)
- Takamichi Ishii (ORCID: https://orcid.org/0000-0002-7461-9653)
- Satoshi Ogiso (ORCID: https://orcid.org/0000-0002-9577-330X)
- Manabu Muto (ORCID: https://orcid.org/0000-0002-3127-8203)
- Takahiro Nishio (ORCID: https://orcid.org/0000-0002-9676-7235)
- Koya Hida (ORCID: https://orcid.org/0000-0001-7210-7075)
- Kazutaka Obama (ORCID: https://orcid.org/0000-0003-2924-6701)
- Shigemi Matsumoto (ORCID: https://orcid.org/0000-0001-6453-7489)
- Etsuro Hatano (ORCID: https://orcid.org/0000-0003-3407-1918)
- Keisuke Okura (ORCID: https://orcid.org/0000-0002-6891-9713)
- Satoru Seo (ORCID: https://orcid.org/0000-0001-9539-8098)
- Tomoaki Yoh (ORCID: https://orcid.org/0000-0001-6518-2077)
- Satoshi Morita (ORCID: https://orcid.org/0000-0002-4344-3317)
- Yukinori Koyama (ORCID: https://orcid.org/0000-0001-5174-3662)
Institutions
- Kyoto University (JP)
- Kyoto University Hospital (JP)
- Kōchi University (JP)
Publication Details
- Journal
- International Journal of Clinical Oncology
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1007/s10147-025-02766-6
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08