Creating strong predictive models in oncology
Many oncology predictive models fail to improve care. Issues include risks of bias, underpowered radiomics studies, and limited clinical impact. A path forward involves an emphasis on clinically actionable questions, rigor, and generalizability.
Authors
- Michael F. Gensheimer (ORCID: https://orcid.org/0000-0002-4897-3843)
Institutions
- Palo Alto University (US)
- Stanford University (US)
Publication Details
- Journal
- Patterns
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1016/j.patter.2026.101492
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00