Modeling Individual-Level Uncertainty From Missing Data in Multifactorial Breast Cancer Risk Prediction
Our results demonstrate that, in some cases, there is considerable probability of reclassification after collecting missing data. Methodology presented here can identify situations where it would be most beneficial to collect additional information, to enable better informed clinical decision making.
Authors
- P Hall
- Lorenzo Ficorella (ORCID: https://orcid.org/0000-0002-0577-1571)
- Mikael Eriksson (ORCID: https://orcid.org/0000-0003-4013-1446)
- Juliet Usher-Smith
- Bethan White
- Marc Tischkowitz
- K Czene
- X Yang
- A Antoniou
- D Easton
- Stephanie Archer
Institutions
- University of Cambridge (GB)
- Karolinska Institutet (SE)
- National Institute for Health and Care Research (GB)
- Cancer Research UK Cambridge Center (GB)
- Stockholm South General Hospital (SE)
Publication Details
- Journal
- JCO Precision Oncology
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1200/po-25-00852
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00