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Creating strong predictive models in oncology
1Stanford University School of Medicine, Palo Alto, CA 94304, USA.
Patterns (New York, N.Y.)
|February 23, 2026
Summary
Many oncology predictive models do not improve patient care due to bias and weak studies. Future models need clear clinical questions, strong methods, and broad applicability for better cancer treatment outcomes.
Area of Science:
- Oncology
- Medical Imaging
- Biostatistics
Background:
- Predictive models in oncology often fail to translate into improved patient care.
- Key challenges include inherent biases, insufficient statistical power in radiomics research, and a lack of demonstrable clinical utility.
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