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Biological domain shift and statistical nesting concerns in generative AI-based spatial tumor growth prediction for
Wang Li1, Xiaohu Sun1, Sining Pei2
1Department of Radiotherapy, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, No.44 Xiaoheyan Road, Dadong District, Shenyang, 110042, Liaoning Province, China.
BMC Medicine
|August 13, 2026
Summary
Generative AI shows promise for pediatric glioma growth prediction, but methodological improvements are needed for reliable radiotherapy planning. Further validation is crucial for clinical translation of these AI models.
Area of Science:
- Artificial intelligence in oncology
- Medical imaging analysis
- Pediatric neuro-oncology
Background:
- Laslo et al. developed a generative AI model for pediatric diffuse midline glioma growth prediction using MRI.
- The model aims to aid personalized radiotherapy planning in data-limited settings.
- However, the study had limitations including a small validation cohort and weaker performance in growth-region prediction.
