Machine Learning in Radiation Oncology

Abstract

Machine learning (ML) is transforming radiation oncology by enhancing precision, efficiency, and personalization. Key applications include: (1) Treatment planning—U-Net and nnU-Net achieve >85% Dice scores for tumor segmentation, while GANs optimize dose distribution; (2) Predictive modeling—Radiomics and genomics predict treatment response/toxicity, though data heterogeneity challenges reproducibility; (3) Personalized therapy—Biomarker-driven adaptation and digital twins enable dynamic adjustments. Challenges include data scarcity, model validation, and interdisciplinary collaboration, with federated learning proposed for privacy-preserving data sharing. ML automates workflows, improves outcomes, and addresses resource disparities, but requires standardized protocols, robust validation, and ethical frameworks for equitable adoption. Future directions include federated learning advances, multi-center trials, and prospective studies to translate research into practice.

Ähnliche Arbeiten