Radiotherapy for primary bone tumors: current techniques and integration of artificial intelligence—a review - Tong, Jian; Chen, Daoyu; Li, Jin; Chen, Haobo; Yu, Tao (Frontiers in Oncology, 2025)
Primary bone tumours remain among the most challenging indications in radiation oncology—not because of anatomical size or distribution, but because curative intent demands ablative dosing alongside stringent normal−tissue preservation. Over the past decade, the therapeutic landscape has shifted markedly. Proton and carbon−ion centres now report durable local control with acceptable late toxicity in unresectable sarcomas. MR−guided linear accelerators enable on−table anatomical visualisation and daily adaptation, permitting margin reduction without prolonging workflow. Emerging ultra−high−dose−rate (FLASH) strategies may further spare healthy bone marrow while preserving tumour lethality; first−in−human studies are underway. Beyond hardware, artificial−intelligence pipelines accelerate contouring, automate plan optimisation, and integrate multi−omics signatures with longitudinal imaging to refine risk stratification in real time. Equally important, privacy−preserving federated learning consortia are beginning to pool sparse datasets across institutions, addressing chronic statistical under−power in rare tumours. Appreciating these convergent innovations is essential for clinicians deciding when and how to escalate dose, for physicists designing adaptive protocols, and for investigators planning the next generation of biology−driven trials. This narrative review synthesises recent technical and translational advances and outlines practical considerations, evidence gaps, and research priorities on the path to truly individualised, data−intelligent radiotherapy for primary bone tumours.