Your new experience awaits. Try the new design now and help us make it even better

MINI REVIEW article

Front. Oncol.

Sec. Radiation Oncology

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1648849

This article is part of the Research TopicAI-Based Prognosis Prediction and Dose Optimization Strategy in Radiotherapy for Malignant TumorsView all 12 articles

Radiother apy for Pr imar y Bone Tumor s: Cur r ent Techniques and Integr ation of Ar tificial Intelligence-A Review

Provisionally accepted
Jian  TongJian Tong*Daoyu  ChenDaoyu ChenJin  LiJin LiHaobo  ChenHaobo ChenTao  YuTao Yu
  • Department of Spinal Surgery, Number One Orthopedics Hospital of Chengdu, Chengdu, China

The final, formatted version of the article will be published soon.

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.

Keywords: Primary bone tumor, Radiotherapy, Proton therapy, artificial intelligence, Radiomics, adaptive radiotherapy, deep learning, FLASH radiotherapy

Received: 17 Jun 2025; Accepted: 04 Aug 2025.

Copyright: © 2025 Tong, Chen, Li, Chen and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Jian Tong, Department of Spinal Surgery, Number One Orthopedics Hospital of Chengdu, Chengdu, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.