This review explores how artificial intelligence can improve dose accuracy in intraoperative radiotherapy, addressing challenges and enhancing treatment outcomes.
Introduction. Intraoperative radiotherapy (IORT) is now widely used in clinical practice. This review aims to report the most relevant clinical data and how the rise of the com-putation capacity can impact the development of IORT. Methods. This descriptive review summarizes key clinical evidence supporting IORT across various tumor types. It also examines emerging innovations, for instance, the use of artificial intelligence (AI) to enhance planning and dose accuracy. Results. IORT has proven effective in several cancers. In breast cancer, it is used as single-dose partial breast irradiation and as a boost in high-risk patients. In sarcomas, IORT enables high-dose delivery with lower toxicity and good functional outcomes. In pancreatic cancer, though not yet standard, IORT shows promise in improving local control in borderline resectable and unresectable cases. IORT has also been used in other malignancies within multimodal treatment approaches. Discussion. While IORT offers benefits like dose escalation under direct surgical visu-alization, it lacks real-time dose distribution assessment, limiting its accuracy. However, innovations such as CT- and MRI-equipped hybrid operating theatres, real-time plan-ning, and AI-driven contouring and dose prediction are closing this gap. Conclusion. IORT is a valuable adjunct in managing select cancers. Technological ad-vances are addressing current challenges in imaging and planning, paving the way for personalized, image-guided IORT. Future integration of AI and real-time imaging may make IORT a more precise, adaptive, and evidence-based treatment option.
No takes yet. Share an insight, caveat, or question.
Krengli et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: