Peer Review in Radiation Oncology: Present and Future Directions

Peer review is a vital process in radiation oncology that aims to enhance treatment quality, reduce errors, and standardize care through multidisciplinary evaluation of radiation treatment plans. Its implementation varies widely across institutions, with ongoing efforts to improve consistency and efficiency. This article summarizes current literature regarding the peer review process in radiation oncology. Peer review identifies change in approximately 3% to 12% of cases, particularly complex cases such as head and neck, gynecologic, and lung cancer with focus on changes in target volumes, dose, and organ-at-risk delineation. Complex treatment planning techniques such as intensity-modulated radiation therapy and proton therapy show higher rates of recommended changes that tend to decrease as departments gain experience and establish guidelines. Peer review processes differ, but often involve weekly multidisciplinary meetings to review selected cases based on complexity or treatment intent. Meeting format ranges from offline individual reviews to in-person or video conferences. Documentation practices vary widely, with inconsistencies in tracking of attendance, recommendations, and implementation of suggested changes. Effective peer review involves the treating physician and an experienced peer, but often includes support from medical physicists, dosimetrists, radiation therapists, and, occasionally, radiologists. Structured feedback varies from informal discussions to documented evaluations covering multiple aspects of treatment, with changes graded as no change, minor change, or major change; documentation of response to suggested change is inconsistent. Peer review is ideally conducted before treatment starts to allow plan modification with protected time and flexible meeting formats to enhance participation and regularity. Automation and artificial intelligence (AI) tools may streamline the review process by identifying cases needing attention, standardizing processes, and predicting dose distributions, thereby reducing clinician workload and improving accuracy. Future integration of AI tools for literature review, incident reporting, and documentation may support high-quality peer review. This underscores the need for standardized peer review frameworks in radiation oncology.