Although radiation oncology has long stood at the forefront of medical automation, recent artificial intelligence (AI) applications are fundamentally redefining technical precision. Speaking with CancerNetwork®, Nevine Hanna, MD, detailed how automated contouring software rapidly delineates target volumes and organs at risk, compressing a task that once took hours into minutes. This dramatic leap in efficiency is paired with AI-driven treatment planning algorithms that calculate optimal beam arrangements and dose distributions, replacing time-consuming iterative tweaks by physics staff.
She described how the clinical benefits span across both proton and photon modalities, enabling real-time adaptive radiation therapy that accounts for daily anatomical changes and range uncertainties. According to Hanna, these automated tools elevate baseline consistency across the board, effectively narrowing the quality gap between community practices and major academic centers. Throughout this technological evolution, human expertise remains irreplaceable, as radiation oncologists must continuously evaluate, fine-tune, and sign off on all contours and treatment plans to guarantee patient safety.
Hanna is lead radiation oncologist at the Thompson Proton Therapy Center and director of the Radiation Oncology Division.
Transcript:
CancerNetwork: From a radiation oncology perspective, how are AI applications enhancing precision in treatment planning, auto-contouring, and organ-at-risk sparing for advanced therapies?
Hanna: Radiation oncology is wonderful in that we have been using AI and automation for quite a while, and these tools have been continually improving. When I attended the first United Nations governance meeting in Geneva to discuss guardrails for AI, one of the first [observations made] was that radiation oncologists must already know all about AI in medicine; they recognize the importance of AI in radiation oncology.
AI is already transforming many aspects. One of the biggest aspects is auto-contouring. It rapidly segments tumors and organs at risk, reducing contouring time from hours to minutes. This improves consistency and efficiency, allowing clinicians to [focus precisely] on where radiation should go and what it should spare. [AI] also improves treatment planning by [generating] predictable, achievable dose distributions and optimizing beam arrangements rather [than relying on iterative manual adjustments] by physics staff.
In proton therapy, AI supports adaptive radiotherapy by accounting for anatomic changes and range uncertainties. In photon therapy, it assists in conformal planning and normal tissue sparing. Overall, AI improves efficiency and consistency in ways that [help narrow the variation between] community and academic practice. Its helps streamline the process. At the end of the day, physician review remains critical; the radiation oncologist remains responsible for validating contours, confirming treatment intent, and ensuring the overall quality of the plan.
