From Manual to AI - Based Tumor Contouring: Advancements in Radiation Therapy Planning

Dr. Anshu Goel

Abstract: AI - based tumor contouring offers several advantages over manual contouring in radiation therapy planning. This article reviews recent studies that demonstrate the potential of AI - based tumor contouring in improving the accuracy and efficiency of radiation therapy planning. The studies show that AI - based contouring significantly reduces the time required for tumor segmentation compared to manual contouring, and has a high level of accuracy. AI - based contouring can also reduce inter - observer variability in tumor segmentation and improve the consistency of target volumes among different radiation oncologists. Additionally, the article discusses the potential future directions for AI - based contouring, such as the use of generative adversarial networks and the integration with other radiation therapy planning technologies. The article also highlights the importance of physician oversight and validation of AI - based contours, as well as the need for ongoing quality control and monitoring of the technology. Overall, the article concludes that AI - based contouring has the potential to greatly improve the accuracy and efficiency of radiation therapy planning, and should be considered as a supplement to clinical decision - making.

Keywords: AI - based contouring; radiation therapy planning; tumor segmentation; deep learning; machine learning; generative adversarial networks; image - guided radiation therapy; proton therapy; tumor response prediction; personalized treatment planning