Artificial intelligence in radiation oncology Target volume definition and organ segmentation

Background: Target volume definition is a central component of radiation planning in radiation oncology. In addition to anatomical organs that are in close proximity to the irradiated target region, target volume definition is a relevant part of a radiation oncologist's medical practice. Advances in the development of artificial intelligence (AI) have produced neural networks that can be used highly effectively to segment medical image data. Aim: To analyze the potential of AI-based autocontouring in radiation planning. The article presents the body of scientific work, existing software solutions and an outlook on future innovative solutions. Materials and methods: A literature search (PubMed) was performed to identify relevant literature. Results: The first approved software solutions allow automated contouring of anatomical organs. The segmentation quality for many organs is high, while certain positionally variable structures or particularly small organs still requiremore substantial corrections. The definition of clinical target volumes, e.g., in terms of local lymphatic drainage, achieve good reproducibility. For a variety of tumors, it has also been shown that neural networks can effectively and reproducibly define the gross tumor volume. Further developments, such as tumor growthmodels, may provide novel individualized definitions of target volumes. Conclusion: AI models for autocontouring have the potential to accelerate the work of radiation oncologists through partial automation and reducing staff time, while achieving increased standardization.