Artificial intelligence-based personalized and interactive radiotherapy target definition for enhanced cancer care

Cancer is the second leading cause of death globally, with radiotherapy (RT) being a cornerstone treatment for nearly half of all patients. A key element of RT is the accurate definition of target volumes, a task traditionally performed manually by physicians. The quality of target volumes differs significantly between treatment centers and healthcare systems, directly impacting patients’ outcomes. While advances in artificial intelligence (AI) have simplified and standardized the segmentation of healthy organs, creating personalized target volumes that incorporate individual patient-specific data remains an unsolved challenge.AI-PIONEER aims to revolutionize the RT treatment planning process by combining the vast potential of multimodal large language models with 3D image segmentation. For the first time, AI-PIONEER will provide a language-based interface for target volume definition, offering a dynamic and adaptable solution for treatment planning. This will enable the unprecedented opportunity to process both scientific publications and patient-specific data to generate literature-founded, personalized, and precise target volumes.The project has three main objectives: (1) curating an extensive and high-quality multicenter CT dataset, (2) developing and fine-tuning a multimodal foundation AI model for personalized target volume creation, and (3) clinically validating the framework across multiple centers using prospective clinical data, ensuring robust results in real-world settings.By integrating cutting-edge AI technology with clinical expertise, AI-PIONEER will bridge the gaps between efficiency, standardization, and personalization in cancer treatment, ultimately improving patient outcomes worldwide. Thorough clinical validation will provide the basis for clinical translation. This approach is poised to significantly reduce the burden on clinicians while elevating the quality of cancer care.