Abstract A007: Artificial intelligence in radiation therapy: Bringing in a new era of cancer therapy, accurate and fast
Authors: Kisia Newtone 
Published: January 2025

Abstract
Introduction: The application of AI in RT has been adopted as innovative method that seeks to enhance accuracy, effectiveness and patient outcome of cancer treatment. This aims to understand current and potential uses of AI-based tools in RT; from planning, monitoring during therapy, adaptive approach, and after treatment assessment. This study defines the role of AI in facilitating decision-making, made to define treatment plans, and to organize the clinical practice in order to improve the therapeutic outcomes and provide individualized approach to the cancer patients. Methods and Experimental Procedures: Radiation therapy work flows which integrated AI algorithms for treatment planning and delivery were examined in a retrospective manner. Imaging data was used to predict tumor behavior and develop the best treatment plan since the models employed by practitioners involved machine learning. Another application of AI was for the shape matching of its patient anatomy during treatment and came up with better matching for therapy adjustments. This involved data from multiple clinical centers employing AI-assisted techniques for prostate, lung as well head and neck cancers. These QA procedures implemented with the help of AI were assessed with respect to their capability in identifying errors and maintaining dose accuracy. Results: Initial data showed that AI optimizes accuracy and time in treatment planning by streamlining the processes of outlining tumors and relevant tissues; planning time is cut in half in comparison with the conventional approach. Real-time positioning indicated by AI optimized adaptive therapy gave accuracy in tracking the anatomical variations thereby justifying the alterations in radiation delivery that enhanced the tumor control without affecting normal tissue. Automated quality assurance systems with AI capabilities successfully detected possible mistakes in the application of treatment processes and thus, decisively enhanced patients’ safety and minimized risks of radiation mishaps. The data also showed that models using AI enhanced accuracy in dose prediction and thus the overall treatment plans and results especially when the anatomy of a patient is unique and cannot easily be navigated. Conclusions: AI is becoming integrated into RT since it delivers, precision, personalization and efficiency that the industry desires. Advanced AI-based technologies help in treatment planning, facilitate monitoring and improve the technique of adaptive RT while also strengthening QA measures thus making patients safer and providing better therapeutic results. In the future, emerging technologies in AI offers prospects to address problems in radiation oncology for improved diagnosis and treatment of cancer. There is need for more clinical and research practice and development to actualize AI applications, remove the constraints and apply these systems in regular clinical services. The future of RT is going to be with AI incorporated in all the departments to ensure that cancer treatment is optimized for the benefit of the patient.
Citation Format: Newtone Kisia. Artificial intelligence in radiation therapy: Bringing in a new era of cancer therapy, accurate and fast. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Targeted Therapies in Combination with Radiotherapy; 2025 Jan 26-29; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(2_Suppl):Abstract nr A007.