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The first AI-guided toxicity atlas for safer and more effective abdominal radiation therapy

CORDIS · observation · Publication date unknown

umor control benefits of high-dose lung RT are accompanied by higher mortality rates due to extensive heart irradiation. The AIDose project will leverage my work on hepatobiliary toxicity prediction, artificial intelligence (AI) advancements, and medical image analysis to develop the first toxicity risk atlas for thoracic and abdominal organs-at-risk (OARs), i.e., organs located in close proximity to tumors. The atlas is envisioned as a three-dimensional map of OARs with pinpointed anatomical subregions associated with high toxicity risks. The AIDose project has four aims: a) developing AI-driven solutions for morphological and radiomic profiling of OARs; b) extracting and analyzing non-dosimetric clinical features associated with toxicities; c) utilizing AI to identify consistent patterns in radiation doses delivered to OAR subregions, and correlating them with toxicity risks; d) validating the resulting toxicity atlas against multi-hospital RT data. The feasibility of AIDose is supported by my previous research on liver and head-and-neck RT planning, which resulted in publications

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recordType

award

status

SIGNED

region

EU

value

1999972.75

unit

EUR