AI-assisted RECIST with OncoChange speeds up reading and improves CT follow-up consistency

A recent study led by Max de Grauw from the team of Alessa Hering at Radboud University Medical Center investigated the impact of AI support developed by Fraunhofer MEVIS on RECIST assessment in follow-up CT scans of 212 cancer patients. Across 23 readers from 11 institutions, the AI assistance reduced assessment time per patient by about one third compared with manual reading and increased inter-reader agreement of RECIST outcomes by 7.7%.

The AI assistance used in this study is based on registration and segmentation algorithms that form the core of the OncoChange solution. It is designed to support more efficient and reliable tumor follow-up by streamlining measurement and image comparison within clinical workflows.

The study highlights how AI-supported image analysis can contribute to faster and more consistent tumor response assessment.

To the publication:

-> https://doi.org/10.1093/radadv/umag028

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-> OncoChange