Andrews, Hillary, Rengier, Fabian, Myers, Andrea and Many further external authors, External (2026) Leveraging AI-Enabled Tumor Assessment Tools on Radiological Images to Evaluate Treatment Effect and Support Clinical Trial Endpoints in Solid Tumors. Friends of Cancer Research website.

Abstract

Accurate and consistent tumor measurement is fundamental to evaluating treatment response in oncology clinical trials. For more than 25 years, RECIST has provided a practical and widely adopted framework for these assessments, however there are limitations. Rapid advances in artificial intelligence (AI) offer an opportunity to modernize tumor response assessment. AI-enabled tumor assessment tools can detect, segment, and quantify tumors across the entire body with greater consistency and improved biological detail.
To realize this potential in drug development, the field must converge on clear standards for how AI-enabled tumor assessment tools are used, validated, and interpreted. Key questions include what is being measured, how results should be compared across tools, and how novel endpoints should be defined and qualified for regulatory use, particularly within pathways like FDA’s Accelerated Approval, where reliable early indicators of benefit are essential.
To drive alignment, Friends of Cancer Research convened a multi-stakeholder working group to evaluate the current landscape of AI-enabled tumor assessment tools and propose a stepwise roadmap for integration into clinical trials. This white paper outlines:
• Limitations of current imaging-based endpoints and opportunities to improve upon RECIST
• Emerging AI-driven approaches including enhanced RECIST, radiomics, volumetric analysis, and growth kinetics
• Necessary components of a framework to validate a novel AI-imaging based endpoint as an early endpoint, including outstanding questions for defining the endpoint and considerations for a meta-analysis
By advancing and aligning on methodological standards, the oncology community can enable AI-based tumor assessments to supplement, and in some cases surpass, traditional measurement approaches in clinical trials. This shift has the potential to improve endpoint precision, reduce trial timelines and costs, and ultimately accelerate patient access to safe and effective therapies.

Item Type:	Article
Date Deposited:	24 Mar 2026 00:45
Last Modified:	24 Mar 2026 00:45
URI:	https://oak.novartis.com/id/eprint/59538