03/06/2026
How can integrating AI-enabled tumor assessment tools into oncology drug development enhance RECIST, enable more comprehensive endpoints, and inform regulatory decision-making, while preserving evidentiary rigor?
Accurate and consistent tumor measurement is fundamental to evaluating treatment response in oncology clinical trials. For over 25 years, Response Evaluation Criteria in Solid Tumors (RECIST) has provided a standardized framework for tumor response assessment and regulatory decision-making. However, its reliance on manual, unidimensional assessment of a limited number of lesions introduces variability and may not fully capture total tumor burden or biological complexity.
AI-enabled tumor assessment tools hold the promise to modernize response evaluation. These tools can detect, segment, track, and quantify tumors across the whole body, capturing more comprehensive and quantitative measures of tumor burden and dynamics, including volumetrics, radiomic features, and growth kinetics. Realizing this potential requires clear evidentiary framework—before AI-enabled tool outputs can be incorporated into oncology drug development and clinical trials to support regulatory decision-making, the field must align on context of use, endpoint definitions, and appropriate standards for analytical and clinical validation.
Read our 2026 white paper which outlines a risk-informed, stepwise approach for integrating AI-enabled tumor assessment tools into clinical trials: https://bit.ly/4qQSlEE.
