Tumor response to therapy in clinical trials is typically evaluated with RECIST, aiming to assess tumor burden objectively. However, even when correctly employed, RECIST evaluations still suffer from inconsistencies. This is primarily a result of the subjectivity in the selection of lesions at baseline, which is limited to a maximum of 5 lesions.
We conducted a mathematical simulation to analyze the factors contributing to RECIST variability, when the inter-reader measurement error is removed.
A patient model, parameterized by the number of lesions, organs, mean tumor growth and variance, was implemented. Multiple virtual cohorts of patients were created, by fixing all but one of the input parameters to a default value and varying the remaining within a range. Each patient was assessed by multiple virtual readers, who randomly select valid target lesions according to RECIST criteria. The discordance (D) in the selection of lesions by the different readers that resulted in divergent RECIST categories was computed.
The choice of lesions at baseline has a sizable impact on RECIST assessments. Variability aggravates when the number of measurable lesions is large (D up to 25%) and when lesion growth borders the thresholds for progressive disease and partial response (D up to 40%).

A radiological simulation computer model to analyze intrinsic RECIST variability

[Networking hour](https://app.wonder.me/?spaceId=fe093845-759f-448f-9f55-01d3adccd820)
[COIs](https://drive.google.com/drive/folders/1nbfwAa9lf9Kz-wAZ7iLKhfpBFjpN_gkn?usp=sharing)