ArticleCPT: pharmacometrics & systems pharmacology2026
AI-Powered Lesion-Level Tumor Growth Inhibition Modeling Improves Model Stability and Prognostic Association With PFS.
Article in CPT: pharmacometrics & systems pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Response Evaluation Criteria in Solid Tumors (RECIST 1.1) is currently the standard for tumor response measurement, but it is time-consuming, labor-intensive, and subject to reader variability. Tumor growth inhibition (TGI) models built on RECIST datasets often consider the time-course of all lesions as one aggregated tumor, as represented by the sum of longest diameters. An AI-based tool was utilized to obtain comprehensive data of both aggregated tumor and individual lesion growth dynamics in terms of their longest diameters and 3D volumes. Standard and AI-derived measurements were used to develop TGI models to evaluate the time-course of aggregate and lesion-level tumor shrinkage for patients receiving lorlatinib in the phase III CROWN trial. Additionally, a parametric time-to-event model was developed to describe the PFS probability. Lesion-level TGI models were connected to the PFS model by investigating the influence of various TGI-derived tumor metrics and parameter estimates on the association with PFS. TGI models developed using the more comprehensive AI-generated lesion measurements demonstrated better precision and were more robust than traditional RECIST-based models when tested under different scenarios using stochastic simulation and estimation (SSE). The mean value of tumor decay rate (K
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