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AI-Powered Lesion-Level Tumor Growth Inhibition Modeling Improves Model Stability and Prognostic Association With PFS
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DOI：10.1002/psp4.70279.png)
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Abstract
En 中文
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 (KD) and the mean lesion size at week 8 were found to be significantly associated with PFS from the lesion-level models. In contrast, aggregated TGI models did not identify any significant prognostic factors of PFS. This modeling approach, utilizing AI-powered auto-measurement and incorporating individual lesion dynamics, provides a more complete understanding of tumor growth and can enable early decision-making for lesion-targeting therapies.
Keywords:
AI
PFS
tumor-growth-inhibition
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