AI-Powered Lesion-Level Tumor Growth Inhibition Modeling Improves Model Stability and Prognostic Association With PFS.

Alan Liu¹, Chong Duan², Kevin Maresca², Yazdi K Pithavala³, Jen-Tang Lu⁴, Cathy Cen Guo⁵, Jennifer E Hibma⁵

¹Pfizer Inc, New York, New York, USA.

²Pfizer Inc, Cambridge, Massachusetts, USA.

³Senior Clinical Pharmacology Consultant, Assistant Clinical Professor, UCSD Skaggs School of Pharmacy, San Diego, California, USA.

⁴Vysioneer Inc, Cambridge, Massachusetts, USA.

⁵Pfizer Inc, La Jolla, California, USA.

PubMed PMC DOI PDF

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 util…

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