Open AccessA New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics
Author(s) -
Yu Jiajie,
Wang Nina,
Kågedal Matts
Publication year - 2020
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12499
Subject(s) - tumor progression , progression free survival , lesion , oncology , hazard ratio , target lesion , medicine , clinical endpoint , solid tumor , response evaluation criteria in solid tumors , clinical trial , cancer , overall survival , pathology , confidence interval , phases of clinical research , percutaneous coronary intervention , myocardial infarction
Progression‐free survival (PFS) has been increasingly used as a primary endpoint for early clinical development. The aim of the present work was to develop a model where target lesion dynamics and risk for nontarget progression are jointly modeled for predicting PFS. The model was developed based on a pooled platinum‐resistant ovarian cancer dataset comprising four different treatments and a wide range of dose levels. The target lesion progression was derived from tumor growth dynamics based on the Response Evaluation Criteria in Solid Tumors (RECIST) criteria. The nontarget progression hazard was correlated to the first derivative of target lesion tumor size with respect to time. The PFS time was determined by the first occurring event, target lesion progression, or nontarget progression. The final joint model not only captured target lesion tumor growth dynamics but also predicted PFS well. A similar approach can potentially be used to predict PFS in future oncology studies.