Comparative Effects of CT Imaging Measurement on RECIST End Points and Tumor Growth Kinetics Modeling

Abstract

Quantitative assessments of tumor burden and modeling of longitudinal growth could improve phase II oncology trials. To identify obstacles to wider use of quantitative measures we obtained recorded linear tumor measurements from three published lung cancer trials. Model‐based parameters of tumor burden change were estimated and compared with similarly sized samples from separate trials. Time‐to‐tumor growth (TTG) was computed from measurements recorded on case report forms and a second radiologist blinded to the form data. Response Evaluation Criteria in Solid Tumors (RECIST)‐based progression‐free survival (PFS) measures were perfectly concordant between the original forms data and the blinded radiologist re‐evaluation (intraclass correlation coefficient = 1), but these routine interrater differences in the identification and measurement of target lesions were associated with an average 18‐week delay (range, −20 to 55 weeks) in TTG (intraclass correlation coefficient = 0.32). To exploit computational metrics for improving statistical power in small clinical trials will require increased precision of tumor burden assessments.

A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors

“…A model structure showed as Eq. 14, where the change of biomarkers from baseline affects the tumor decay rate, was 52,56 An extension of Eq. 30 T = BASE · e − A·t + B · t + C · t 2 Eq.…”

“…This model structure has been successfully applied afterward 11 and was recently applied to analyze SLD measurements collected from NSCLC patients from three clinical studies to identify the obstacles to wider use of quantitative measures. 56 A quadratic growth term with a coefficient C was later introduced to this model structure as is shown in Eq. 30.…”

Transforming Translation: Impact of Clinical and Translational Science

“…Biomarkers serve as the “glue” that adheres many of the component translational disciplines together, and are undergoing a renaissance of increased interest. In this issue Li et al . discuss the innovative use of imaging as a biomarker in oncology.…”

“…Biomarkers serve as the "glue" that adheres many of the component translational disciplines together, and are undergoing a renaissance of increased interest. In this issue Li et al 6 Another tenet of the ASCPT definition of translational medicine is that translational research is bolstered by quantitative, model-based, and mechanistic approaches. Sheiner's learn-confirm paradigm 7 leads naturally to the idea of translational medicine in which pharmacometric models of drug efficacy and safety link preclinical and clinical data, particularly biomarker information, with prior knowledge of disease, and provide a quantitative approach to improving drug development and decision-making, as well as translational medicine more generally.…”

Vol-PACT: A Foundation for the NIH Public-Private Partnership That Supports Sharing of Clinical Trial Data for the Development of Improved Imaging Biomarkers in Oncology

“…However, tumor diameter measurements and qualitative metrics collected at sites on case report forms (CRFs) over the course of a trial are limited and lead to a potentially flawed estimation of tumor burden and representation of treatment effect. 19 Interest in the power of data sharing and big data to fill knowledge gaps has surged. One such knowledge gap is an understanding of which imaging end points are most reliable and efficient for characterizing therapeutic effect in clinical trials.…”