Interpreting Change in Quantitative Imaging Biomarkers

Quantitative imaging biomarkers (QIBs) are becoming increasingly adopted into clinical practice to monitor changes in patients' conditions. The repeatability coefficient (RC) is the clinical cut-point used to discern between changes in a biomarker's measurements due to measurement error and changes that exceed measurement error, thus indicating real change in the patient. Imaging biomarkers have characteristics that make them difficult for estimating the repeatability coefficient, including nonconstant error, non-Gaussian distributions, and measurement error that must be estimated from small studies.

We conducted a Monte Carlo simulation study to investigate how well three statistical methods for estimating the repeatability coefficient perform under five settings common for QIBs.

When the measurement error is constant and replicates are normally distributed, all of the statistical methods perform well. When the measurement error is proportional to the true value, approaches that use the log transformation or coefficient of variation perform similarly. For other common settings, none of the methods for estimating the repeatability coefficient perform adequately.

Many of the common approaches to estimating the repeatability coefficient perform well for only limited scenarios. The optimal approach depends strongly on the pattern of the within-subject variability; thus, a precision profile is critical in evaluating the technical performance of QIBs. Asymmetric bounds for detecting regression vs progression can be implemented and should be used when clinically appropriate.