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An Empirical Bayes Approach to Robust Variance Estimation: A Statistical Proposal for Quantitative Medical Image Testing



John Lu, Charles D. Fenimore


The current standard for measuring tumor response using X-ray, CT and MRI is based on the response evaluation crite- rion in solid tumors (RECIST) which, while providing simplifications over previous (WHO) 2-D methods, stipulate four response categories: CR (complete response), PR (partial response), PD (progressive disease), SD (stable disease) based purely on percentage changes without consideration of any measurement uncertainty. In this paper, we propose a statistical procedure for tumor response assessment based on uncertainty measures of radiologist’s measurement data. We present several variance estimation methods using time series methods and empirical Bayes methods when a small number of serial observations are available on each member of a group of subjects. We use a publically available data- base which contains a set of over 100 CT scan images on 23 patients with annotated RECIST measurements by two radiologist readers. We show that despite the bias in each individual reader’s measurements, statistical decisions on tu- mor change can be made on each individual subject. The consistency of the two readers can be established based on the intra-reader change assessments. Our proposal compares favorably with the RECIST standard protocol, raising the hope that, statistically sound decision on change analysis can be made in the future based on careful variability and measurement uncertainty analysis.
Open Journal of Statistics


RECIST, Quantitative Imaging as a Biomarker, Change Analysis, Lung CT Image Measurement, Inter-Reader and Intra-Reader Variability, Time Series Variance Estimation, Estimation of Many Variances, Statistical Decision Rule on Change.


Lu, J. and Fenimore, C. (2012), An Empirical Bayes Approach to Robust Variance Estimation: A Statistical Proposal for Quantitative Medical Image Testing, Open Journal of Statistics, [online], (Accessed April 18, 2024)
Created October 23, 2012, Updated November 10, 2018