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A Critical Discussion of Computer Analysis in Medical Imaging

Divisions of ¹ Nuclear Medicine/Radiology and ² Pediatric Pulmonary Medicine/Pediatrics, Stanford University, Stanford, California

Correspondence and requests for reprints should be addressed to M. L. Goris, M.D., Ph.D., Division of Nuclear Medicine, H0101, Stanford University School of Medicine, Stanford, CA 94305-5281. E-mail: mlgoris{at}stanford.edu

ABSTRACT

Medical imaging has increasingly provided surrogate endpoints in therapeutic trials. This use assumes that the interpretation of the images can be unbiased and reproducible and that the image attributes included in the interpretation are relevant to the mechanism of the trial. The principal motivation for computer analysis is to evaluate an attribute of the image as a metric in an algorithmic manner, independent of observer bias or variability. The metric is expected to reflect change in rough proportion with at least one aspect of the degree of disease or the effectiveness of the therapeutic intervention. If either condition is satisfied, the measure is quantitative. Visual interpretation explicitly or implicitly tends to be based on multiple image attributes. Explicit combination of multiple attributes yields composite scores. To evaluate the risk or probability of disease, they are useful. But the components of the scores can be combined only if they are mathematically isomorphic. For the evaluation of interventions, they are less useful because the effect on one component may be obscured by the lack of effect on other components. This article reviews quantification of air trapping in cystic fibrosis and quantification in general. Validation of any computer analysis can rely on agreement with visual interpreters (on average), they can be derived from first principles, or by agreement with an alternative method that measures the pathophysiological mechanism directly (xenon washout for air trapping). However, in the context of trials, the validation may come from a superior ability to detect objective change and to discriminate between affected and unaffected individuals.

Key Words: computer analysis • medical images • composite scores

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