Abstract
Quantitative imaging biomarkers (QIBs) provide medical image–derived intensity, texture, shape, and size features that may help characterize cancerous tumors and predict clinical outcomes. Successful clinical translation of QIBs depends on the robustness of their measurements. Biomarkers derived from positron emission tomography images are prone to measurement errors owing to differences in image processing factors such as the tumor segmentation method used to define volumes of interest over which to calculate QIBs. We illustrate a new Bayesian statistical approach to characterize the robustness of QIBs to different processing factors. Study data consist of 22 QIBs measured on 47 head and neck tumors in 10 positron emission tomography/computed tomography scans segmented manually and with semiautomated methods used by 7 institutional members of the NCI Quantitative Imaging Network. QIB performance is estimated and compared across institutions with respect to measurement errors and power to recover statistical associations with clinical outcomes. Analysis findings summarize the performance impact of different segmentation methods used by Quantitative Imaging Network members. Robustness of some advanced biomarkers was found to be similar to conventional markers, such as maximum standardized uptake value. Such similarities support current pursuits to better characterize disease and predict outcomes by developing QIBs that use more imaging information and are robust to different processing factors. Nevertheless, to ensure reproducibility of QIB measurements and measures of association with clinical outcomes, errors owing to segmentation methods need to be reduced.
Highlights
Quantitative imaging biomarkers (QIBs) provide medical image– derived intensity, texture, shape, and size features that have potential use in the characterization of disease and prediction of clinical outcomes
Other research has focused on performance of QIBs across multiple institutions, such as the analysis provided by Castelli et al [18] regarding the predictive value of quantitative fluorodeoxyglucose positron emission tomography (FDG PET) in 45 studies of head and neck cancer
While our work focuses on errors because of using different segmentation tools, the used statistical methods are broadly applicable to other settings in which scanner, operator, or other image source differences contribute to QIB measurement errors
Summary
Quantitative imaging biomarkers (QIBs) provide medical image– derived intensity, texture, shape, and size features that have potential use in the characterization of disease and prediction of clinical outcomes. Other research has focused on performance of QIBs across multiple institutions, such as the analysis provided by Castelli et al [18] regarding the predictive value of quantitative fluorodeoxyglucose positron emission tomography (FDG PET) in 45 studies of head and neck cancer. Despite the growing body of radiomics research and the established use of some imaging biomarkers, such as metabolic tumor volume (MTV), few new QIBs have been adopted for clinical decision-making. Discovery is the process of identifying biomarkers associated with a disease or disease outcome of interest in a limited patient population; whereas, validation and qualification are formal assessments of biomarker performance and clinical utility in a broader population. This process requires proper statistical estimation of measurement accuracy and precision for each of technical and clinical validation and proper statistical design and analysis of clinical trials for establishment of clinical utility
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