title: ROC Analysis for Evaluation of Radiation Biodosimetry Technologies.
abstract: Receiver operating characteristic (ROC) analysis is a fundamental tool used for the evaluation and comparison of diagnostic systems that provides estimates of the combinations of sensitivity and specificity that can be achieved with a given technique. Along with critical considerations of practical limitations, such as throughput and time to availability of results, ROC analyses can be applied to provide meaningful assessments and comparisons of available biodosimetry methods. Accordingly, guidance from the Food and Drug Administration to evaluate biodosimetry devices recommends using ROC analysis. However, the existing literature for the numerous biodosimetry methods that have been developed to address the needs for triage either do not contain ROC analyses or present ROC analyses where the dose distributions of the study samples are not representative of the populations to be screened. The use of non-representative sample populations can result in a significant spectrum bias, where estimated performance metrics do not accurately characterize the true performance under real-world conditions. Particularly, in scenarios where a large group of people is screened because they were potentially exposed in a large-scale radiation event, directly measured population data do not exist. However, a number of complex simulations have been performed and reported in the literature that provide estimates of the required dose distributions. Based on these simulations and reported data about the output and uncertainties of biodosimetry assays, we illustrate how ROC curves can be generated that incorporate a realistic representative sample. A technique to generate ROC curves for biodosimetry data is presented along with representative ROC curves, summary statistics and discussion based on published data for triage-ready electron paramagnetic resonance in vivo tooth dosimetry, the dicentric chromosome assay and quantitative polymerase chain reaction assay. We argue that this methodology should be adopted generally to evaluate the performance of radiation biodosimetry screening assays so that they can be compared in the context of their intended use.
authors.affiliation: Department of Medicine, Section of Radiation Oncology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA Benjamin.B.Williams@dartmouth.edu.|Department of Radiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
authors.forename: Benjamin B
authors.initials: BB
authors.lastname: Williams
authors.affiliation: Department of Radiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
authors.forename: Ann Barry
authors.initials: AB
authors.lastname: Flood
authors.affiliation: Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
authors.forename: Eugene
authors.initials: E
authors.lastname: Demidenko
authors.affiliation: Department of Medicine, Section of Radiation Oncology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.|Department of Radiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
authors.forename: Harold M
authors.initials: HM
authors.lastname: Swartz
mesh_terms: D000465:Algorithms; D001681:Biological Assay / Q000379:methods*; D012372:ROC Curve; D000069079:Radiation Exposure / Q000032:analysis*; D011874:Radiometry / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity