Estimation and inference on the partial volume under the receiver operating characteristic surface

measures of biomarker accuracy that employ the receiver operating characteristic surface have been proposed for biomarkers that classify patients into one of three groups: healthy, benign, or aggressive disease. The volume under the receiver operating characteristic surface summarizes the overall discriminatory ability of a biomarker in such configurations, but includes cutoffs associated with clinically irrelevant true classification rates. Due to the lethal nature of pancreatic cancer, cutoffs associated with a low true classification rate for identifying patients with pancreatic cancer may be undesirable and not appropriate for use in a clinical setting. In this project, we study the properties of a more focused criterion, the partial volume under the receiver operating characteristic surface, that summarizes the diagnostic accuracy of a marker in the three-class setting for regions restricted to only those of clinical interest. We propose methods for estimation and inference on the partial volume under the receiver operating characteristic surface under parametric and non-parametric frameworks and apply these methods to the evaluation of potential biomarkers for the diagnosis of pancreatic cancer.

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Publication Details

Journal
Statistical Methods in Medical Research
Published
2024-08-08
DOI
https://doi.org/10.1177/09622802241267356
Citations
1
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.35

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article

Estimation and inference on the partial volume under the receiver operating characteristic surface

Kate Young, Leonidas E. Bantis
1 citations
Statistical Methods in Medical Research
AI in cancer detection
0.35
article

Estimation and inference on the partial volume under the receiver operating characteristic surface

Kate Young, Leonidas E. Bantis
article en
1 citations

Abstract

measures of biomarker accuracy that employ the receiver operating characteristic surface have been proposed for biomarkers that classify patients into one of three groups: healthy, benign, or aggressive disease. The volume under the receiver operating characteristic surface summarizes the overall discriminatory ability of a biomarker in such configurations, but includes cutoffs associated with clinically irrelevant true classification rates. Due to the lethal nature of pancreatic cancer, cutoffs associated with a low true classification rate for identifying patients with pancreatic cancer may be undesirable and not appropriate for use in a clinical setting. In this project, we study the properties of a more focused criterion, the partial volume under the receiver operating characteristic surface, that summarizes the diagnostic accuracy of a marker in the three-class setting for regions restricted to only those of clinical interest. We propose methods for estimation and inference on the partial volume under the receiver operating characteristic surface under parametric and non-parametric frameworks and apply these methods to the evaluation of potential biomarkers for the diagnosis of pancreatic cancer.

Statistical Methods in Medical ResearchVol. 33(9)
University of Kansas Medical Center (US)
U.S. Department of Defense, Honorable Tina Brozman Foundation, Children's Mercy Hospital, Ovarian Cancer Research Alliance, Kansas IDeA Network of Biomedical Research Excellence, National Institutes of Health, Cancer Center, University of Kansas, National Cancer Institute, National Institute of General Medical Sciences, National Center for Advancing Translational Sciences
Reduced inequalities
Openalex Percentile: Top 35%
AI in cancer detection
0.35
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