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Acad Radiol. 2017 Aug;24(8):1036-1049. doi: 10.1016/j.acra.2017.03.002. Epub 2017 Apr 26.

Beyond Correlations, Sensitivities, and Specificities: A Roadmap for Demonstrating Utility of Advanced Imaging in Oncology Treatment and Clinical Trial Design.

Author information

1
Biometric Research Program, Division of Cancer Treatment, Diagnosis National Cancer Institute, NIH, 9609 Medical Center Drive, MSC 9735, Bethesda, MD 20892-9735. Electronic address: erich.huang@nih.gov.
2
Cancer Imaging Program, Division of Cancer Treatment, Diagnosis National Cancer Institute, NIH, Bethesda, Maryland.

Abstract

Despite the widespread belief that advanced imaging should be very helpful in guiding oncology treatment decision and improving efficiency and success rates in treatment clinical trials, its acceptance has been slow. Part of this is likely attributable to gaps in study design and statistical methodology for these imaging studies. Also, results supporting the performance of the imaging in these roles have largely been insufficient to justify their use within the design of a clinical trial or in treatment decision making. Statistically significant correlations between the imaging results and clinical outcomes are often incorrectly taken as evidence of adequate performance. Assessments of whether the imaging can outperform standard techniques or meaningfully supplement them are also frequently neglected. This paper provides guidance on study designs and statistical analyses for evaluating the performance of advanced imaging in the various roles in treatment decision guidance and clinical trial conduct. Relevant methodology from the imaging literature is reviewed; gaps in the literature are addressed using related concepts from the more extensive genomic and in vitro biomarker literature.

KEYWORDS:

Clinical trial; cancer imaging; statistical analysis; study design

PMID:
28456570
PMCID:
PMC5547568
DOI:
10.1016/j.acra.2017.03.002
[Indexed for MEDLINE]
Free PMC Article

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