A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases.
Authors
Pathak, RyanRagheb, Hossein
Thacker, Neil A
Morris, D
Amiri, H
Kuijer, J
deSouza, N
Heerschap, A
Jackson, Alan
Affiliation
University of Manchester, Wolfson Molecular Imaging Centre, ManchesterIssue Date
2017-10-26
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Apparent Diffusion Coefficient (ADC) is a potential quantitative imaging biomarker for tumour cell density and is widely used to detect early treatment changes in cancer therapy. We propose a strategy to improve confidence in the interpretation of measured changes in ADC using a data-driven model that describes sources of measurement error. Observed ADC is then standardised against this estimation of uncertainty for any given measurement. 20 patients were recruited prospectively and equitably across 4 sites, and scanned twice (test-retest) within 7 days. Repeatability measurements of defined regions (ROIs) of tumour and normal tissue were quantified as percentage change in mean ADC (test vs. re-test) and then standardised against an estimation of uncertainty. Multi-site reproducibility, (quantified as width of the 95% confidence bound between the lower confidence interval and higher confidence interval for all repeatability measurements), was compared before and after standardisation to the model. The 95% confidence interval width used to determine a statistically significant change reduced from 21.1 to 2.7% after standardisation. Small tumour volumes and respiratory motion were found to be important contributors to poor reproducibility. A look up chart has been provided for investigators who would like to estimate uncertainty from statistical error on individual ADC measurements.Citation
A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases. 2017, 7 (1):14084 Sci RepJournal
Scientific ReportsDOI
10.1038/s41598-017-14625-0PubMed ID
29075009Type
ArticleLanguage
enISSN
2045-2322ae974a485f413a2113503eed53cd6c53
10.1038/s41598-017-14625-0
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