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dc.contributor.authorPathak, Ryan
dc.contributor.authorRagheb, Hossein
dc.contributor.authorThacker, Neil A
dc.contributor.authorMorris, D
dc.contributor.authorAmiri, H
dc.contributor.authorKuijer, J
dc.contributor.authordeSouza, N
dc.contributor.authorHeerschap, A
dc.contributor.authorJackson, Alan
dc.date.accessioned2017-12-15T15:25:01Z
dc.date.available2017-12-15T15:25:01Z
dc.date.issued2017-10-26
dc.identifier.citationA 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 Repen
dc.identifier.issn2045-2322
dc.identifier.pmid29075009
dc.identifier.doi10.1038/s41598-017-14625-0
dc.identifier.urihttp://hdl.handle.net/10541/620726
dc.description.abstractApparent 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.
dc.language.isoenen
dc.rightsArchived with thanks to Scientific reportsen
dc.titleA data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases.en
dc.typeArticleen
dc.contributor.departmentUniversity of Manchester, Wolfson Molecular Imaging Centre, Manchesteren
dc.identifier.journalScientific Reportsen
refterms.dateFOA2018-12-17T15:10:32Z
html.description.abstractApparent 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.


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