A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases.
dc.contributor.author | Pathak, Ryan | |
dc.contributor.author | Ragheb, Hossein | |
dc.contributor.author | Thacker, Neil A | |
dc.contributor.author | Morris, D | |
dc.contributor.author | Amiri, H | |
dc.contributor.author | Kuijer, J | |
dc.contributor.author | deSouza, N | |
dc.contributor.author | Heerschap, A | |
dc.contributor.author | Jackson, Alan | |
dc.date.accessioned | 2017-12-15T15:25:01Z | |
dc.date.available | 2017-12-15T15:25:01Z | |
dc.date.issued | 2017-10-26 | |
dc.identifier.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 Rep | en |
dc.identifier.issn | 2045-2322 | |
dc.identifier.pmid | 29075009 | |
dc.identifier.doi | 10.1038/s41598-017-14625-0 | |
dc.identifier.uri | http://hdl.handle.net/10541/620726 | |
dc.description.abstract | 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. | |
dc.language.iso | en | en |
dc.rights | Archived with thanks to Scientific reports | en |
dc.title | A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases. | en |
dc.type | Article | en |
dc.contributor.department | University of Manchester, Wolfson Molecular Imaging Centre, Manchester | en |
dc.identifier.journal | Scientific Reports | en |
refterms.dateFOA | 2018-12-17T15:10:32Z | |
html.description.abstract | 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. |