A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases.
Embargo End Date
ICR Authors
Authors
Pathak, R
Ragheb, H
Thacker, NA
Morris, DM
Amiri, H
Kuijer, J
deSouza, NM
Heerschap, A
Jackson, A
Ragheb, H
Thacker, NA
Morris, DM
Amiri, H
Kuijer, J
deSouza, NM
Heerschap, A
Jackson, A
Document Type
Journal Article
Date
2017-10-26
Date Accepted
2017-10-09
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.
Citation
Scientific reports, 2017, 7 (1), pp. 14084 - ?
Source Title
Publisher
NATURE PORTFOLIO
ISSN
2045-2322
eISSN
2045-2322
Collections
Research Team
Magnetic Resonance