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    A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases.

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    Authors
    Pathak, Ryan
    Ragheb, Hossein
    Thacker, Neil A
    Morris, D
    Amiri, H
    Kuijer, J
    deSouza, N
    Heerschap, A
    Jackson, Alan
    Affiliation
    University of Manchester, Wolfson Molecular Imaging Centre, Manchester
    Issue Date
    2017-10-26
    
    Metadata
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    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
    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
    Journal
    Scientific Reports
    URI
    http://hdl.handle.net/10541/620726
    DOI
    10.1038/s41598-017-14625-0
    PubMed ID
    29075009
    Type
    Article
    Language
    en
    ISSN
    2045-2322
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41598-017-14625-0
    Scopus Count
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