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

Ryan Pathak*, Hossein Ragheb, Neil A. Thacker, David M. Morris, Houshang Amiri, Joost Kuijer, Nandita M. Desouza, Arend Heerschap, Alan Jackson

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Article number14084
JournalScientific Reports
Issue number1
Publication statusPublished - 1 Dec 2017

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