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

Scientific Reports
Ryan PathakAlan Jackson

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 reprod...Continue Reading

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Citations

Oct 13, 2019·Canadian Association of Radiologists Journal = Journal L'Association Canadienne Des Radiologistes·Myles T TaffelAndrew B Rosenkrantz
Jan 8, 2021·European Radiology·Nicolas F MichouxFrédéric E Lecouvet
May 15, 2021·The British Journal of Radiology·Roberto CannellaMarco Dioguardi Burgio

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