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

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

doi:10.1038/s41598-017-14625-0

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.

Highlights

Diffusion weighted imaging (DWI) is a Magnetic Resonance Imaging (MRI) sequence acquisition that is sensitive to free water diffusion[1,2]
The primary endpoint of this study was to define a statistical model of predictable sources of variability that contribute to measurement error, and fit this to observed data in order to quantify the level of uncertainty in mean Apparent Diffusion Coefficient (ADC) repeatability
Through standardisation of repeatability measurements for predictable sources of statistical variability that contribute to uncertainty in the mean ADC, we sought to increase our confidence in detecting genuine post treatment changes for future studies

Summary

Introduction

Diffusion weighted imaging (DWI) is a Magnetic Resonance Imaging (MRI) sequence acquisition that is sensitive to free water diffusion[1,2]. Regions of reduced extra-cellular space due to high cell density or other micro environmental factors will result in restricted diffusion of free water relative to surrounding tissue. In a densely cellular homogeneous tumour, such as lymphoma, treatment-related ADC changes may be as high as 50%4, treatment responses may be heterogeneous due to regional micro environmental factors or genetic variation[5,6,7]. The primary endpoint of this study was to define a statistical model of predictable sources of variability that contribute to measurement error, and fit this to observed data in order to quantify the level of uncertainty in mean ADC repeatability. Through standardisation of repeatability measurements for predictable sources of statistical variability that contribute to uncertainty in the mean ADC, we sought to increase our confidence in detecting genuine post treatment changes for future studies. There is considerable difference in the appearance and margination of metastases from different primary tumours and the potential impact of this will be discussed below

Methods

Results

Discussion

Conclusion