Fixing the MRI R2‐iron calibration in liver

Iron overload is surprisingly common, resulting from genetic abnormalities of iron regulation or as a result of chronic transfusion therapy. The magnetic resonance imaging (MRI) assessment of tissue iron stores has become the standard of care for monitoring iron chelation strategies.1 Note, MRI relaxometry using R2 or R2* are most commonly used. Only one MRI method, based upon a standardized protocol of spin-echo acquisitions and analysis (Ferriscan®, Resonance Health, Western Australia), has achieved regulatory approval in Europe and the United States.2, 3 The Ferriscan® method has been compared against 338 biopsies in two large cohorts, demonstrates good interstudy reproducibility, and has strong quality control practices. However, its cost remains a challenge for many institutions, making iron measurements by R2* acquisitions more financially attractive. Several large studies comparing liver iron concentration (LIC) by R2* and by Ferriscan® R2 have identified substantial bias between R2* and R2 LIC estimates.4-6 We postulated that the original Ferriscan R2 calibration overestimates LIC at high iron concentrations, exaggerating disagreements between the two techniques. We searched the literature for all studies comparing single spin echo R2 acquisitions and liver biopsy results, identifying three studies having 1052, 247, and 2333 liver biopsies, respectively. We used a publicly available program(www.arizona-software.ch/graphclick) to digitally capture the values of R2 for each LIC. We fit the data to the existing FDA-approved calibration2, 7 and compared the residual errors to two other calibration curves. The first was derived using a linear fit in log transformed LIC and R2 coordinates (so-called power-law fit). The second was derived from a spline fit to data generated from previously published8 computer model. This computer model generates "synthetic" R2-iron pairs over the entire physiological range of iron overload, using ideal mathematical approximations to the MRI imaging physics and quantitative statistics of tissue iron deposition.8 It has been used to successfully translate the R2 and R2* liver calibrations to 3T9 with high accuracy. Figure 1A demonstrates a scattergram of all available R2-LIC pairs for liver biopsy data and the FDA approved R2-iron calibration curve. At first blush, the FDA approved calibration appears to represent a good fit to the aggregate data. However, on closer inspection, a preponderance of points lie above the fit line at high LIC. Furthermore, the measurement uncertainty increases as iron burden and liver R2 increases. This is common in biological systems and indicates that calibration error should be calculated as a percentage, rather than an absolute LIC difference. Figure 1B demonstrates the relative difference between the biopsy and FDA-approved LIC plotted against the average of the two measurements. The 95% confidence intervals of the raw Bland Altman relationship are [−69% to 69%]. However, there is a significant downward linear drift (r2 = 0.097, P < .001) with FDA-approved calibration overestimating biopsy by 1.1% per mg/g; the root mean squared of this regression is 30.5%. The drift remained significant even if LIC values greater than 20 mg/g were suppressed. These data suggest that the FDA-approved R2-iron calibration used by Ferriscan® overestimates true liver iron concentration for LIC values exceeding 16.5 mg/g dry weight, with the differences growing geometrically. The calibration error is sufficient to completely explain the differences between LIC by R2* and Ferriscan® R2 described in previously studies.4-6 Importantly, any attempts to "calibrate" R2* or other MRI methods against Ferriscan® need to account for this bias. Several factors contribute to the bias in FDA-approved calibration. The original calibration study probably had insufficient patients (N = 104) to fully characterize a complicated, nonlinear relationship having four degrees of freedom (the power-law calibration has two degrees of freedom). Secondly, the initial patient pool did not have sufficient numbers of patients with severe iron overload. Thirdly, the original calibration was intentionally biased for accurate low iron behavior by including 32 subjects having normal liver iron concentration. It is challenging to find a single curve that perfectly describes very low and very high LIC values. The properties of normal liver structure dominate R2 values at low iron concentration while the impact of siderosomes dominate at high iron concentration.8 The power-law and simulations are optimized for LIC values greater than 5 mg/g while the FDA-calibration was optimized for excellent low iron performance; a piecewise approach may ultimately represent the optimal solution. Lastly, the data heteroscedasticity was not adequately controlled during the fitting and evaluation processes for the FDA-approved calibration, contributing to systematic bias; the power law calibration corrects for heteroscedasticity. Fortunately, the clinical impact of the observed calibration bias is manageable. Patients with LIC below 16.5 mg/g are being accurately risk stratified. Patients with LIC greater than 16.5 mg/g in their liver are universally at high risk and should be treated aggressively, regardless. Serial trends in LIC-R2 values may also lessen the impact of calibration bias.5 Both Ferriscan® and R2* LIC estimates and provide accurate estimates of chelator efficiency on an annual basis.5 Further, both Ferriscan® and liver R2* LIC estimates are more accurate than liver biopsy in tracking changes in total body iron concentration.10 However, hematologists should treat Ferriscan® predicted LIC values of more than 16.5 mg/g with appropriate caution and integrate the predicted LIC values with the patient's entire clinical picture to avoid over-reacting to large changes in predicted LIC. If more accurate LIC predictions are desired in highly loaded subjects, Ferriscan® R2 values can be converted to LIC values from equation (2) using a simple calculator. This work supported by the National Institutes of Health, Diabetes, Digestive and Kidney Diseases (1R01DK097115-01A1). Dr. Wood serves as a consultant to Apopharma, Biomedinformatics, Bluebirdbio, Celgene, Ionis Pharmaceuticals, Imago Biosciences, Silence Therapeutics, and World Care Clinical.

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