The use of appropriate calibration curves corrects for systematic differences in liver R2* values measured using different software packages

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Abstract

Precise, reproducible measurements of liver iron concentration (LIC) are critical for the early diagnosis, treatment and follow-up of patients with primary or secondary iron overload. Magnetic resonance imaging (MRI), which exploits the paramagnetic properties of iron, has gained acceptance as a noninvasive and accurate tool for LIC assessment. Two different techniques have been described: methods measuring signal intensity ratio between liver and muscles (Gandon et al, 2004), and relaxometry methods measuring absolute R2 (St Pierre et al, 2005) or R2* (Wood et al, 2005) values, which increase proportionally to iron concentration. The gradient-echo R2* technique is most widely used in clinical practice (Anderson et al, 2001; Hankins et al, 2009; Meloni et al, 2011; Wood et al, 2005), because it is faster and easier than R2 acquisition. R2* values can be converted to underlying tissue iron concentration, using appropriate calibration curves. The first calibration curve was proposed by Anderson et al (2001) and later updated by Garbowski et al (2009). This methodology has been implemented in a popular analysis software named “ThalassaemiaTools”, a CMRtools (Cardiovascular Imaging Solutions Ltd, London, UK) plug-in. A second calibration curve was proposed (Wood et al, 2005) and later confirmed (Hankins et al, 2009), but it demonstrated a 15% lower scaling coefficient between R2* and LIC. The two R2* analysis methods differ in the size of analysed region of interest (ROI) and the model used to fit the signal decay at different echo times. A systematic comparison of R2* and LIC values obtained with the two methodologies has never been reported. This study aimed to detect the potential differences in R2* values obtained with different post-processing approaches and to explore whether the detected differences would be corrected when converted into LIC values. Single- and multi-centre patient cohorts were used. The single-centre cohort included 45 patients (25 males, 16·4 ± 10·2 years) scanned at the Children's Hospital of Los Angeles (CHLA). The multi-centre cohort (n = 47; 19 females, 28·1 ± 8·9 years) was baseline data from a phase II clinical trial of the iron chelator FBS0701 and was included to obtain higher generalizability. The study was approved by the CHLA Committee for the Protection of Human Subjects and the institutional review boards of all participating hospitals. In order to compare the two approaches linear regression analysis and Bland-Altman technique were used. The results are indicated in Table 1 and Supplemental Figs 1 and 2. For both the cohorts the relationship between R2*Wood and R2*Pennell values was well described by a line. Results were unbiased for R2* R2*Pennell value. For the multi-centre cohort the mean difference was 59·5 ± 76·7 Hz (95% CI, 36·9–81·9 Hz), corresponding to a relative difference of 8·5 ± 13·8%. When the technique-appropriate calibration curves were used, this bias effectively disappeared, producing excellent agreement between the two approaches. For the single-centre cohort the mean difference was −0·8 ± 1·5 mg/g/dry weight (dw) (95% CI, −1·3 to −0·3 mg/g per dw). Individual LIC estimates had 95% CI from −3·8 to 2·2 mg/g per dw. For the multi-centre cohort the mean difference was −1·0 ± 1·4 mg/g per dw (95% CI, −1·4 to −0·6 mg/g per dw), indicating a small, systematic bias. Individual LIC estimates had 95% CI of −3·8 to 1·8 mg/g per dw. There is ongoing discussion as to which of the two decay models most closely describes the true tissue relaxation. By simulation (true value known), the key determinant is whether there is any other signal contribution besides iron. If there is any background signal from non-iron containing tissue, the exponential-plus-constant model is more accurate (Positano et al, 2009). If there is no signal contribution from bile, blood or fat, the truncated exponential is more appropriate (Beaumont et al, 2009). Given that the two approaches sample the liver differently, one could potentially attribute the observed R2* differences to systematic gradients in iron distribution with proximity to the hilum. This was demonstrated to be untrue (McCarville et al, 2010; Positano et al, 2009). The 0·8–1 mg/g residual bias between the two LIC estimates is clinically irrelevant at higher values, but could be important when assessing the risk of over chelation. Some of the bias is evident from inspection of Equations 1 and 2; the calibration curves have a 0·5 mg/g difference in y-intercept. To ensure good extrapolation into normal iron levels, the y-intercept in Equation 2 was constrained such that the calibration curve passes through the middle of the normal range (Wood et al, 2005), while Equation 1 was not. To illustrate the potential consequences of this difference, R2* values in healthy volunteers are typically reported to be 30–40 Hz. Equation 2 predicts LIC values of 1·0–1·2 mg/g and Equation 1 predicts LIC values of 1·4–1·6 mg/g. Practitioners who use Equation 1 should know that “normal” LIC corresponds to a value around 1·5 mg/g and base their therapeutic judgments about that set-point. In conclusion, both signal decay models yield clinically-acceptable estimates of LIC if the ROI's are drawn correctly and the proper calibration curve is applied to correct for systematic differences in R2* estimation. Proper choice of technique at any given institution will depend on software availability and training. However, in the literature, R2* values should be converted into LIC values using the appropriate calibration curve to facilitate comparisons across studies. This work was supported by a grant from the National Institutes of Health, National Heart Lung and Blood Institute (1 RO1 HL075592-01A1), the Center for Disease Control (1 U01 DD000309-1), the National Center for Research Resources, Children's Hospital Los Angeles General Clinical Research Center (RR00043-43) and FerroKin BioSciences as sponsor of the study. We thank all patients for their cooperation. AM analysed the images, performed the statistical analysis and wrote the manuscript. HR, AJ, AP and ML contributed to the interpretation of the results. JCW conceived the study and was involved in writing the manuscript. The authors declare no competing interests. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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