Inter-method reproducibility of biexponential R2 MR relaxometry for estimation of liver iron concentration

Liver Fibrosis, Fat, and Iron Evaluation with MRI and Fibrosis and Fat Evaluation with US: A Practical Guide for Radiologists

Quantitative imaging biomarkers of liver disease measured by using MRI and US are emerging as important clinical tools in the management of patients with chronic liver disease (CLD). Because of their high accuracy and noninvasive nature, in many cases, these techniques have replaced liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. The most commonly evaluated imaging biomarkers are surrogates for liver fibrosis, fat, and iron. MR elastography is now routinely performed to evaluate for liver fibrosis and typically combined with MRI-based liver fat and iron quantification to exclude or grade hepatic steatosis and iron overload, respectively. US elastography is also widely performed to evaluate for liver fibrosis and has the advantage of lower equipment cost and greater availability compared with those of MRI. Emerging US fat quantification methods can be performed along with US elastography. The author group, consisting of members of the Society of Abdominal Radiology (SAR) Liver Fibrosis Disease-Focused Panel (DFP), the SAR Hepatic Iron Overload DFP, and the European Society of Radiology, review the basics of liver fibrosis, fat, and iron quantification with MRI and liver fibrosis and fat quantification with US. The authors cover technical requirements, typical case display, quality control and proper measurement technique and case interpretation guidelines, pitfalls, and confounding factors. The authors aim to provide a practical guide for radiologists interpreting these examinations. ^© RSNA, 2023 See the invited commentary by Ronot in this issue. Quiz questions for this article are available in the supplemental material.

Quantification of Liver Iron Overload with MRI: Review and Guidelines from the ESGAR and SAR

Accumulation of excess iron in the body, or systemic iron overload, results from a variety of causes. The concentration of iron in the liver is linearly related to the total body iron stores and, for this reason, quantification of liver iron concentration (LIC) is widely regarded as the best surrogate to assess total body iron. Historically assessed using biopsy, there is a clear need for noninvasive quantitative imaging biomarkers of LIC. MRI is highly sensitive to the presence of tissue iron and has been increasingly adopted as a noninvasive alternative to biopsy for detection, severity grading, and treatment monitoring in patients with known or suspected iron overload. Multiple MRI strategies have been developed in the past 2 decades, based on both gradient-echo and spin-echo imaging, including signal intensity ratio and relaxometry strategies. However, there is a general lack of consensus regarding the appropriate use of these methods. The overall goal of this article is to summarize the current state of the art in the clinical use of MRI to quantify liver iron content and to assess the overall level of evidence of these various methods. Based on this summary, expert consensus panel recommendations on best practices for MRI-based quantification of liver iron are provided.

Multicenter Reproducibility of Liver Iron Quantification with 1.5-T and 3.0-T MRI

Background MRI is a standard of care tool to measure liver iron concentration (LIC). Compared with regulatory-approved R2 MRI, R2* MRI has superior speed and is available in most MRI scanners; however, the cross-vendor reproducibility of R2*-based LIC estimation remains unknown. Purpose To evaluate the reproducibility of LIC via single-breath-hold R2* MRI at both 1.5 T and 3.0 T with use of a multicenter, multivendor study. Materials and Methods Four academic medical centers using MRI scanners from three different vendors (three 1.5-T scanners, one 2.89-T scanner, and two 3.0-T scanners) participated in this prospective cross-sectional study. Participants with known or suspected liver iron overload were recruited to undergo multiecho gradient-echo MRI for R2* mapping at 1.5 T and 3.0 T (2.89 T or 3.0 T) on the same day. R2* maps were reconstructed from the multiecho images and analyzed at a single center. Reference LIC measurements were obtained with a commercial R2 MRI method performed using standardized 1.5-T spin-echo imaging. R2*-versus-LIC calibrations were generated across centers and field strengths using linear regression and compared using F tests. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic performance of R2* MRI in the detection of clinically relevant LIC thresholds. Results A total of 207 participants (mean age, 38 years ± 20 [SD]; 117 male participants) were evaluated between March 2015 and September 2019. A linear relationship was confirmed between R2* and LIC. All calibrations within the same field strength were highly reproducible, showing no evidence of statistically significant center-specific differences (P > .43 across all comparisons). Calibrations for 1.5 T and 3.0 T were generated, as follows: for 1.5 T, LIC (in milligrams per gram [dry weight]) = -0.16 + 2.603 × 10-2 R2* (in seconds-1); for 2.89 T, LIC (in milligrams per gram) = -0.03 + 1.400 × 10-2 R2* (in seconds-1); for 3.0 T, LIC (in milligrams per gram) = -0.03 + 1.349 × 10-2 R2* (in seconds-1). Liver R2* had high diagnostic performance in the detection of clinically relevant LIC thresholds (area under the ROC curve, >0.98). Conclusion R2* MRI enabled accurate and reproducible quantification of liver iron overload over clinically relevant ranges of liver iron concentration (LIC). The data generated in this study provide the necessary calibrations for broad clinical dissemination of R2*-based LIC quantification. ClinicalTrials.gov registration no.: NCT02025543 © RSNA, 2022 Online supplemental material is available for this article.

Abdominal magnetic resonance imaging examination of Tibetan patients with abnormal iron metabolism and a preliminary study of correlations with blood cell analysis

OBJECTIVE

The objective was to investigate factors influencing the high incidence of abnormal iron metabolism in a Tibetan population.

METHODS

This was a retrospective observational study. Magnetic resonance imaging and blood analysis of 363 Tibetan patients were performed and patients divided into normal and abnormal groups based on the clinical diagnostic standard. The upper limit of normal liver iron content was 50 μmol/g. We analyzed the association between abnormal iron metabolism and blood cell indicators using the Spearman rank correlation test.

RESULTS

In male patients, differences in mean corpuscular hemoglobin (MCH) and MCH concentration in blood between the normal and abnormal groups were significant. Abnormal iron metabolism in male patients was positively correlated with MCH and MCH concentration. In female patients, differences in erythrocytes, hemoglobin, and hematocrit levels between the two groups were significant. Erythrocyte counts and hemoglobin and hematocrit levels of female patients were positively correlated.

CONCLUSION

Iron overload in male patients was correlated with an increase in MCH and MCH concentration, and that in female patients was correlated with levels of erythrocytes, hemoglobin, and hematocrit. The incidence rate of iron overload was higher in males than in females and was correlated with age in this Tibetan population.

A novel gamma GLM approach to MRI relaxometry comparisons

PURPOSE

To demonstrate that constant coefficient of variation (CV), but nonconstant absolute variance in MRI relaxometry (T₁ , T₂ , R₁ , R₂ ) data leads to erroneous conclusions based on standard linear models such as ordinary least squares (OLS). We propose a gamma generalized linear model identity link (GGLM-ID) framework that factors the inherent CV into parameter estimates. We first examined the effects on calculations of contrast agent relaxivity before broadening to other applications such as analysis of variance (ANOVA) and liver iron content (LIC).

METHODS

Eight models including OLS and GGLM-ID were initially fit to data obtained on sulfated dextran iron oxide (SDIO) nanoparticles. Both a resampling simulation on the data as well as two separate Monte Carlo simulations (with and without concentration error) were performed to determine mean square error (MSE) and type I error rate. We then evaluated the performance of OLS/GGLM-ID on R₁ repeatability and LIC data sets.

RESULTS

OLS had an MSE of 4-5× that of GGLM-ID as well as a type I error rate of 20-30%, whereas GGLM-ID was near the nominal 5% level in the relaxivity study. Only OLS found statistically significant effects of MRI facility on relaxivity in an R₁ repeatability study, but no significant differences were found in a resampling, whereas GGLM was more consistent. GGLM-ID was also superior to OLS for modeling LIC.

CONCLUSIONS

OLS leads to erroneous conclusions when analyzing MRI relaxometry data. GGLM-ID factors in the inherent CV of an MRI experiment, leading to more reproducible conclusions.

R2 relaxometry based MR imaging for estimation of liver iron content: A comparison between two methods

PURPOSE

To compare the reproducibility and accuracy of R2-relaxometry MRI for estimation of liver iron concentration (LIC) between in-house analysis and FDA-approved commercially available third party results.

METHODS

All MR studies were performed on a 1.5T scanner. Multi-echo spin-echo scans with a fixed TR and increasing TE values of 6 ms, 9 ms, 12 ms, 15 ms, and 18 ms (spaced at 3 ms intervals) were used. Post-processing of the images to calculate mean relaxivity, R2, included drawing of regions of interest to include the whole liver on mid-slice. The relationship between liver R2 values and estimated LIC calculated with in-house analysis and values reported by an external company (FerriScan^®, Resonance Health, Australia) were assessed with correlation coefficients and Bland-Altman difference plots. Continuous variables are presented as mean ± standard deviation. Significance was set at p value < 0.05.

RESULTS

474 studies from 175 patients were included in the study (mean age 10.4 ± 4.2 years (range 1-18 years); 254 studies from girls, 220 studies from boys). LIC ranged from 0.6 to 43 mg/g dry tissue, covering a broad range from normal levels to extremely high iron levels. Linearity between proprietary and in-house methods was excellent across the observed range for R2 (31.5 to 334.8 s^-1); showing a correlation coefficient of r = 0.87, p < 0.001. Bland-Altman R2 difference plot between the two methods shows a mean bias of + 21.5 s^-1 (range - 47.0 to + 90.0 s^-1 between two standard deviations). LIC reported by FerriScan^® compared with LIC estimated in-house with R2 as reported by FerriScan^® agreed strongly, (r = 1.0, p < 0.001).

CONCLUSION

R2 relaxometry MR imaging for liver iron concentration estimation is reproducible between proprietary FDA-approved commercial software and in-house analysis methods.

Protocol optimization for cardiac and liver iron content assessment using MRI: What sequence should I use?

OBJECTIVE

To determine the optimal MRI protocol and sequences for liver and cardiac iron estimation in children.

METHODS

We evaluated patients ≤18 years with cardiac and liver MRIs for iron content estimation. Liver T2 was determined by a third-party company. Cardiac and Liver T2* values were measured by an observer. Liver T2* values were calculated using the available liver parenchyma in the cardiac MRI. Linear correlations and Bland-Altman plots were run between liver T2 and T2*, cardiac T2* values; and liver T2* on dedicated cardiac and liver MRIs.

RESULTS

139 patients were included. Mean liver T2 and T2* values were 8.6 ± 5.4 ms and 4.5 ± 4.1 ms, respectively. A strong correlation between liver T2 and T2* values was observed (r = 0.96, p < 0.001) with a bias (+4.1 ms). Mean cardiac bright- and dark-blood T2* values were 26.5 ± 12.9 ms and 27.2 ± 11.9 ms, respectively. Cardiac T2* values showed a strong correlation (r = 0.81, p < 0.001) with a low bias (-1.0 ms). The mean liver T2* on liver and cardiac MRIs were 4.9 ± 4.7 ms and 4.6 ± 3.9 ms, respectively. A strong correlation between T2* values was observed (r = 0.96, p < 0.001) with a small bias (-0.2 ms).

CONCLUSION

MRI protocols for iron concentration in the liver and the heart can be simplified to avoid redundant information and reduce scan time. In most patients, a single breath-hold GRE sequence can be used to evaluate the iron concentration in both the liver and heart.

IDEAL-IQ in an oncologic population: meeting the challenge of concomitant liver fat and liver iron

Background

Cancer patients often have a history of chemotherapy, putting them at increased risk of liver toxicity and pancytopenia, leading to elevated liver fat and elevated liver iron respectively. T1-in-and-out-of-phase, the conventional MR technique for liver fat assessment, fails to detect elevated liver fat in the presence of concomitantly elevated liver iron. IDEAL-IQ is a more recently introduced MR fat quantification method that corrects for multiple confounding factors, including elevated liver iron.

Methods

This retrospective study was approved by the institutional review board with a waiver for informed consent. We reviewed the MRI studies of 50 cancer patients (30 males, 20 females, 50–78 years old) whose exams included (1) T1-in-and-out-of-phase, (2) IDEAL-IQ, and (3) T2* mapping. Two readers independently assessed fat and iron content from conventional and IDEAL-IQ MR methods. Intraclass correlation coefficient (ICC) was estimated to evaluate agreement between conventional MRI and IDEAL-IQ in measuring R2* level (a surrogate for iron level), and in measuring fat level. Agreement between the two readers was also assessed. Wilcoxon signed rank test was employed to compare iron level and fat fraction between conventional MRI and IDEAL-IQ.

Results

Twenty percent of patients had both elevated liver iron and moderate/severe hepatic steatosis. Across all patients, there was high agreement between readers for IDEAL-IQ fat fraction (ICC = 0.957) and IDEAL R2* (ICC = 0.971) measurements, but lower agreement for conventional fat fraction measurements (ICC = 0.626). The fat fractions calculated with IOP were statistically significantly different from those calculated with IDEAL-IQ (reader 1: p < 0.001, reader 2: p < 0.001).

Conclusion

Fat measurements using IDEAL-IQ and IOP diverged in patients with concomitantly elevated liver fat and liver iron. Given prior work validating IDEAL-IQ, these diverging measurements indicate that IOP is inadequate to screen for hepatic steatosis in our cancer population.

MRI liver fat quantification in an oncologic population: the added value of complex chemical shift-encoded MRI

INTRODUCTION

Chemotherapy prolongs the survival of patients with advanced and metastatic tumors. Since the liver plays an active role in the metabolism of chemotherapy agents, hepatic injury is a common adverse effect. The purpose of this study is to compare a novel quantitative chemical shift encoded magnetic resonance imaging (CSE-MRI) method with conventional T1-weighted In and Out of phase (T1 IOP) MR for evaluating the reproducibility of the methods in an oncologic population exposed to chemotherapy.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board with a waiver for informed consent. The study included patients who underwent chemotherapy, no suspected liver iron overload, and underwent upper abdomen MRI. Two radiologists independently draw circular ROIsin the liver parenchyma. The fat fraction was calculated from IOP imaging and measured from IDEAL-IQ fat fraction maps. Two different equations were used to estimate fat with IOP sequences. Intra-class correlation coefficient and repeatability coefficient were estimated to evaluate agreement between two readers on iron level and fat fraction measurement.

RESULTS

CSE-MRI showed a higher reliability in fat quantification compared with both IOP methods, with a substantially higher inter-reader agreement (0.961 vs 0.372). This has important clinical implications.

CONCLUSION

The novel CSE-MRI method described here provides increased reproducibility and confidence in diagnosing hepatic steatosis in a oncologic clinical setting. IDEAL-IQ has been proved to be more reproducible than conventional IOP imaging.