The relationship between liver stiffness by two-dimensional shear wave elastography and iron overload status in transfusion-dependent patients.

Increased liver stiffness (LS) can be result of increased liver iron concentration (LIC) which may not yet be reflected in the liver fibrotic status. The objective of our study was to examine relationship between hemochromatosis, LS, and serum ferritin level in transfusion-dependent patients. We recruited all 70 transfusion-dependent patients, whose median age was 15, referred for evaluating LIC status by magnetic resonance imaging (MRI) followed by two-dimensional ultrasonography shear wave elastography (2D-SWE). Thalassemia beta affected the majority of the patients. The optimal cut point for prediction of severe hemochromatosis using median SWE (kPa) and SWV (m/s) was ≥ 7.0 kPa and ≥ 1.54 m/s, respectively, with sensitivity of 0.76 (95% confidence interval [CI] 0.55, 0.91) and, specificity of 0.69 (95%CI 0.53, 0.82). When combing the optimal cut point of SWE (kPa) at ≥ 7.0 and serum ferritin ≥ 4123 ng/mL, the sensitivity increased to 0.84 (95%CI 0.64, 0.95) with specificity of 0.67 (95%CI 0.50, 0.80), positive predictive value (PPV) of 0.60 (95%CI 0.42, 0.76), and negative predictive value (NPV) of 0.88 (95%CI 0.71, 0.96). Simultaneous tests of 2D-SWE and serum ferritin for prediction of severe hemochromatosis showed the highest sensitivity of 84% (95%CI 0.64-0.95), as compared to 2D-SWE alone at 76% (95%CI 0.55, 0.91) or serum ferritin alone at 44% (95%CI 0.24-0.65). We recommend measuring both 2D-SWE and serum ferritin in short interval follow up patients. Adding 2D-SWE to management guideline will help in deciding for aggressive adjustment of iron chelating medication and increased awareness of patients having severe hemochromatosis.

Do iron homeostasis biomarkers mediate the associations of liability to type 2 diabetes and glycemic traits in liver steatosis and cirrhosis: a two-step Mendelian randomization study.

BACKGROUND: Previous studies, including Mendelian randomization (MR), have demonstrated type 2 diabetes (T2D) and glycemic traits are associated with increased risk of metabolic dysfunction-associated steatotic liver disease (MASLD). However, few studies have explored the underlying pathway, such as the role of iron homeostasis. METHODS: We used a two-step MR approach to investigate the associations of genetic liability to T2D, glycemic traits, iron biomarkers, and liver diseases. We analyzed summary statistics from various genome-wide association studies of T2D (n = 933,970), glycemic traits (n ≤ 209,605), iron biomarkers (n ≤ 246,139), MASLD (n ≤ 972,707), and related biomarkers (alanine aminotransferase (ALT) and proton density fat fraction (PDFF)). Our primary analysis was based on inverse-variance weighting, followed by several sensitivity analyses. We also conducted mediation analyses and explored the role of liver iron in post hoc analysis. RESULTS: Genetic liability to T2D and elevated fasting insulin (FI) likely increased risk of liver steatosis (ORliability to T2D: 1.14 per doubling in the prevalence, 95% CI: 1.10, 1.19; ORFI: 3.31 per log pmol/l, 95% CI: 1.92, 5.72) and related biomarkers. Liability to T2D also likely increased the risk of developing liver cirrhosis. Genetically elevated ferritin, serum iron, and liver iron were associated with higher risk of liver steatosis (ORferritin: 1.25 per SD, 95% CI 1.07, 1.46; ORliver iron: 1.15 per SD, 95% CI: 1.05, 1.26) and liver cirrhosis (ORserum iron: 1.31, 95% CI: 1.06, 1.63; ORliver iron: 1.34, 95% CI: 1.07, 1.68). Ferritin partially mediated the association between FI and liver steatosis (proportion mediated: 7%, 95% CI: 2-12%). CONCLUSIONS: Our study provides credible evidence on the causal role of T2D and elevated insulin in liver steatosis and cirrhosis risk and indicates ferritin may play a mediating role in this association.

Iron absorption in adults with sickle cell anemia: a stable-isotope approach.

PURPOSE: Iron absorption in sickle cell anemia (SCA) remains unclear and studies in adults with SCA are scarce. The aim of this study was to evaluate the iron absorption SCA adults and its association with iron status and hepcidin concentration. METHODS: SCA patients (n = 13; SCAtotal) and control participants (n = 10) ingested an oral stable iron isotope (57Fe). Iron absorption was measured by inductively coupled plasma mass spectrometry (ICP-MS) 14 days after isotope administration. Patients with ≥ 1000 ng/mL serum ferritin were considered to present iron overload (IO) (SCAio+; n = 3) and others classified without IO (SCAio-; n = 10). RESULTS: Iron absorption in the control group ranged from 0.3 to 26.5% (median = 0.9%), while it varied from 0.3 to 5.4% in SCAio+ (median = 0.5%) and from 0.3 to 64.2% in the SCAio- (median = 6.9%). Hepcidin median values were 14.1 ng/mL (3.0-31.9 ng/mL) in SCAio-, 6.2 ng/mL (3.3-7.8 ng/mL) in SCAio + and 6.2 ng/mL (0.6-9.3 ng/mL) in control. Iron absorption was associated with ferritin level (r = - 0.641; p = 0.018) and liver iron concentration (LIC; r = - 0.786; p = 0.036) in the SCAtotal group. CONCLUSION: Our data suggest that SCAio- individuals may be at risk of developing primary IO. Simultaneously, secondary IO may induce physiological adaptation, resulting in reduced iron absorption. Further studies evaluating intestinal iron absorption using larger sample sizes should be conducted to help establish a safe nutrition approach to be adopted and to ensure the security of food-fortifying public policies for these patients. TRIAL REGISTRATION: This trial was registered at www.ensaiosclinicos.gov.br (Identifier RBR-4b7v8pt).

Practical Application of Multivendor MRI-Based R2* Mapping for Liver Iron Quantification at 1.5 T and 3.0 T.

BACKGROUND: Recent multicenter, multivendor MRI-based R2* vs. liver iron concentration (LIC) calibrations (i.e., MCMV calibrations) may facilitate broad clinical dissemination of R2*-based LIC quantification. However, these calibrations are based on a centralized offline R2* reconstruction, and their applicability with vendor-provided R2* maps is unclear. PURPOSE: To determine R2* ranges of agreement between the centralized and three MRI vendors' R2* reconstructions. STUDY TYPE: Prospective. SUBJECTS: Two hundred and seven subjects (mean age 37.6 ± 19.6 years; 117 male) with known or suspected iron overload from four academic medical centers. FIELD STRENGTH/SEQUENCE: Standardized multiecho spoiled gradient echo sequence at 1.5 T and 3.0 T for R2* mapping and a multiple spin-echo sequence at 1.5 T for LIC quantification. MRI vendors: GE Healthcare, Philips Healthcare, and Siemens Healthineers. ASSESSMENT: R2* maps were generated using both the centralized and vendor reconstructions, and ranges of agreement were determined. R2*-LIC linear calibrations were determined for each site, field strength, and reconstruction and compared with the MCMV calibrations. STATISTICAL TESTS: Bland-Altman analysis to determine ranges of agreement. Linear regression, analysis of covariance F tests, and Tukey's multiple comparison testing to assess reproducibility of calibrations across sites and vendors. A P value <0.05 was considered significant. RESULTS: The upper limits of R2* ranges of agreement were approximately 500, 375, and 330 s-1 for GE, Philips, and Siemens reconstructions, respectively, at 1.5 T and approximately 700 and 800 s-1 for GE and Philips, respectively, at 3.0 T. Within the R2* ranges of agreement, vendor R2*-LIC calibrations demonstrated high reproducibility (no significant differences between slopes or intercepts; P ≥ 0.06) and agreed with the MCMV calibrations (overlapping 95% confidence intervals). DATA CONCLUSION: Based on the determined upper limits, R2* measurements obtained from vendor-provided R2* maps may be reliably and practically used to quantify LIC less than approximately 8-13 mg/g using the MCMV calibrations and similar acquisition parameters as this study. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 3.

A fully automatic parenchyma extraction method for MRI T2* relaxometry of iron loaded liver in transfusion-dependent patients.

PURPOSE: To develop a fully automatic parenchyma extraction method for the T2* relaxometry of iron overload liver. METHODS: A retrospective multicenter collection of liver MR examinations from 177 transfusion-dependent patients was conducted. The proposed method extended a semiautomatic parenchyma extraction algorithm to a fully automatic approach by introducing a modified TransUNet on the R2* (1/T2*) map for liver segmentation. Axial liver slices from 129 patients at 1.5 T were allocated to training (85%) and internal test (15%) sets. Two external test sets separately included 1.5 T data from 20 patients and 3.0 T data from 28 patients. The final T2* measurement was obtained by fitting the average signal of the extracted liver parenchyma. The agreement between T2* measurements using fully and semiautomatic parenchyma extraction methods was assessed using coefficient of variation (CoV) and Bland-Altman plots. RESULTS: Dice of the deep network-based liver segmentation was 0.970 ± 0.019 on the internal dataset, 0.960 ± 0.035 on the external 1.5 T dataset, and 0.958 ± 0.014 on the external 3.0 T dataset. The mean difference bias between T2* measurements of the fully and semiautomatic methods were separately 0.12 (95% CI: -0.37, 0.61) ms, 0.04 (95% CI: -1.0, 1.1) ms, and 0.01 (95% CI: -0.25, 0.23) ms on the three test datasets. The CoVs between the two methods were 4.2%, 4.8% and 2.0% on the internal test set and two external test sets. CONCLUSIONS: The developed fully automatic parenchyma extraction approach provides an efficient and operator-independent T2* measurement for assessing hepatic iron content in clinical practice.

Liver iron overload and fat content analyzed by magnetic resonance contribute to evaluatingthe progression of chronic hepatitis B.

Chronic hepatitis B (CHB) and its complications still have a major role in liver-related mortality. It has been indicated that hepatic iron and steatosis may influence liver fibrosis and carcinogenesis. The present study aimed to assess the liver iron and fat in patients with CHB by MRI in order to estimate the associations among liver iron, fat and the severity and progression of liver fibrosis. In the present retrospective study, consecutive patients with CHB examined from August 2018 to August 2020 were analyzed. Liver iron and fat content were assessed by MRI, which was measured as liver iron content (LIC) and proton density fat fraction (PDFF). A total of 340 patients were included in the current study. For LIC, the median value was 1.68 mg/g and elevated LIC was seen in 122 patients (35.9%). For liver fat content, the median value of PDFF was 3.1%, while only 15.0% of patients had liver steatosis (PDFF ≥5%). Age, total bilirubin and sex were independent predictive factors of liver iron overload [odds ratio (OR)=1.036, 1.005 and 8.834, respectively]. A higher platelet count (OR=1.005) and no portal hypertension (OR=0.381) independently predicted liver steatosis. The areas under the receiver operating characteristic curves of PDFF for the identification of liver cirrhosis estimated by different non-invasive tools ranged from 0.629 to 0.704. It was concluded that iron overload was common in patients with CHB, particularly in those with older age, male sex and high total bilirubin level, and liver steatosis was less common in CHB. Liver iron and fat content analyzed by MRI may contribute to the evaluation of the severity and progression of CHB.

Relationship between bone marrow iron load and liver iron concentration in dialysis-associated haemosiderosis.

BACKGROUND: Iron overload due to the excessive use of parenteral iron in haemodialysis is now an increasingly recognised clinical issue. Before erythropoiesis-stimulating agents (ESA) were introduced, a specific feature of patients treated by dialysis and having iron overload was that iron levels in the bone marrow were paradoxically low in most of them, despite severe hepatosplenic siderosis. Whether or not this paradox persists in the actual ESA era was unknown until recently, when an autopsy study in 21 patients treated by haemodialysis revealed similarities between liver and bone marrow iron content. The aim of this study was to further explore these recent findings in a cohort of alive patients on dialysis and to analyse the determinants of iron bone marrow. METHODS: Liver iron concentration (LIC) and vertebral T2∗ (a surrogate marker of bone marrow iron) were analysed retrospectively in 152 alive patients on dialysis (38.8% female) of whom 47.4% had iron overload by quantitative magnetic resonance imaging (MRI). FINDINGS: Vertebral T2∗ differed significantly between patients classified according to liver iron content at MRI: those with mild or moderate and severe liver iron overload had increased vertebral iron content at R2∗ relaxometry MRI (mild: vertebral T2∗ = 9.9 ms (4-24.8); moderate and severe: vertebral T2∗ = 8.5 ms (4.9-22.8)) when compared to patients with normal LIC (vertebral T2∗ = 13.2 ms (6.6-30.5) (p < 0.0001 Kruskal-Wallis test)). INTERPRETATION: The paradoxical discrepancy between bone marrow and liver iron-storage compartments observed in the pre-ESA era has disappeared today, as shown by a recent autopsy study and the present study in a cohort of alive patients treated by dialysis. FUNDING: None.

Hyperferritinemia and liver iron content determined with MRI: Reintroduction of the liver iron index.

BACKGROUND: Hyperferritinemia is found in around 12 % of the general population. Analyzing the cause can be difficult. In case of doubt about the presence of major iron overload most guidelines advice to perform a MRI as a reliable non-invasive marker to measure liver iron concentration (LIC). In general, a LIC of ≥ 36 µmol/g dw is considered the be elevated however in hyperferritinemia associated with, for example, obesity or alcohol (over)consumption the LIC can be ≥ 36 µmol/g dw in abscence of major iron overload. So, unfortunately a clear cut-off value to differentiate iron overload from normal iron content is lacking. Previously the liver iron index (LII) (LIC measured in liver biopsy (LIC-b)/age (years)), was introduced to differentiate between patients with major (LII ≥ 2) and minor or no iron overload (LII < 2). Based on the good correlation between the LIC-b and LIC determined with MRI (LIC-MRI), our goal was to investigate whether a LII_MRI ≥ 2 is a good indicator of major iron overload, reflected by a significantly higher amount of iron needed to be mobilized to reach iron depletion. METHODS: We compared the amount of mobilized iron to reach depletion and inflammation-related characteristics in two groups: LII-MRI ≥ 2 versus LII-MRI <2 in 92 hyperferritinemia patients who underwent HFE genotyping and MRI-LIC determination. RESULTS: Significantly more iron needed to be mobilized to reach iron depletion in the LII ≥ 2 group (mean 4741, SD ± 4135 mg) versus the LII-MRI <2 group (mean 1340, SD ± 533 mg), P < 0.001. Furthermore, hyperferritinemia in LII-MRI < 2 patients was more often related to components of the metabolic syndrome while hyperferritinemia in LII-MRI ≥ 2 patients was more often related to HFE mutations. ROC curve analysis showed good performance of LII =2 as cut-off value. However the calculations showed that the optimal cut-off for the LII = 3.4. CONCLUSION: The LII-MRI with a cut-off value of 2 is an effective method to differentiate major from minor iron overload in patients with hyperferritinemia. But the LII-MRI = 3.4 seems a more promising diagnostic test for major iron overload.

Using artificial intelligence to improve body iron quantification: A scoping review.

This scoping review explores the potential of artificial intelligence (AI) in enhancing the screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic search was performed to identify studies that utilize machine learning in iron-related disorders. The search revealed a wide range of machine learning algorithms used by different studies. Notably, most studies used a single data type. The studies varied in terms of sample sizes, participant ages, and geographical locations. AI's role in quantifying iron concentration is still in its early stages, yet its potential is significant. The question is whether AI-based diagnostic biomarkers can offer innovative approaches for screening, diagnosing, and monitoring of iron overload and anemia.

Simulation of a virtual liver iron overload model and R2 * estimation using multispectral fat-water models for GRE and UTE acquisitions at 1.5 T and 3 T.

R2 *-MRI has emerged as a noninvasive alternative to liver biopsy for assessment of hepatic iron content (HIC). Multispectral fat-water R2 * modeling techniques such as the nonlinear least squares (NLSQ) fitting and autoregressive moving average (ARMA) models have been proposed for the accurate assessment of iron overload by also considering fat, which can otherwise confound R2 *-based HIC measurements in conditions of coexisting iron overload and steatosis. However, the R2 * estimation by these multispectral models has not been systematically investigated for various acquisition methods in iron overload only conditions and across the full clinically relevant range of HICs (0-40 mg Fe/g dry liver weight). The purpose of this study is to evaluate the R2 * accuracy and precision of multispectral models for various multiecho gradient echo (GRE) and ultrashort echo time (UTE) imaging acquisitions by constructing virtual iron overload models based on true histology and synthesizing MRI signals via Monte Carlo simulations at 1.5 T and 3 T, and comparing their results with monoexponential model and published in vivo R2 *-HIC calibrations. The signals were synthesized with TE1 = 1.0 ms for GRE and TE1 = 0.1 ms for UTE acquisition for varying echo spacing, ΔTE (0.1, 0.5, 1, 2 ms), and maximum echo time, TEmax (2, 4, 6, 10 ms). An iron-doped phantom study is also conducted to validate the simulation results in experimental GRE (TE1 = 1.2 ms, ΔTE = 0.72 ms, TEmax = 6.24 ms) and UTE (TE1 = 0.1 ms, ΔTE = 0.5 ms, TEmax = 6.1 ms) acquisitions. For GRE acquisitions, the multispectral ARMA and NLSQ models produced higher slopes (0.032-0.035) compared with the monoexponential model and published in vivo R2 *-HIC calibrations (0.025-0.028). However, for UTE acquisition for shorter echo spacing (≤0.5 ms) and longer maximum echo time, TEmax (≥6 ms), the multispectral and monoexponential signal models produced similar R2 *-HIC slopes (1.5 T, 0.028-0.032; 3 T, 0.014-0.016) and precision values (coefficient of variation < 25%) across the full clinical spectrum of HICs at both 1.5 T and 3 T. The phantom analysis also showed that all signal models demonstrated a significant improvement in R2 * estimation for UTE acquisition compared with GRE, confirming our simulation findings. Future work should investigate the performance of multispectral fat-water models by simulating liver models in coexisting conditions of iron overload and steatosis for accurate R2 * and fat quantification.

Free-breathing high isotropic resolution quantitative susceptibility mapping (QSM) of liver using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction.

PURPOSE: To enable free-breathing and high isotropic resolution liver quantitative susceptibility mapping (QSM) using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction. METHODS: Using 3D multi-echo UTE cones MRI, a respiratory motion was estimated from the k-space center of the imaging data. After sorting the k-space data with estimated motion, respiratory motion state-resolved reconstruction was performed for multi-echo data followed by nonlinear least-squares fitting for proton density fat fraction (PDFF), R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , and fat-corrected B0 field maps. PDFF and B0 field maps were subsequently used for QSM reconstruction. The proposed method was compared with motion-averaged (gridding) reconstruction and conventional 3D multi-echo Cartesian MRI in moving gadolinium phantom and in vivo studies. Region of interest (ROI)-based linear regression analysis was performed on these methods to investigate correlations between gadolinium concentration and QSM in the phantom study and between R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and QSM in in vivo study. RESULTS: Cones with motion-resolved reconstruction showed sharper image quality compared to motion-averaged reconstruction with a substantial reduction of motion artifacts in both moving phantom and in vivo studies. For ROI-based linear regression analysis of the phantom study, susceptibility values from cones with motion-resolved reconstruction ( QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.31 × gadolinium mM + $$ \times {\mathrm{gadolinium}}_{\mathrm{mM}}+ $$ 0.05, R 2 $$ {R}^2 $$ = 0.999) and Cartesian without motion ( QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.32 × gadolinium mM + $$ \times {\mathrm{gadolinium}}_{\mathrm{mM}}+ $$ 0.04, R 2 $$ {R}^2 $$ = 1.000) showed linear relationships with gadolinium concentrations and showed good agreement with each other. For in vivo, motion-resolved reconstruction showed higher goodness of fit ( QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.00261 × R 2 s - 1 * - $$ \times {\mathrm{R}}_{2_{{\mathrm{s}}^{-1}}}^{\ast }- $$ 0.524, R 2 $$ {R}^2 $$ = 0.977) compared to motion-averaged reconstruction ( QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.0021 × R 2 s - 1 * - $$ \times {\mathrm{R}}_{2_{{\mathrm{s}}^{-1}}}^{\ast }- $$ 0.572, R 2 $$ {R}^2 $$ = 0.723) in ROI-based linear regression analysis between R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and QSM. CONCLUSION: Feasibility of free-breathing liver QSM was demonstrated with motion-resolved 3D multi-echo UTE cones MRI, achieving high isotropic resolution currently unachievable in conventional Cartesian MRI.

Non-invasive measurement of liver iron concentration by magnetic resonance imaging and its clinical usefulness.

Determination of liver iron concentration by magnetic resonance imaging (MRI) is becoming the new technique of choice for the diagnosis of iron overload in hereditary haemochromatosis and other liver iron surcharge diseases. Determination of hepatic iron concentration obtained by liver biopsy has been the gold standard for years. The development of MRI techniques, via signal intensity ratio methods or relaxometry, has provided a non-invasive and more accurate approach to the diagnosis of liver iron overload. This article reviews the available MRI methods for the determination of liver iron concentration and also evaluates the technique for the diagnosis and quantification of iron overload in different clinical practice scenarios.

A multicenter study on the quantification of liver iron concentration in thalassemia patients by means of the MRI T2* technique.

Objective: To investigate the feasibility and accuracy of quantifying liver iron concentration (LIC) in patients with thalassemia (TM) using 1.5T and 3T T2* MRI. Methods: 1.5T MRI T2* values were measured in 391 TM patients from three medical centers: the T2* values of the test group were combined with the LIC (LICF) provided by FerriScan to construct the curve equation. In addition, the liver 3T MRI liver T2* data of 55 TM patients were measured as the 3T group: the curve equation of 3T T2* value and LICF was constructed. Results: Based on the test group LICF (0.6-43 mg/g dw) and the corresponding 1.5T T2* value, the equation was LICF = 37.393T2*∧(-1.22) (R2 = 0.971; P < 0.001). There was no significant difference between LICe - 1.5T and LICF in each validation group (Z = -1.269, -0.977, -1.197; P = 0.204, 0.328, 0.231). There was significant consistency (Kendall's W = 0.991, 0.985, 0.980; all P < 0.001) and high correlation (rs = 0.983, 0.971, 0.960; all P < 0.001) between the two methods. There was no significant difference between the clinical grading results of LICe - 1.5T and LICF in each validation group (χ2 = 3.0, 4.0, 2.0; P = 0.083, 0.135, 0.157), and there was significant consistency between the clinical grading results (Kappa's K = 0.943, 0.891, 0.953; P < 0.001). There was no statistical correlation between the LICF (≥14 mg/g dw) and the 3T T2* value of severe iron overload (P = 0.085). The LICF (2-14 mg/g dw) in mild and moderate iron overload was significantly correlated with the corresponding T2* value (rs = -0.940; P < 0.001). The curve equation constructed from LICF and corresponding 3T T2* values in this range is LICF = 18.463T2*∧(-1.142) (R2 = 0.889; P < 0.001). There was no significant difference between LICF and LICe - 3T in the mild to moderate range (Z = -0.523; P = 0.601), and there was a significant correlation (rs = 0.940; P < 0.001) and significant consistency (Kendall's W = 0.970; P = 0.008) between them. LICe - 3T had high diagnostic efficiency in the diagnosis of severe, moderate, and mild liver iron overload (specificity = 1.000, 0.909; sensitivity = 0.972, 1.000). Conclusion: The liver iron concentration can be accurately quantified based on the 1.5T T2* value of the liver and the specific LIC-T2* curve equation. 3T T2* technology can accurately quantify mild-to-moderate LIC, but it is not recommended to use 3T T2* technology to quantify higher iron concentrations.

Multiparametric MR assessment of liver fat, iron, and fibrosis: a concise overview of the liver "Triple Screen".

Chronic liver disease (CLD) is a common source of morbidity and mortality worldwide. Non-alcoholic fatty liver disease (NAFLD) serves as a major cause of CLD with a rising annual prevalence. Additionally, iron overload can be both a cause and effect of CLD with a negative synergistic effect when combined with NAFLD. The development of state-of-the-art multiparametric MR solutions has led to a change in the diagnostic paradigm in CLD, shifting from traditional liver biopsy to innovative non-invasive methods for providing accurate and reliable detection and quantification of the disease burden. Novel imaging biomarkers such as MRI-PDFF for fat, R2 and R2* for iron, and liver stiffness for fibrosis provide important information for diagnosis, surveillance, risk stratification, and treatment. In this article, we provide a concise overview of the MR concepts and techniques involved in the detection and quantification of liver fat, iron, and fibrosis including their relative strengths and limitations and discuss a practical abbreviated MR protocol for clinical use that integrates these three MR biomarkers into a single simplified MR assessment. Multiparametric MR techniques provide accurate and reliable non-invasive detection and quantification of liver fat, iron, and fibrosis. These techniques can be combined in a single abbreviated MR "Triple Screen" assessment to offer a more complete metabolic imaging profile of CLD.

Quantitative Analysis of Liver Iron Deposition Based on Dual-Energy CT in Thalassemia Patients.

Background: To explore the feasibility and accuracy of liver iron deposition based on dual-energy CT in thalassemia patients. Materials and methods: 105 thalassemia patients were examined with dual-energy CT and MR liver scanning. Dual-energy CT was performed to measure CT values on 80kVp, 140kVp, and virtual iron content (VIC) imaging; ΔH was figured out by the difference in CT values between 80kVp and 140kVp. Using the liver iron concentration (LIC) obtained by FerriScan as a gold standard, the correlation between CT measurements and LIC was evaluated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance for dual-energy CT in liver iron quantification and stratification. Results: The correlation analysis between CT measurements and LIC showed that 80kVp, 140kVp, VIC, and ΔH all had a high positive correlation with LIC (P<0.001). The correlation analysis among different degree groups of VIC, ΔH, and LIC showed that the normal, moderate, and severe groups of VIC and ΔH had moderate or high positive correlations with that of LIC (P<0.01), but the mild group had no correlation (P>0.05). ROC analysis revealed that the corresponding optimal cutoff value of VIC was -2.8, 6.3,11.9 HU (corresponds to 3.2,7.0,15.0 mg/g dry weight) respectively, while the ΔH were 5.1, 8.4, 17.8HU, respectively. The area under the receiver operating characteristic curves (AUCs) for both VIC and ΔH increased with LIC thresholds. Conclusion: Dual-energy CT can accurately quantify and stratify liver iron deposition, contributing to predicting the status of liver iron deposition in thalassemia patients.

Hemosiderosis in chronic dialysis patients: Monitoring the response to deferasirox by quantitative hepatic magnetic resonance imaging.

INTRODUCTION: Hemosiderosis of chronic dialysis has always been a frequent phenomenon in dialysis; formerly related to blood transfusions before the advent of Erythropoiesis Stimulating Agents (ESA), it is currently in connection with the use of massive doses of injectable iron, to ensure the full therapeutic efficacy of ESA. Few studies have looked at the therapeutic aspect of iron chelators in the dialysis population. METHODS: We followed 31 dialysis patients treated for secondary hemosiderosis with deferasirox (DFX) at the dose 10 mg/kg/day, by hepatic MRI from September 2017 to September 2021, in order to evaluate the efficacy of iron chelators on the reduction of liver iron concentration (LIC). The diagnosis of hemosiderosis was carried for a value of the LIC > 50 µmol/g of dry liver. RESULTS: Chelation resulted in a significant reduction in liver iron burden as measured by liver MRI: (201.4 ± 179.9 vs. 122.6 ± 154.3 µmol/g liver) (p = 0.000) and in mean ferritin level: (2058.8 ± 2004.9 vs. 644.2 ± 456.6 ng/mL) (p = 0.002). A gain of 1.1 g/dL in mean hemoglobin level: (10.5 ± 1.6 vs. 11.6 ± 2.0 g/dL) (p = 0.006). A significant increase in mean albumin level: (43 ± 5.5 to 46.2 ± 6.1 g/L) (p = 0.04). The therapeutic response was clearly influenced by the cause of overload, longer in polytransfused patients (p = 0.023) and the degree of overload assessed by MRI (p = 0.003) and ferritin level (p = 0.04). CONCLUSION: DFX, prescribed at a dose of 10 mg/kg/day, resulted in a significant reduction in hepatic iron burden as measured by liver MRI and ferritin. The therapeutic response was clearly influenced by blood transfusions and the degree of iron overload.

Cascade of Denoising and Mapping Neural Networks for MRI R2* Relaxometry of Iron-Loaded Liver.

MRI of effective transverse relaxation rate (R2*) measurement is a reliable method for liver iron concentration quantification. However, R2* mapping can be degraded by noise, especially in the case of iron overload. This study aimed to develop a deep learning method for MRI R2* relaxometry of an iron-loaded liver using a two-stage cascaded neural network. The proposed method, named CadamNet, combines two convolutional neural networks separately designed for image denoising and parameter mapping into a cascade framework, and the physics-based R2* decay model was incorporated in training the mapping network to enforce data consistency further. CadamNet was trained using simulated liver data with Rician noise, which was constructed from clinical liver data. The performance of CadamNet was quantitatively evaluated on simulated data with varying noise levels as well as clinical liver data and compared with the single-stage parameter mapping network (MappingNet) and two conventional model-based R2* mapping methods. CadamNet consistently achieved high-quality R2* maps and outperformed MappingNet at varying noise levels. Compared with conventional R2* mapping methods, CadamNet yielded R2* maps with lower errors, higher quality, and substantially increased efficiency. In conclusion, the proposed CadamNet enables accurate and efficient iron-loaded liver R2* mapping, especially in the presence of severe noise.

Estimation of iron overload with T2*MRI in children treated for hematological malignancies.

Iron overload may contribute to long-term complications in childhood cancer survivors. There are limited reports of assessment of tissue iron overload in childhood leukemia by magnetic resonance imaging (MRI). A cross-sectional, observational study in children treated for hematological malignancy was undertaken. Patients ≥6 months from the end of therapy who had received ≥5 red-cell transfusions were included. Iron overload was estimated by serum ferritin (SF) and T2*MRI. Forty-five survivors were enrolled among 431 treated for hematological malignancies. The median age at diagnosis was 7-years. A median of 8 red-cell units was transfused. The median duration from the end of treatment was 15 months. An elevated SF (>1,000 ng/ml), elevated liver iron concentration (LIC) and myocardial iron concentration (MIC) were observed in 5 (11.1%), 20 (45.4%), and 2 (4.5%) patients, respectively. All survivors with SF >1,000 ng/ml had elevated LIC. The LIC correlated with SF (p < 0.001). MIC lacked correlation with SF or LIC. Factors including the number of red-cell units transfused and duration from the last transfusion were associated with elevated SF (p = 0.001, 0.002) and elevated LIC (p = 0.012, 0.005) in multiple linear regression. SF >595 ng/ml predicted elevated LIC with a sensitivity of 85% and specificity of 91.6% (AUC 91.2%). A cutoff >9 units of red cell transfusions had poor sensitivity and specificity of 70% and 75% (AUC 76.6%) to predict abnormal LIC. SF >600 ng/ml is a robust tool to predict iron overload, and T2*MRI should be considered in childhood cancer survivors with SF exceeding 600 ng/ml.

Comparison of R1ρ Imaging Between Rapid Acquisition with Relaxation Enhancement (RARE) and Ultrashort TE (UTE) Sequence in the Assessment of Rat Liver Iron Overload at 11.7T.

INTRODUCTION: Since the most prominent effect of iron is increasing R2* and R2 relaxation rates, the iron-overload liver shows little signal with conventional T1ρ sequences like RARE. Whereas UTE MR imaging sequences can detect the signal from short T2/T2* relaxation components in tissues. This study aims to evaluate the difference in R1ρ profiles and compare the correlations between RARE-based and UTE-based sequences with LIC in assessing rat liver iron overload. METHODS: Iron dextran (Sigma, 100 mg Fe/ml) was injected into thirty-five rats (25-100 mg/kg body weight), while the rats in the control group were injected with saline (n=5). The liver specimen was taken after one week. A portion of the largest hepatic lobe was extracted to quantify the LIC by inductively coupled plasma, and the remaining liver tissue was stored in 4% buffered paraformaldehyde for 24 h before MRI. Spin-lock preparation with RARE readout and 2D UTE readout pulses were developed to quantify R1ρ on a Bruker 11.7T MR system. RESULTS: The mean R1ρ value of the rat liver with UTE-based R1ρ sequence was significantly higher compared to the RARE-based R1ρ sequence (p<0.001). Spearman's correlation analysis (two-tailed) indicated that the R1ρ values were significantly correlated with LIC for both UTE-R1ρ and RARER1ρ sequences (r = 0.727, P < 0.001, and r = 0.712, P < 0.001, respectively). CONCLUSION: The current study adds to evidence that there is a correlation between iron concentration and R1ρ. Moreover, the UTE-based R1ρ sequence is more sensitive to the liver iron than the RAREbased R1ρ sequence. R1ρ might serve as a complementary imaging biomarker for liver iron overload quantification.

Deferasirox versus deferoxamine in managing iron overload in patients with Sickle Cell Anaemia: a systematic review and meta-analysis.

OBJECTIVES: To examine the efficacy of deferasirox (DFX) by comparison with deferoxamine (DFO) in managing iron overload in patients with sickle cell anaemia (SCA). METHODS: Online databases were systematically searched for studies published from January 2007 to July 2022 that had investigated the efficacy of DFX compared with DFO in managing iron overload in patients with SCA. RESULTS: Of the 316 articles identified, three randomized clinical trials met the inclusion criteria. Meta-analysis of liver tissue iron concentration (LIC) showed that iron overload was not significantly higher in the DFX group compared with DFO group (WMD, -1.61 mg Fe/g dw (95% CI -4.42 to 1.21). However, iron overload as measured by serum ferritin was significantly lower in DFO compared with DFX group (WMD, 278.13 µg/l (95% CI 36.69 to 519.57). Although meta-analysis was not performed on myocardial iron concentration due to incomplete data, the original report found no significant difference between DFX and DFO. CONCLUSION: While limited by the number of studies included in this meta-analysis, overall, the results tend to show that DFX was as effective as DFO in managing iron overload in patients with SCA.