Quantitative magnetic resonance imaging evaluation of hepatic fat content with iron deposition: will it be disturbed?

Application of the magnetic resonance 3D multiecho Dixon sequence for quantifying hepatic iron overload and steatosis in patients with thalassemia

OBJECTIVE

To investigate the feasibility and diagnostic efficacy of a 3D multiecho Dixon (qDixon) research application for simultaneously quantifying the liver iron concentration (LIC) and steatosis in thalassemia patients.

MATERIALS AND METHODS

This prospective study enrolled participants with thalassemia who underwent 3 T MRI of the liver for the evaluation of hepatic iron overload. The imaging protocol including qDixon and conventional T2* mapping based on 2D multiecho gradient echo (ME GRE) sequences respectively. Regions of interest (ROIs) were drawn in the liver on the qDixon maps to obtain R2* and proton density fat fraction (PDFF). The reference R2* value was measured and calculated on conventional T2* mapping using the CMRtools software. Correlation analysis, Linear regression analysis, and Bland-Altman analysis were performed.

RESULTS

84 patients were finally included in this study. The median R2*-ME-GRE was 366.97 (1/s), range [206.68 (1/s), 522.20 (1/s)]. 8 patients had normal hepatic iron deposition, 16 had Insignificant, 42 had mild, 18 had moderate. The median of R2*-qDixon was 376.88 (1/s) [219.33 (1/s), 491.75 (1/s)]. A strong correlation was found between the liver R2*-qDixon and the R2*-ME-GRE (r = 0.959, P < 0.001). The median value of PDFF was 1.76% (1.10%, 2.95%). 8 patients had mild fatty liver, and 1 had severe fatty liver.

CONCLUSION

MR qDixon research sequence can rapidly and accurately quantify liver iron overload, that highly consistent with the measured via conventional GRE sequence, and it can also simultaneously detect hepatic steatosis, this has great potential for clinical evaluation of thalassemia patients.

Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma

Objectives

The present study aims at establishing a noninvasive and reliable model for the preoperative prediction of glypican 3 (GPC3)-positive hepatocellular carcinoma (HCC) based on multiparametric magnetic resonance imaging (MRI) and clinical indicators.

Methods

As a retrospective study, the subjects included 158 patients from two institutions with surgically-confirmed single HCC who underwent preoperative MRI between 2020 and 2022. The patients, 102 from institution I and 56 from institution II, were assigned to the training and the validation sets, respectively. The association of the clinic-radiological variables with the GPC3 expression was investigated through performing univariable and multivariable logistic regression (LR) analyses. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (GPC3-negative HCCs) in the training set, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. Next, a prediction nomogram was developed and validated for patients with GPC3-positive HCC. The performance of the nomogram was evaluated through examining its calibration and clinical utility.

Results

Based on the results obtained from multivariable analyses, alpha-fetoprotein levels > 20 ng/mL, 75th percentile ADC value < 1.48 ×103 mm2/s and R2* value ≥ 38.6 sec-1 were found to be the significant independent predictors of GPC3-positive HCC. The SMOTE-LR model based on three features achieved the best predictive performance in the training (AUC, 0.909; accuracy, 83.7%) and validation sets (AUC, 0.829; accuracy, 82.1%) with a good calibration performance and clinical usefulness.

Conclusions

The nomogram combining multiparametric MRI and clinical indicators is found to have satisfactory predictive efficacy for preoperative prediction of GPC3-positive HCC. Accordingly, the proposed method can promote individualized risk stratification and further treatment decisions of HCC patients.

Quantitative MRI of diffuse liver diseases: techniques and tissue-mimicking phantoms

Quantitative magnetic resonance imaging (MRI) techniques are emerging as non-invasive alternatives to biopsy for assessment of diffuse liver diseases of iron overload, steatosis and fibrosis. For testing and validating the accuracy of these techniques, phantoms are often used as stand-ins to human tissue to mimic diffuse liver pathologies. However, currently, there is no standardization in the preparation of MRI-based liver phantoms for mimicking iron overload, steatosis, fibrosis or a combination of these pathologies as various sizes and types of materials are used to mimic the same liver disease. Liver phantoms that mimic specific MR features of diffuse liver diseases observed in vivo are important for testing and calibrating new MRI techniques and for evaluating signal models to accurately quantify these features. In this study, we review the liver morphology associated with these diffuse diseases, discuss the quantitative MR techniques for assessing these liver pathologies, and comprehensively examine published liver phantom studies and discuss their benefits and limitations.

Simultaneous liver steatosis, fibrosis and iron deposition quantification with mDixon quant based on radiomics analysis in a rabbit model

PURPOSE

To evaluate the feasibility of simultaneous quantification of liver fibrosis, liver steatosis and abnormal iron deposition using mDixon Quant based on radiomics analysis, and to eliminate the interference among different histopathologic features.

METHODS

One hundred and twenty rabbits that were administered CCl4 for 4-16 weeks and a cholesterol rich diet for the initial 4 weeks in the experimental group and 20 rabbits in the control group were examined using mDixon. Radiomics features of the whole liver were extracted from PDFF and R2* and radiomics models for discriminating steatosis: S0-S1 vs. S2-S4, fibrosis: F0-F2 vs. F3-F4 and iron deposition: normal vs. abnormal were constructed respectively and evaluated using receiver operating characteristic (ROC) curves with the histopathological results as reference standard. Combined corrected models merging the radscore and the other two histopathologic features were evaluated using multiple logistic regression analyses and compared with radiomics models.

RESULTS

The area under the ROC curve (AUC) of the radiomics model with PDFF features was 0.886 and 0.843 in the training and the test set, respectively, for the diagnosis of liver steatosis grade S0-1 and S2-S4. The radiomics model based on R2* features were 0.815 and 0.801 for distinguishing F0-F2 and F3-F4 and 0.831 and 0.738 for discriminating abnormal iron deposition in the training and test set, respectively. The corrected model for liver steatosis and fibrosis (0.944 and 0.912 in the test set) outperformed the radiomics models by eliminating the interference of histopathologic features(P < 0.05), but had comparable diagnostic performance for abnormal iron deposition(P > 0.05).

CONCLUSIONS

It is feasible for mDixon to simultaneously quantify whole liver steatosis, fibrosis and iron deposition based on radiomics analysis. It is valuable to minimize the interference of different pathological features for the assessment of liver steatosis and fibrosis.

Evaluation of Glypican-3 Expression in Hepatocellular Carcinoma by Using IDEAL IQ Magnetic Resonance Imaging

RATIONALE AND OBJECTIVES

To investigate the value of iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL IQ) and gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for evaluating Glypican-3 (GPC3) expression in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Seventy-six patients with histopathologic diagnosis of HCC were retrospectively included in this study. In all patients IDEAL IQ and Gd-EOB-DTPA-enhanced MRI were performed preoperatively using a 3 T MRI system. For an identical slice through the liver of each patient a region of interest was drawn on the tumor in the hepatobiliary phase image and copied to the R2* map and fat fraction map produced by IDEAL IQ. A Mann-Whitney U test was used to compare the region of interest values of R2*, fat fraction and uptake of Gd-EOB-DTPA values between patients with positive and negative GPC3 expression HCC. Receiver operating characteristic analysis was used to determine the diagnostic performances of each of the MRI parameters in evaluating GPC3 expression and histological grade in HCC.

RESULTS

R2* value was significantly higher in cases of positive than negative GPC3 expression HCCs (p < 0.001), whereas there were no significant differences in fat fraction and uptake of Gd-EOB-DTPA between the 2 groups (both p > 0.05). R2* value had higher areas under receiver operating characteristic (0.881), sensitivity (85.96%), and specificity (84.21%) compared to the fat fraction and uptake of Gd-EOB-DTPA.

CONCLUSION

R2* value yielded from IDEAL IQ could reliably predict GPC3 expression in HCC prior to surgery.

Iron-containing pathologies of the spleen: magnetic resonance imaging features with pathologic correlation

Systemic and non-systemic pathologies that involve iron deposition within the spleen have characteristic features on MRI due to the susceptibility properties of deposited iron, or hemosiderin. These lesions will have signal loss on longer echo sequences due to the T2* effect when evaluated with dual-echo gradient-echo sequences. The pathophysiology of systemic and localized iron sequestration disease processes can elucidate an underlying diagnosis based on these imaging features in conjunction with clinical information.

Accuracies of Chemical Shift In/Opposed Phase and Chemical Shift Encoded Magnetic Resonance Imaging to Detect Intratumoral Fat in Hepatocellular Carcinoma

BACKGROUND

Magnetic Resonance Imaging (MRI) being a noninvasive modality may help in preoperative evaluation of intratumoral fat in hepatocellular carcinoma (HCC) using chemical shift encoded (CSE) MRI and in-/opposed-phase (IOP) imaging sequences.

PURPOSE

To compare the diagnostic accuracy of chemical shift encoded fat fraction at three different flip angles (FAs) using quantitative chemical shift encoded MRI (CSE-MRI) with in-/opposed phase (IOP) imaging to evaluate intratumoral fat in HCC.

STUDY TYPE

Retrospective.

POPULATION

Eighty-six patients with 87 pathology proven HCCs.

FIELD STRENGTH/SEQUENCE

IOP (LAVA-Flex) and CSE-MRI (IDEAL IQ) a three-dimensional spoiled gradient-echo pulse sequences acquired at 3 T.

ASSESSMENT

Regions of interest (ROIs) were manually drawn by two observers in the tumors to measure mean fat fractions. Surgical specimens were reassessed for intratumoral fat content. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were assessed for CSE-MRI sequence at FA 3°, 8°, and 9°.

STATISTICAL TESTS

Intraclass correlation coefficient (ICC) was expressed in terms of inter- and intra-observer agreements. Receiver operating characteristic curve analysis was performed for the diagnostic performance followed by combined metric of both. SNR/CNR were analyzed by Kruskal-Wallis test.

RESULTS

Excellent inter- and intra-observer agreements (ICC >0.95, P < 0.001) were observed for both IOP and CSE-MRI. IOP (86.4%) showed higher sensitivity than CSE-MRI at FA 3° (72.5%), FA 8° (76.4%) and FA 9° (76.3%). In contrast, the specificity for CSE-MRI at FA 3° (86%), FA 8° (87%), and FA 9° (87%) were greater than IOP (72%). A combined metric of IOP and CSE-MRI derived fat fractions at FA 8° gave highest AUC of 87% and accuracy of 86%. SNR and CNR for CSE-MRI were significantly higher at FA 8° and FA 9° than FA 3° (P < 0.05).

DATA CONCLUSION

IOP and quantitative CSE-MRI are both feasible methods to detect intratumoral fat in HCC with higher accuracy and SNR for CSE-MRI at FA 8° and 9°.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Applications of the Dixon technique in the evaluation of the musculoskeletal system

The acquisition of images with suppression of the fat signal is very useful in clinical practice and can be achieved in a variety of sequences. The Dixon technique, unlike other fat suppression techniques, allows the signal of fat to be suppressed in the postprocessing rather than during acquisition, as well as allowing the visualization of maps showing the distribution of water and fat. This review of the Dixon technique aims to illustrate the basic physical principles, to compare the technique with other magnetic resonance imaging sequences for fat suppression or fat quantification, and to describe its applications in the study of diseases of the musculoskeletal system. Many variants of the Dixon technique have been developed, providing more consistent separation of the fat and water signals, as well as allowing correction for many confounding factors. It allows homogeneous fat suppression, being able to be acquired in combination with several other sequences, as well as with different weightings. The technique also makes it possible to obtain images with and without fat suppression from a single acquisition. In addition, the Dixon technique can be used as a quantitative method, allowing the proportion of tissue fat to be determined, and, in more updated versions, can quantify tissue iron.

Quantification of Hepatic Fat Fraction in Patients With Nonalcoholic Fatty Liver Disease: Comparison of Multimaterial Decomposition Algorithm and Fat (Water)-Based Material Decomposition Algorithm Using Single-Source Dual-Energy Computed Tomography

METHODS

Hepatic fat fractions were quantified by noncontrast (HFFnon-CE) and contrast-enhanced single-source dual-energy computed tomography in arterial phase (HFFAP), portal venous phase (HFFPVP) and equilibrium phase (HFFEP) using MMD in 19 nonalcoholic fatty liver disease patients. The fat concentration was measured on fat (water)-based images. As the standard of reference, magnetic resonance iterative decomposition of water and fat with echo asymmetry and least-squares estimation-iron quantification images were reconstructed to obtain HFF (HFFIDEAL-IQ).

RESULTS

There was a strong correlation between HFFnon-CE, HFFAP, HFFPVP, HFFEP, fat concentration and HFFIDEAL-IQ (r = 0.943, 0.923, 0.942, 0.952, and 0.726) with HFFs having better correlation with HFFIDEAL-IQ. Hepatic fat fractions did not significantly differ across scanning phases. The HFFs of 3-phase contrast-enhanced computed tomography had a good consistency with HFFnon-CE.

CONCLUSIONS

Hepatic fat fraction using MMD has excellent correlation with that of magnetic resonance imaging, is independent of the computed tomography scanning phases, and may be used as a routine technique for quantitative assessment of HFF.