Quantitative Susceptibility Mapping versus R2*-based Histogram Analysis for Evaluating Liver Fibrosis: Preliminary Results

Evaluating the Potential of Quantitative Susceptibility Mapping for Detecting Iron Deposition of Renal Fibrosis in a Rabbit Model

BACKGROUND

As ferroptosis is a key factor in renal fibrosis (RF), iron deposition monitoring may help evaluating RF. The capability of quantitative susceptibility mapping (QSM) for detecting iron deposition in RF remains uncertain.

PURPOSE

To investigate the potential of QSM to detect iron deposition in RF.

STUDY TYPE

Animal model.

ANIMAL MODEL

Eighty New Zealand rabbits were randomly divided into control (N = 10) and RF (N = 70) groups, consisting of baseline, 7, 14, 21, and 28 days (N = 12 in each), and longitudinal (N = 10) subgroups. RF was induced via unilateral renal arteria stenosis.

FIELD STRENGTH/SEQUENCE

3 T, QSM with gradient echo, arterial spin labeling with gradient spin echo.

ASSESSMENT

Bilateral kidney QSM values (χ) in the cortex (χCO) and outer medulla (χOM) were evaluated with histopathology.

STATISTICAL TESTS

Analysis of variance, Kruskal-Wallis, Spearman's correlation, and the area under the receiver operating characteristic curve (AUC). P < 0.05 was significant.

RESULTS

In fibrotic kidneys, χCO decreased at 7 days ([-6.69 ± 0.98] × 10-2 ppm) and increased during 14-28 days ([-1.85 ± 2.11], [0.14 ± 0.58], and [1.99 ± 0.60] × 10-2 ppm, respectively), while the χOM had the opposite trend. Both significantly correlated with histopathology (|r| = 0.674-0.849). AUC of QSM for distinguishing RF degrees was 0.692-0.993. In contralateral kidneys, the χCO initially decreased ([-6.67 ± 0.84] × 10-2 ppm) then recovered to baseline ([-4.81 ± 0.89] × 10-2 ppm), while the χOM at 7-28 days ([2.58 ± 1.40], [2.25 ± 1.83], [2.49 ± 2.11], [2.43 ± 1.32] × 10-2 ppm, respectively) were significantly higher than baseline ([0.54 ± 0.18] × 10-2 ppm).

DATA CONCLUSION

Different iron deposition patterns were observed in RF with QSM values, suggesting the potential of QSM for iron deposition monitoring in RF.

PLAIN LANGUAGE SUMMARY

Renal fibrosis (RF) is a common outcome in most kidney diseases, leading to scarring and loss of kidney function. Increasing evidence suggests that abnormal iron metabolism plays an important role in RF. This study used a technique called quantitative susceptibility mapping (QSM) to measure kidney iron levels in rabbits with RF. Specifically, rabbits with advanced RF exhibited higher kidney iron concentrations, and moderate to strong correlations between QSM values and histopathology demonstrated that QSM could accurately detect changes in iron levels and assess RF severity. Overall, QSM shows promise as a tool for monitoring iron deposition in RF progression.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 3.

Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy

OBJECTIVES

This study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy.

METHODS

We conducted a retrospective analysis involving 202 patients with surgically confirmed single HCC having undergone preoperative magnetic resonance imaging between 2018 and 2021 at two different institutions. 126 patients from Institution 1 were assigned to the training set, and 76 patients from Institution 2 were assigned to the validation set. A least absolute shrinkage and selection operator (LASSO) regularization was conducted to operate a logistic regression, then features were identified to construct a radiomic score (Rad-score). Uni- and multi-variable tests were used to assess the correlations of clinicopathological features and Rad-score with ER. We then established a combined model encompassing the optimal Rad-score and clinical-pathological risk factors. Additionally, we formulated and validated a predictive nomogram for predicting ER in HCC. The nomogram's discrimination, calibration, and clinical utility were thoroughly evaluated.

RESULTS

Multivariable logistic regression revealed the Rad-score, microvascular invasion (MVI), and α fetoprotein (AFP) level > 400 ng/mL as significant independent predictors of ER in HCC. We constructed a nomogram based on these significant factors. The areas under the receiver operator characteristic curve of the nomogram and precision-recall curve were 0.901 and 0.753, respectively, with an F1 score of 0.831 in the training set. These values in the validation set were 0.827, 0.659, and 0.808.

CONCLUSION

The nomogram that integrates the radiomic score, MVI, and AFP demonstrates high predictive efficacy for estimating the risk of ER in HCC. It facilitates personalized risk classification and therapeutic decision-making for HCC patients.

Magnetic susceptibility and R2*-based texture analysis for evaluating liver fibrosis in chronic liver disease

PURPOSE

To explore potential feasibility of texture features in magnetic susceptibility and R2* maps for evaluating liver fibrosis.

METHODS

Thirty-one patients (median age 46 years; 22 male) with chronic liver disease were prospectively recruited and underwent magnetic resonance imaging (MRI), blood tests, and liver biopsy. Susceptibility and R2* maps were obtained using a 3-dimensional volumetric interpolated breath-hold examination sequence with a 3T MRI scanner. Texture features, including histogram, gray-level co-occurrence matrix (GLCM), gray-level dependence matrix (GLDM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and neighboring gray tone difference matrix (NGTDM) features, were extracted. Texture features and blood test results of non-significant (Ishak-F < 3) and significant fibrosis patients (Ishak-F ≥ 3) were compared, and correlations with Ishak-F stages were analyzed. Areas under the curve (AUCs) were calculated to determine the efficacy for evaluating liver fibrosis.

RESULTS

Nine texture features of susceptibility maps and 19 features of R2* maps were significantly different between non-significant and significant fibrosis groups (all P < 0.05). Large dependence high gray-level emphasis (LDHGLE) of GLDM and long run high gray-level emphasis (LRHGLE) of GLRLM in R2* maps showed significantly negative and good correlations with Ishak-F stages (r = -0.616, P < 0.001; r = -0.637, P < 0.001). Busyness (NGTDM) in susceptibility maps, LDHGLE of GLDM and LRHGLE of GLRLM in R2* maps yield the highest AUCs (AUC = 0.786, P = 0.007; AUC = 0.807, P = 0.004; AUC = 0.819, P = 0.003).

CONCLUSION

Texture characteristics of susceptibility and R2* maps revealed possible staging values for liver fibrosis. Susceptibility and R2*-based texture analysis may be a useful and noninvasive method for staging liver fibrosis.

Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study

OBJECTIVES

To investigate the value of R2* mapping-based radiomics nomograms in staging liver fibrosis in patients with chronic hepatitis B.

METHODS

Between January 2020 and December 2020, 151 patients with chronic hepatitis B were randomly divided into training (n = 103) and validation (n = 48) cohorts. From January to February 2021, 58 patients were included in a test cohort. Radiomics features were selected using the interclass correlation coefficient and least absolute shrinkage and selection operator method. Three radiomics nomograms, combining the radiomics score (Radscore) derived from R2* mapping and clinical variables, were used for staging significant and advanced fibrosis, and cirrhosis. Performance of the model was evaluated using the AUC. The utility and clinical benefits were evaluated using the continuous net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).

RESULTS

The Radscore calculated by 12 radiomics features and independent factors (laminin and platelet) of advanced fibrosis were used to construct the radiomics nomograms. In the test cohort, the AUCs of the radiomics nomograms for staging significant fibrosis, advanced fibrosis, and cirrhosis were 0.738 (95% confidence interval [CI]: 0.604-0.872), 0.879 (95% CI: 0.779-0.98), and 0.952 (95% CI: 0.878-1), respectively. NRI, IDI, and DCA confirmed that radiomics nomograms demonstrated varying degrees of clinical benefit and improvement for advanced fibrosis and cirrhosis, but not for significant fibrosis.

CONCLUSIONS

Radiomics nomograms combined with R2* mapping-based Radscore, laminin, and platelet have value in staging advanced fibrosis and cirrhosis but limited value for staging significant fibrosis.

KEY POINTS

• Laminin and platelets were independent predictors of advanced fibrosis. • Radiomics analysis based on R2* mapping was beneficial for evaluating advanced fibrosis and cirrhosis. • It was difficult to distinguish significant fibrosis using a radiomics nomogram, which is possibly due to the complex pathological microenvironment of chronic liver diseases.

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.

Quantitative Susceptibility Mapping: Basic Methods and Clinical Applications

Quantitative susceptibility mapping (QSM), one of the advanced MRI techniques for evaluating magnetic susceptibility, offers precise quantitative measurements of spatial distributions of magnetic susceptibility. Magnetic susceptibility describes the magnetizability of a material to an applied magnetic field and is a substance-specific value. Recently, QSM has been widely used to estimate various levels of substances in the brain, including iron, hemosiderin, and deoxyhemoglobin (paramagnetism), as well as calcification (diamagnetism). By visualizing iron distribution in the brain, it is possible to identify anatomic structures that are not evident on conventional images and to evaluate various neurodegenerative diseases. It has been challenging to apply QSM in areas outside the brain because of motion artifacts from respiration and heartbeats, as well as the presence of fat, which has a different frequency to the proton. In this review, the authors provide a brief overview of the theoretical background and analyze methods of converting MRI phase images to QSM. Moreover, we provide an overview of the current clinical applications of QSM. Online supplemental material is available for this article. ^©RSNA, 2022.