Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model

BACKGROUND

Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF).

PURPOSE

To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model.

METHODS

Thirty mice with carbon tetrachloride (CCl4)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000 s/mm2) diffusion-weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2-5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF.

RESULTS

According to BIC results, a 4-compartment RSI model (RSI4) with optimal ADCs of 0.471 × 10-3, 1.653 × 10-3, 9.487 × 10-3, and > 30 × 10-3, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C1 and C3 compartments were observed between LF and control groups (P = 0.018 and 0.003, respectively). ROC analysis showed that RSI4-C3 was the most effective single diffusion parameter for characterizing LF (AUC = 0.876, P = 0.003). Furthermore, the combination of ADC values and RSI4-C3 value increased the diagnosis performance significantly (AUC = 0.894, P = 0.002).

CONCLUSION

The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI4-C3 showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI4-C3 values further improved the discrimination performance.

Using Texture Analysis of Neck Computed Tomography Images to Differentiate Primary Hyperparathyroidism From Normal Controls

OBJECTIVE

To investigate the utility of texture analysis in detecting osseous changes associated with hyperparathyroidism on neck CT examinations compared with control patients and to explore the best regions in the head and neck to evaluate changes in the trabecular architecture secondary to hyperparathyroidism.

METHODS

Patients with hyperparathyroidism who underwent a 4D CT of the neck with contrast were included in this study. Age-matched control patients with no history of hyperparathyroidism who underwent a contrast-enhanced neck CT were also included. Mandibular condyles, bilateral mandibular bodies, the body of the C4 vertebra, the manubrium of the sternum, and bilateral clavicular heads were selected for analysis, and oval-shaped regions of interest were manually placed. These segmented areas were imported into an in-house developed texture analysis program, and 41 texture analysis features were extracted. A mixed linear regression model was used to compare differences in the texture analysis features contoured at each of the osseous structures between patients with hyperparathyroidism and age-matched control patients.

RESULTS

A total of 30 patients with hyperparathyroidism and 30 age-matched control patients were included in this study. Statistically significant differences in texture features between patients with hyperparathyroidism and control patients in all 8 investigated osseous regions. The sternum showed the greatest number of texture features with statistically significant differences between these groups.

CONCLUSIONS

Some CT texture features demonstrated statistically significant differences between patients with hyperparathyroidism and control patients. The results suggest that texture features may discriminate changes in the osseous architecture of the head and neck in patients with hyperparathyroidism.

Clinical Application of Quantitative MR Imaging in Nonalcoholic Fatty Liver Disease

Viral hepatitis was previously the most common cause of chronic liver disease. However, in recent years, nonalcoholic fatty liver disease (NAFLD) cases have been increasing, especially in developed countries. NAFLD is histologically characterized by fat, fibrosis, and inflammation in the liver, eventually leading to cirrhosis and hepatocellular carcinoma. Although biopsy is the gold standard for the assessment of the liver parenchyma, quantitative evaluation methods, such as ultrasound, CT, and MRI, have been reported to have good diagnostic performances. The quantification of liver fat, fibrosis, and inflammation is expected to be clinically useful in terms of the prognosis, early intervention, and treatment response for the management of NAFLD. The aim of this review was to discuss the basics and prospects of MRI-based tissue quantifications of the liver, mainly focusing on proton density fat fraction for the quantification of fat deposition, MR elastography for the quantification of fibrosis, and multifrequency MR elastography for the evaluation of inflammation.

Cone-beam computed tomography texture analysis can help differentiate odontogenic and non-odontogenic maxillary sinusitis

Purpose

This study aimed to assess texture analysis (TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis (OS and NOS, respectively).

Materials and Methods

CBCT images of 40 patients diagnosed with OS (N=20) and NOS (N=20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α=5%).

Results

The results showed statistically significant differences (P<0.05) between the OS and NOS patients regarding 3 TA parameters. NOS patients presented higher values for contrast, while OS patients presented higher values for correlation and inverse difference moment. Greater textural homogeneity was observed in the OS patients than in the NOS patients, with statistically significant differences in standard deviations between the groups for correlation, sum of squares, sum of entropy, and entropy.

Conclusion

TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment.

Risk assessment of external apical root resorption associated with orthodontic treatment using computed tomography texture analysis

OBJECTIVES

This study aimed to quantitatively assess maxillary central incisor roots using pre-orthodontics computed tomography (CT) texture analysis as part of a radiomics quantitative analysis.

METHODS

This retrospective case-control study included 16 patients with external apical root resorption (EARR) and 16 age- and sex-matched patients without EARR, after orthodontic treatment who underwent pre-orthodontics CT for jaw deformities. All patients were treated with a fixed orthodontic appliance before and after surgical orthodontic treatment. EARR was defined as root resorption ≥ 2 mm of the left and right maxillary central incisors on CT images more than 2 years after the start of orthodontic treatment. Texture features of the maxillary central incisor with and without EARR after orthodontic treatment were analyzed using the open-access software, MaZda Ver. 3.3. Ten texture features were selected using the Fisher method in MaZda from 279 original parameters, which were calculated for each of the maxillary central incisors with and without EARR. The results were tested using the Student's t test, Welch's t test, or Mann-Whitney U test.

RESULTS

Four gray-level run length matrix features and six gray-level co-occurrence matrix features displayed significant differences between both the groups (p < 0.01).

CONCLUSIONS

CT texture analysis was able to quantitatively assess maxillary central incisor roots and distinguish between maxillary central incisor roots with and without EARR. CT texture analysis may be a useful method for predicting EARR after orthodontic treatment.

Noninvasive assessment of renal function and fibrosis in CKD patients using histogram analysis based on diffusion kurtosis imaging

PURPOSE

To investigate the potential of histogram analysis based on diffusion kurtosis imaging (DKI) in evaluating renal function and fibrosis associated with chronic kidney disease (CKD).

MATERIALS AND METHODS

Thirty-six CKD patients were enrolled, and DKI was performed in all patients before the renal biopsy. The histogram parameters of diffusivity (D) and kurtosis (K) were obtained using FireVoxel. The histogram parameters between the stable [estimated glomerular filtration rate (eGFR) ≥ 60 ml/min/1.73 m²] and impaired (eGFR < 60 ml/min/1.73 m²) eGFR group were compared. Besides, patients were classified into mild, moderate, and severe fibrosis group using a semi-quantitative standard. The correlations of histogram parameters with eGFR and fibrosis scores were investigated and the diagnostic performances of histogram parameters in assessing renal dysfunction and fibrosis were analyzed. The added value of combination of most significant parameter with 24 h urinary protein (24 h-UPRO) in evaluating fibrosis was also explored.

RESULTS

Seven D histogram parameters in cortex (mean, median, 10th, 25th, 75th, 90th percentiles and entropy), two D histogram parameters in medulla (75th, 90th percentiles), seven K histogram parameters in cortex (mean, min, median, 10th, 25th, 75th, 90th percentiles) and three K histogram parameters in medulla (mean, median, 25th percentile) were significantly different between the two groups. The D_mean of cortex was the most relevant parameter to eGFR (r = 0.648, P < 0.001) and had the largest area under the curve (AUC) for differentiating the stable from impaired eGFR group [AUC = 0.889; 95% confidence interval (CI) 0.728-0.970]. The K_90th of cortex presented the strongest correlation with fibrosis scores (r = 0.575, P < 0.001) and achieved the largest AUC for distinguishing the mild from moderate to severe fibrosis group (AUC = 0.849, 95% CI 0.706-0.993). Combining the K_90th in cortex with 24 h-UPRO gained statistically higher AUC value (AUC = 0.880, 95% CI 0.763-0.996).

CONCLUSION

Histogram analysis based on DKI is practicable for the noninvasive assessment of renal function and fibrosis in CKD patients.

Prospective Evaluation of Virtual MR Elastography With Diffusion-Weighted Imaging in Subjects With Nonalcoholic Fatty Liver Disease

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is increasingly common worldwide and can lead to the development of cirrhosis, liver failure and cancer. Virtual magnetic resonance elastography (VMRE), which is based on a shifted apparent diffusion coefficient (sADC), is a potential noninvasive method to assess liver fibrosis without the specialized hardware and expertise required to implement traditional MR elastography (MRE). Although hepatic steatosis is known to confound ADC measurements, previous studies using VMRE have not corrected for hepatic fat fraction.

PURPOSE

To compare VMRE, corrected for the confounding effects of unsuppressed fat signal, to MRE and biopsy in subjects with suspected NAFLD.

STUDY TYPE

Prospective, cross-sectional.

POPULATION

A total of 49 adult subjects with suspected NAFLD (18 male; median age 55 years, range 33-74 years) who underwent liver biopsy.

FIELD STRENGTH/SEQUENCE

3T, diffusion-weighted spin echo planar, chemical-shift encoded (IDEAL IQ) and MRE sequences.

ASSESSMENT

Two observers drew regions of interest on sADC, proton density fat fraction and MRE-derived stiffness maps. Fat-corrected sADC values were used to calculate the diffusion-based shear modulus according to the VMRE method. Predicted fibrosis stage for MRE and VMRE was determined using previously published cut-off values.

STATISTICAL TESTS

The relationship between VMRE and MRE was assessed with least-squares linear regression (coefficient of determination, R² ). Agreement between MRE and VMRE-predicted fibrosis stage was evaluated with a kappa coefficient and accuracy compared using McNemar's test. A one-way ANOVA determined if the fat-corrected sADC (VMRE) and MRE differed by fibrosis stage. A P value < 0.05 was considered statistically significant.

RESULTS

Least squares regression of VMRE vs. MRE revealed R²  = 0.046 and a slope that was not significantly different from zero (P = 0.14). There was no agreement between MRE and VMRE-predicted fibrosis stage (kappa = -0.01). The proportion of correctly predicted fibrosis stage was significantly higher for MRE compared to VMRE. MRE was significantly associated with fibrosis stage, but fat-corrected sADC was not (P = 0.24).

DATA CONCLUSION

Fat-corrected VMRE was not associated with fibrosis stage in NAFLD. Further investigation is required if VMRE is to be considered in subjects with NAFLD.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)

PURPOSE

To investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC).

METHODS AND MATERIALS

Two hundred and seventy-four eligible patients with ESCC were divided into a training set (n =193) and a validation set (n =81). The least absolute shrinkage and selection operator algorithm (LASSO) was used to select radiomics features. The predictive models were constructed with radiomics features and clinical factors through multivariate logistic regression analysis. The predictive performance and clinical application value of the models were evaluated by area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Delong Test was used to evaluate the differences in AUC among models.

RESULTS

Sixteen and eighteen features were respectively selected from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images. The model established using only clinical factors (Model 1) has an AUC value of 0.655 (95%CI 0.552-0.759) with a sensitivity of 0.585, a specificity of 0.725 and an accuracy of 0.654. The models contained clinical factors with radiomics features of NECT or/and CECT (Model 2,3,4) have significantly improved prediction performance. The values of AUC of Model 2,3,4 were 0.766, 0.811 and 0.809, respectively. It also achieved a great AUC of 0.800 in the model built with only radiomics features derived from NECT and CECT (Model 5). DCA suggested the potential clinical benefit of model prediction of LN metastasis of ESCC. A comparison of the receiver operating characteristic (ROC) curves using the Delong test indicated that Models 2, 3, 4, and 5 were superior to Model 1(P< 0.05), and no difference was found among Model 2, 3, 4 and Model 5(P > 0.05).

CONCLUSION

Radiomics models based on CT at different phases could accurately predict the lymph node metastasis in patients with ESCC, and their predictive efficiency was better than the clinical model based on tumor size criteria. NECT-based radiomics model could be a reasonable option for ESCC patients due to its lower price and availability for renal failure or allergic patients.

Magnetic resonance imaging texture analysis in the quantitative evaluation of acute osteomyelitis of the mandibular bone

OBJECTIVES

Accurate assessment of radiological images can help in early diagnosis and therapy of suppurative osteomyelitis (OM). The purpose of this study was to apply texture analysis to MRI as a means of quantitatively evaluating acute OM of the mandible.

METHODS

We analyzed the data from 38 patients who complained of pain and underwent MRI between April 2017 and March 2019. From the MRIs of these patients, with (n = 19) and without OM (n = 19), 279 radiomics features were extracted using short tau inversion recovery, data of the regions of interest and analyzed with MaZda v. 3.3. 10 features, including one histogram feature (90th percentile), eight gray-level co-occurrence matrix features (Sum Averg), and one gray-level run-length matrix feature (Horzl_RLNonUni), were selected using Fisher coefficient and compared between the acute OM and non-OM groups. The two groups were compared using Mann-Whitney U test with p value set at 0.05.

RESULTS

All 10 radiomics features showed significant differences between the acute OM and non-OM groups (p < 0.05).

CONCLUSIONS

MRI texture analysis has potential application in radiomics diagnosis of acute OM of the mandible.

Assessment of renal function using magnetic resonance quantitative histogram analysis based on spatial labeling with multiple inversion pulses

Background

The incidence of chronic kidney disease (CKD) is high, and is easy to develop into end-stage renal disease (ESRD), which requires kidney dialysis or kidney transplantation. Therefore, we want to explore the clinical value of magnetic resonance quantitative histogram analysis based on spatial labeling with multiple inversion pulses (SLEEK) in assessing renal function in the early stage.

Methods

One hundred and twenty-nine patients underwent abdominal MRI examination, including a coronal SLEEK sequence. The patients were divided into the control group [CG, 47 cases, estimated glomerular filtration rate (eGFR) >90], the mild renal function impairment (mRI) group (48 cases, eGFR =60–90), and the moderate to severe renal function impairment (m-sRI) group (34 cases, eGFR <60). Two experienced radiologists delineated cortex and medulla regions of interest (ROIs) on SLEEK images to obtain cortex and medulla quantitative histogram parameters [Mean, Median, Percentiles (5^th, 10^th, 25^th, 75^th, and 90^th), Skewness, Kurtosis, and Entropy] using FireVoxel. These histogram parameters were compared by proper statistical methods such as one-way analysis of variance, the χ² test, and receiver operating characteristic (ROC) curve analysis.

Results

Four histogram parameters (Inhomogeneity_cortex, Skewness_cortex, Kurtosis_medulla, and Entropy_medulla) differed significantly between the CG and the mRI group. One medulla (Entropy_medulla) and nine cortex (Mean_cortex, Median_cortex, Kurtosis_cortex, Entropy_cortex, and 5^th, 10^th, 25^th, 75^th, and 90^th Percentile_cortex) histogram parameters were significantly different between the m-RI and m-sRI groups. The most relevant parameter to eGFR was Inhomogenity_cortex (r=−0.450, P<0.001). Inhomogeneity_cortex had the largest area under the curve (AUC) for differentiating the mRI group from the CG (AUC =0.718; 95% CI: 0.616–0.806), while 25^th Percentile_cortex generated the largest AUC (AUC =0.786; 95% CI: 0.681–0.869) for differentiating the mRI and m-sRI groups.

Conclusions

Quantitative histogram parameters based on a SLEEK sequence can be used to supplement renal dysfunction assessment. Cortex histogram parameters are more valuable for evaluating renal function than medulla histogram parameters.

Quantitative assessment of mandibular bone marrow using computed tomography texture analysis for detect stage 0 medication-related osteonecrosis of the jaw

PURPOSE

Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication of treatment with bisphosphonates or antiangiogenic inhibitors. MRONJ has four stages (0-3); however, stage 0 MRONJ is difficult to detect using computed tomography (CT). This study aimed to quantitatively assess the mandibular bone marrow using texture analysis to detect stage 0 MRONJ from CT images.

METHODS

This retrospective study included 25 patients with stage 0 MRONJ who had a history of treatment with bisphosphonates and underwent CT and magnetic resonance imaging (MRI). The mandibular bone marrow with abnormal signals (T1-weighted imaging: low, T2-weighted imaging: low or high, short-tau inversion recovery: high) on MRI, and no qualitative characteristic CT and oral findings indicative of osteonecrosis (exposed bone, sequestrum, periosteal reaction, and osteolysis) was identified as 0 MRONJ. Texture features of the bone marrow of the mandible with MRONJ and the contralateral, normal mandibular bone marrow were extracted using an open-access software, namely, LIFEx. The volumes of interest (VOIs) were manually placed on CT images by tracing the bilateral mandibular bone marrow regions, excluding the teeth, mandibular canal, and cortical bone. Thirty-seven texture features were extracted from each VOI.

RESULTS

Six gray-level run length matrix features and four gray-level zone length matrix features exhibited significant differences between mandibular bone marrow with and without MRONJ.

CONCLUSIONS

CT was able to quantitatively assess texture features of normal mandibular bone marrow and that with MRONJ. Texture analysis may be useful as a new method for detecting stage 0 MRONJ using CT.

Quantitative assessment of the parotid gland using computed tomography texture analysis to detect parotid sialadenitis

OBJECTIVE

We aimed to quantitatively assess the parotid gland by using computed tomography (CT) texture analysis to detect parotid sialadenitis (PS).

STUDY DESIGN

This retrospective case-control study included 43 patients with PS who underwent CT and magnetic resonance imaging (MRI). Parotid glands with an abnormal signal (STIR: High) on MRI were identified as showing PS. Patients with parotid gland tumors, bilateral PS, marked fatty degeneration, and severe artifacts on CT were excluded. The texture features of parotid glands with PS and the contralateral normal parotid glands were analyzed using the open-access software LIFEx. The regions of interest were manually placed by tracing contours of both parotid glands on CT images. The results were tested with the paired t-test (or Wilcoxon rank-sum test when appropriate). Receiver operating characteristic (ROC) curve analysis was performed to assess the ability of texture features to predict PS.

RESULTS

Six gray level run length matrix features, 2 neighborhood gray level difference matrix features, and 5 gray level zone length matrix features displayed significant differences between PS and normal glands (P ≤ .047). ROC curve analysis showed acceptable accuracy in 4 texture features.

CONCLUSIONS

CT texture analysis allowed quantitative assessment of parotid glands and may have the potential to detect PS.

Comparison of diffusion-weighted imaging and MR elastography in staging liver fibrosis: a meta-analysis

PURPOSE

To compare the diagnostic performance of diffusion-weighted imaging (DWI), gradient-recalled echo-based magnetic resonance elastography (GRE-MRE), and spin-echo echo-planar imaging-based MRE (SE-EPI-MRE) in liver fibrosis staging.

METHODS

A systematic literature search was done to collect studies on the performance of DWI, GRE-MRE, and SE-EPI-MRE for diagnosing liver fibrosis. Pooled sensitivity, specificity, diagnostic odds ratio, positive and negative likelihood ratio, and a summary receiver operating characteristic (ROC) curve were estimated with a bivariate random effects model. Subgroup analyses on various study characteristics were performed.

RESULTS

Sixty studies with a total of 6620 patients were included in the meta-analysis. Pooled sensitivity and specificity of GRE-MRE and SE-EPI-MRE showed high diagnostic accuracy and did not differ significantly. The area under the summary ROC curve for all stages of fibrosis differed significantly between DWI (0.83-0.88) and either GRE-MRE (0.95-0.97) or SE-EPI-MRE (0.95-0.99). Substantial heterogeneity was detected for all three imaging methods.

CONCLUSIONS

Both GRE-MRE and SE-EPI-MRE are highly accurate for detection of each liver fibrosis stage, with high potential to replace liver biopsy. Although DWI had a moderate accuracy in distinguishing liver fibrosis, it could be regarded as an alternative to MRE, since it is widely available and easily implemented in routine liver MRI.

Quantitative assessment of the maxillary sinusitis using computed tomography texture analysis: odontogenic vs non-odontogenic etiology

OBJECTIVES

The purpose of this study was to investigate computed tomography (CT) texture features of mucosal thickening of maxillary sinus mucosa to differentiate odontogenic maxillary sinusitis (OMS) from non-odontogenic maxillary sinusitis (NOMS).

METHODS

Eighteen OMS patients and age- and gender-matched 18 NOMS patients who underwent sinus CT were retrospectively reviewed. OMS patients were identified by histopathological examination of tissues excised at surgery combined with CT imaging findings. Patients with mucosal thickening in the maxillary sinus without apical periodontitis or advanced periodontal bone loss near the maxillary sinus on CT were defined as NOMS. Patients with thin mucosal thickening (< 10 mm), cyst, tumor, post-operative deformity, severe metal artifact precluding visualization of the maxillary sinus, and age younger than 20 years were excluded. CT texture features of the mucosal thickening were analyzed using an in-house developed Matlab-based texture analysis program. Forty-five texture features were extracted from each segmented volume. The results were tested with the Mann-Whitney U test.

RESULTS

Six histogram features (mean, median, standard deviation, entropy, geometric mean, harmonic mean) and two gray-level co-occurrence matrix features (entropy, correlation) showed significant differences between OMS and NOMS patients.

CONCLUSIONS

CT texture analysis revealed the quantitative differences between OMS and NOMS. The texture features can serve as a quantitative indicator of maxillary sinusitis to differentiate between OMS and NOMS and help prevent incorrect treatment choices.

Noninvasive DW-MRI metrics for staging hepatic fibrosis and grading inflammatory activity in patients with chronic hepatitis B

PURPOSE

To assess the value of various diffusion parameters obtained from monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging (DWI) models for staging hepatic fibrosis (HF) and grading inflammatory activity in patients with chronic hepatitis B (CHB).

METHODS

82 patients with CHB and 30 healthy volunteers underwent DWI with 13 b-values on a 3T MRI unit. The standard apparent diffusion coefficient (ADC_st) was calculated using a monoexponential model. The true diffusion coefficient (D_t), pseudo-diffusion coefficient (D_p), and perfusion fraction (f) were calculated using a biexponential model. The distributed diffusion coefficient (DDC) and water-molecule diffusion heterogeneity index (α) were calculated using a stretched-exponential model. Receiver operating characteristic (ROC) curves were performed for diffusion parameters to compare the diagnosis performance.

RESULTS

The distributions of hepatic fibrosis stages and the inflammatory activity grades (METAVIR scoring system) were as follows: F0, n = 1; F1, n = 16; F2, n = 31; F3, n = 19; and F4, n = 15. A0, n = 1; A1, n = 14; A2, n = 46; and A3, n = 21. ADC_st, D_t and DDC values showed negative correlation with the fibrosis stage (r = - 0.418, - 0.717 and - 0.630, all P < 0.001) and the inflammatory activity grade (r = - 0.514, - 0.626 and - 0.550, all P < 0.001). The area under the ROC curve (AUC) of D_t (AUC = 0.854, 0.881) and DDC (AUC = 0.794, 0.834) were significantly higher than that of ADC_st (AUC = 0.637, 0.717) in discriminating significant fibrosis (≥ F2) and advanced fibrosis (≥ F3) (all P < 0.05). Although D_t (AUC = 0.867, 0.836) and DDC (AUC = 0.810, 0.808) showed higher AUCs than ADC_st (AUC = 0.767, 0.803), there was no significant difference in their ability in detecting inflammatory activity grade ≥ A2/A3 (P > 0.05).

CONCLUSIONS

D_t and DDC are promising indicators and outperform ADC_st for staging HF. While both D_t and DDC have similar diagnostic performance compared with ADC_st for grading inflammatory activity.

A Heterogeneity Radiomic Nomogram for Preoperative Differentiation of Primary Gastric Lymphoma From Borrmann Type IV Gastric Cancer

OBJECTIVE

This study aimed to preoperatively differentiate primary gastric lymphoma from Borrmann type IV gastric cancer by heterogeneity nomogram based on routine contrast-enhanced computed tomographic images.

METHODS

We enrolled 189 patients from 2 hospitals (90 in the training cohort and 99 in the validation cohort). Subjective findings, including high-enhanced mucosal sign, high-enhanced serosa sign, nodular or an irregular outer layer of the gastric wall, and perigastric fat infiltration, were assessed to construct a subjective finding model. A deep learning model was developed to segment tumor areas, from which 1680 three-dimensional heterogeneity radiomic parameters, including first-order entropy, second-order entropy, and texture complexity, were extracted to build a heterogeneity signature by least absolute shrinkage and selection operator logistic regression. A nomogram that integrates heterogeneity signature and subjective findings was developed by multivariate logistic regression. The diagnostic performance of the nomogram was assessed by discrimination and clinical usefulness.

RESULTS

High-enhanced serosa sign and nodular or an irregular outer layer of the gastric wall were identified as independent predictors for building the subjective finding model. High-enhanced serosa sign and heterogeneity signature were significant predictors for differentiating the 2 groups (all, P < 0.05). The area under the curve with heterogeneity nomogram was 0.932 (95% confidence interval, 0.863-0.973) in the validation cohort. Decision curve analysis and stratified analysis confirmed the clinical utility of the heterogeneity nomogram.

CONCLUSIONS

The proposed heterogeneity radiomic nomogram on contrast-enhanced computed tomographic images may help differentiate primary gastric lymphoma from Borrmann type IV gastric cancer preoperatively.

Diffusion-weighted magnetic resonance imaging for the assessment of liver fibrosis in chronic viral hepatitis

Background

Accurate noninvasive methods for the assessment of liver fibrosis are urgently needed. This prospective study evaluated the diagnostic accuracy of diffusion-weighted magnetic resonance imaging (DWI) for the staging of liver fibrosis and proposed a diagnostic algorithm using DWI to identify cirrhosis in patients with chronic viral hepatitis.

Methods

One hundred twenty-one treatment-naïve patients with chronic hepatitis B or C were evaluated with DWI followed by liver biopsy on the same day. Breath-hold single-shot echo-planar DWI was performed to measure the apparent diffusion coefficient (ADC) of the liver and spleen. Normalized liver ADC was calculated as the ratio of liver ADC to spleen ADC.

Results

There was an inverse correlation between fibrosis stage and normalized liver ADC (p<0.05). For the prediction of fibrosis stage ≥2, stage ≥3, and cirrhosis, the area under the receiver-operating curve of normalized liver ADC was 0.603, 0.704, and 0.847, respectively. The normalized liver ADC value ≤1.02×10⁻³ mm²/s had 88% sensitivity, 81% specificity, 25% positive predictive value (PPV), and 99% negative predictive value (NPV) for the diagnosis of cirrhosis. Using a sequential approach with the Fibrosis-4 index followed by DWI, normalized liver ADC ≤1.02×10⁻³ mm²/s in patients with Fibrosis-4 >3.25 yielded an 80% PPV for cirrhosis, and a 100% NPV to exclude cirrhosis in patients with Fibrosis-4 between 1.45 and 3.25. Only 15.7% of patients would require a liver biopsy. This sequential strategy can reduce DWI examinations by 53.7%.

Conclusion

Normalized liver ADC measurement on DWI is an accurate and noninvasive tool for the diagnosis of cirrhosis in patients with chronic viral hepatitis.

Analysis of 7-tesla diffusion-weighted imaging in the prediction of pituitary macroadenoma consistency

OBJECTIVE

Endoscopic surgery is an effective treatment strategy for pituitary adenomas; however, intrinsic tumor properties such as tumor consistency can challenge or preclude gross-total resection. Preoperative characterization of tumor consistency may help to guide the surgical approach and to predict the extent of resection that is possible. Advanced radiological modalities such as 7T diffusion-weighted imaging (DWI) may be useful in probing biological tissue properties of pituitary adenomas. The objective of the present study was to examine 7T DWI as a novel method of measuring the consistency of pituitary adenomas.

METHODS

Thirteen patients with pituitary macroadenomas underwent 7T MRI, including a DWI image acquisition. Tumor apparent diffusion coefficient (ADC) was normalized to the adjacent temporal gray matter ADC. All patients underwent resection, and a single neurosurgeon blinded to ADC values rated tumor firmness from 1 (least firm) to 5 (most firm) using objective criteria. The tumor specimens were evaluated histopathologically for cellularity, collagen content, and vascularity by a neuropathologist who was also blinded to ADC values. The tumor ADC was correlated with intraoperative consistency rating, histopathology, and extent of resection. Receiver operating characteristic (ROC) curve analyses were performed to identify thresholds to predict tumor consistency.

RESULTS

Corrected ADC values were significantly correlated with both tumor firmness (r = -0.60, p = 0.029) and the extent of trichrome staining (r = -0.72, p = 0.009) such that greater ADC values were associated with both decreased tumor firmness and decreased collagen staining. Correlations between ADC values and tumor vascularity were not significant (r = -0.09, p = 0.78). Corrected ADC values in totally resected tumors (1.54) were greater than those in subtotally resected tumors (0.85) (p = 0.02), and ADC values were greater with moderate tumor cellularity (1.51) than with high tumor cellularity (0.8) (p = 0.035). There was a trend-level association for partial resections to exhibit greater tumor firmness rating (3 vs 1.7; p = 0.051). Finally, the degree of trichrome staining positively correlated with tumor firmness (r = 0.60, p = 0.04). The optimal threshold for predicting intraoperative consistency rating was an ADC ratio of 0.87 (sensitivity 80%, specificity 100%, area under the curve [AUC] 0.90; p = 0.043). The optimal cutoff for distinguishing the extent of resection was 1.19 (sensitivity 85.7%, specificity 83.3% AUC 0.833; p = 0.046).

CONCLUSIONS

The authors' results suggest that a high-resolution ADC of pituitary adenomas is a sensitive measure of tumor consistency. 7T DWI may hold clinical value in the preoperative workup and surgical management of patients with pituitary macroadenomas.

Computed tomography texture analysis of mandibular condylar bone marrow in diabetes mellitus patients

OBJECTIVES

Diabetes mellitus (DM) is associated with a broad range of complications, such as retinopathy, nephropathy, neuropathy, and cardiovascular disease. Therefore, predicting DM from head and neck images is a challenge for clinicians. The purpose of this study was to assess the mandibular condylar bone marrow in DM patients using computed tomography (CT) texture analysis.

METHODS

This retrospective study included 16 DM and age and sex matched 16 control patients (11 men, 5 women; mean age, 56.8 ± 14.4 years; range 31-78 years). Patients with Type I DM, prior history of taking bisphosphonates, osteoarthritis of the temporomandibular joint, and CT images with metal artifacts were excluded from this study. Bilateral mandibular condylar bone marrow was manually contoured on axial CT images. The presence or absence of DM is the primary predictor variable. Texture features of the region of interest was the outcome variable, that were analyzed using an open-access software, MaZda Ver.3.3. For each group, 20 features out of 279 parameters were selected with Fisher, probability of error and average correlation coefficient methods in MaZda. Bivariate statistics were computed with the Mann-Whitney U test and the P value was set at .05.

RESULTS

One histogram feature, 15 Gy level co-occurrence matrix features, and four gray level run length matrix features showed differences between the DM patients and non-DM patients (P < 0.05).

CONCLUSIONS

Several texture features of the condyle demonstrated differences between the DM and non-DM patients. CT texture analysis may potentially detect DM from the condylar bone marrow.

Diffusion-weighted MRI in staging of post hepatitis C fibrosis: does ADC value challenge liver biopsy?

Background

There is obvious interest in finding a non-invasive diagnostic tool to detect the development of hepatic fibrosis and distinguish between its various stages. Chronic inflammation of the liver secondary to viral hepatitis, autoimmune conditions, sclerosing cholangitis, drug toxicity, chronic alcohol intake, different metabolic disorders, and steatosis lead to fibrosis and maybe cirrhosis. The current study aimed to assess the usefulness of diffusion-weighted magnetic resonance imaging (DW-MRI) in diagnosis of post hepatitis C fibrosis and detection of its stage.

Results

A prospective study had included 232 participants; 120 patients had chronic hepatitis C with/without HCC and 112 subjects had normal liver. There was no significant difference between the two groups regarding age or gender (p 0.192 and 0.227 respectively). DW-MRI was performed using 1.5 T machine. The mean liver ADC values and normalized liver ADC (liver ADC/spleen ADC) were measured at b value 800 s/mm2; both were significantly lower among cases than controls. Cutoff values of liver ADC were 1.531 × 10−3 mm2/s, 1.409 × 10−3 mm2/s, 1.192 × 10−3 mm2/s, and 1.093 × 10−3 mm2/s for METAVIR stages ≥ F1, ≥ F2, ≥ F3, and F4, respectively. Normalized liver ADC showed larger area under the curve (AUC) than mean liver ADC in all differentiation categories except for differentiating between F0 and all other fibrosis stages.

Conclusion

In line with the literature, DW-MR imaging using b value of 800 s/mm2 has proved to be a valuable diagnostic technique for detection and staging of post hepatitis C fibrosis/cirrhosis being noninvasive procedure with acceptable accuracy. DWI using liver/spleen ADC values raised the diagnostic performance with AUC more than 90% in all fibrosis stages on METAVIR score.

Quantitative assessment of normal submandibular glands and submandibular sialadenitis using CT texture analysis: A retrospective study

OBJECTIVE

The purpose of this study was to quantitatively assess normal submandibular glands and submandibular sialadenitis (SS) using computed tomography (CT) texture analysis as part of radiomics quantitative analysis.

STUDY DESIGN

In total, 31 patients with unilateral SS who underwent head and neck magnetic resonance imaging (MRI) and CT and were retrospectively reviewed. Submandibular glands with abnormal signals (STIR: high, T2-weighted image: high, T1-weighted image: low) on MRI were identified as SS. The radiomics features of the contralateral normal submandibular glands and SS were analyzed using an open-access software, MaZda Version 3.3. Sixteen radiomics features were selected with Fisher and probability of error and average correlation coefficient methods in MaZda from 279 original parameters calculated for each of the normal and SS glands. The results were statistically analyzed with the Wilcoxon rank sum test.

RESULTS

One gray-level co-occurrence matrix feature and 9 gray-level run length matrix features displayed significant differences between normal submandibular glands and glands with SS (P < .05).

CONCLUSIONS

CT texture analysis was able to quantitatively distinguish between normal and diseased submandibular glands. It therefore may have the potential to detect SS.

Spectral CT-Based Iodized Oil Quantification to Predict Tumor Response Following Chemoembolization of Hepatocellular Carcinoma

PURPOSE

To quantify iodized oil retention in tumors after transarterial chemoembolization using spectral computed tomography (CT) imaging in patients with hepatocellular carcinoma (HCC) and evaluate its performance in predicting 12-month tumor responses.

MATERIALS AND METHODS

From September 2017 to December 2018, 111 patients with HCC underwent initial conventional transarterial chemoembolization. Immediately after the procedure, unenhanced CT was performed using a spectral CT scanner, and the iodized oil densities in index tumors were measured. In tumor-level analyses, a threshold level of iodized oil density in the tumors was calculated using clustered receiver operating characteristic curve analyses to predict the 12-month tumor responses. In patient-level analyses, significant factors associated with a 12-month complete response, including the presence of tumors below the threshold value (ie, suspected residual tumors), were evaluated by logistic regression.

RESULTS

Forty-eight HCCs in 39 patients were included in the analyses. The lower 10th percentile of the iodine density was identified as the threshold for determining the 12-month nonviable responses. The area under the curve of the iodine density measurements in predicting the 12-month nonviable responses was 0.893 (95% confidence interval, 0.797-0.989). The threshold value of the iodine density of 10.68 mg/mL yielded a sensitivity of 82.76% and specificity of 94.74% (P < .001). In the patient-level analysis, the 12-month complete response was significantly associated with the presence of a suspected residual tumor, with an odds ratio of 72.0 (95% confidence interval, 7.273-712.770).

CONCLUSIONS

Spectral CT imaging using quantitative analysis of the iodized oil retention in target HCCs can predict tumor responses after a conventional transarterial chemoembolization procedure.

Magnetic Resonance imaging analysis of liver fibrosis and inflammation: overwhelming gray zones restrict clinical use

Magnetic resonance (MR) identification and grading of subjects with liver fibrosis and inflammation represents a clinical challenge. MR elastography plays a well-defined role in fibrosis estimation, but its use is not widely available in clinical settings. Given that liver MR is becoming the reference standard for fat and iron quantitation, there is a need to clarify whether there is any role for MR imaging in the concomitant evaluation of fibrosis and inflammation in this setting. This review summarizes the diagnostic estimations of different MR imaging parameters obtained from conventional non-contrast-enhanced multiple b values diffusion-weighted acquisitions, variable flip angles T1 relaxation maps and STIR images. Although some derived parameters have shown a significant correlation to histological scores, a small magnitude of effect with wide overlap across severity grades is the rule. Contrary to fat and iron quantification, the low precision and reproducibility of MR imaging metrics limits its clinical relevance in fibrosis and inflammation assessment. In a sequential clinical approach combining different methodologies, MR imaging has no applicability for ruling-out and low accuracy for ruling-in advanced fibrosis. Thereby, MR elastography remains as the only image method with high diagnostic accuracy for the detection of advanced fibrosis. Until date, inflammation remains in a gray zone where biopsy cannot be replaced, and further investigations are needed. The present review offers an in-depth discuss of the MR imaging diagnostic performance for the evaluation of liver fibrosis and inflammation, highlighting the need for scientific improvements.

Diffusion-weighted imaging and texture analysis: current role for diffuse liver disease

Multiparametric MRI represents the primary imaging modality to assess diffuse liver disease, both in a qualitative and in a quantitative manner. Diffusion-weighted imaging (DWI) is among the imaging techniques that can be used to assess fibrosis due to its unique capability to assess microstructural changes at the tissue level. DWI is based on water mobility patterns and has the potential to become a non-invasive and non-destructive virtual biopsy to assess diffuse liver disease, overcoming sampling bias errors due to its three-dimensional imaging capabilities. Parallel to DWI, another quantitative method called texture analysis may be used to assess early and advanced diffused liver disease through quantifying spatial relationships in a global and local level, applying to any type of digital imaging technique like MRI or CT. Initial results using texture analysis hold great promise. In the current paper, we will review the role of DWI and texture analysis using MR images in assessing diffuse liver disease.

Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD

Objectives

To assess whole-liver texture analysis on T1 maps for risk stratification of advanced fibrosis in patients with suspected nonalcoholic fatty liver disease (NAFLD).

Methods

This retrospective study included 53 patients. Histogram and texture parameters (volume, mean, SD, median, 5th percentile, 95th percentile, skewness, kurtosis, diff-entropy, diff-variance, contrast, and entropy) of T1 maps were calculated based on the semi-automatically segmented whole-liver volume. A two-step approach combining the Nonalcoholic Fatty Liver Disease Fibrosis Score (NFS) and Fibrosis-4 Index (FIB-4) with the liver stiffness measurement (LSM) for the risk stratification was used. Univariate analysis was performed to identify significant parameters. Logistic regression models were then run on the significant features. Diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis.

Results

In total, 33 (62%) subjects had a low risk and 20 (38%) subjects had an intermediate-to-high risk of advanced fibrosis. The following significantly different parameters with the best performance were diff-entropy, entropy, and diff-variance, with AUROC 0.837 (95% CI 0.73–0.95), 0.821 (95% CI 0.71–0.94), and 0.807 (95% CI 0.69–0.93). The optimal combination of median, 5th percentile, and diff-entropy as a multivariate model improved the diagnostic performance to diagnose an intermediate-to-high risk of advanced fibrosis with AUROC 0.902(95% CI 0.79–0.97).

Conclusions

Parameters obtained by histogram and texture analysis of T1 maps may be a noninvasive analytical approach for stratifying the risk of advanced fibrosis in NAFLD.

Key Points

• Variable flip angle (VFA) T1 mapping can be used to acquire 3D T1 maps within a clinically acceptable duration.

• Whole-liver histogram and texture parameters on T1 maps in patients with NAFLD can distinguish those with an intermediate-to-high risk of advanced fibrosis.

• The multivariate model of combination of texture parameters improved the diagnostic performance for a high risk of advanced fibrosis and clinical parameters offer no added value to the multivariate model.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-07235-4) contains supplementary material, which is available to authorized users.

Intravoxel incoherent motion diffusion-weighted MRI for the characterization of inflammation in chronic liver disease

OBJECTIVE

To evaluate the diagnostic performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for grading hepatic inflammation.

METHODS

In this retrospective cross-sectional dual-center study, 91 patients with chronic liver disease were recruited between September 2014 and September 2018. Patients underwent 3.0-T MRI examinations within 6 weeks from a liver biopsy. IVIM parameters, perfusion fraction (f), diffusion coefficient (D), and pseudo-diffusion coefficient (D*), were estimated using a voxel-wise nonlinear regression on DWI series (10 b-values from 0 to 800 s/mm²). The reference standard was histopathological analysis of hepatic inflammation grade, steatosis grade, and fibrosis stage. Intraclass correlation coefficients (ICC), univariate and multivariate correlation analyses, and areas under receiver operating characteristic curves (AUC) were assessed.

RESULTS

Parameters f, D, and D* had ICCs of 0.860, 0.839, and 0.916, respectively. Correlations of f, D, and D* with inflammation grade were ρ = - 0.70, p < 0.0001; ρ = 0.10, p = 0.35; and ρ = - 0.27, p = 0.010, respectively. When adjusting for fibrosis and steatosis, the correlation between f and inflammation (p < 0.0001) remained, and that between f and fibrosis was also significant to a lesser extent (p = 0.002). AUCs of f, D, and D* for distinguishing inflammation grades 0 vs. ≥ 1 were 0.84, 0.53, and 0.70; ≤ 1 vs. ≥ 2 were 0.88, 0.57, and 0.60; and ≤ 2 vs. 3 were 0.86, 0.54, and 0.65, respectively.

CONCLUSION

Perfusion fraction f strongly correlated, D very weakly correlated, and D* weakly correlated with inflammation. Among all IVIM parameters, f accurately graded inflammation and showed promise as a biomarker of hepatic inflammation.

KEY POINTS

• IVIM parameters derived from DWI series with 10 b-values are reproducible for liver tissue characterization. • This retrospective two-center study showed that perfusion fraction provided good diagnostic performance for distinguishing dichotomized grades of inflammation. • Fibrosis is a significant confounder on the association between inflammation and perfusion fraction.

Entropy and uniformity as additional parameters to optimize the effectiveness of bone CT in the evaluation of acromegalic patients

PURPOSE

Acromegaly is considered an important cause of secondary osteoporosis. However, studies on bone mineral density (BMD) have yielded conflicting results and there are few studies that evaluate an accurate imaging method for early diagnosis of osteoporosis in these patients. The objective of this study was to assess whether entropy and uniformity on computed tomography (CT) scans are useful parameters for optimization of assessment of bone fragility in patients with acromegaly.

METHODS

We included 34 patients and 36 controls matched for age and sex in a cross-sectional study. Patients and controls underwent CT scan of the lumbosacral spine, dual-energy x-ray absorptiometry (DXA) and blood tests. A software was developed to calculate the entropy and uniformity by a region of interest (ROI) of the trabecular bone of the first lumbar vertebra (L1).

RESULTS

The acromegalic group presented higher mean bone entropy (6.87 ± 0.98 vs. 6.03 ± 1.68, p = 0.013) and lower mean bone uniformity (0.035 ± 0.704 vs. 0.113 ± 0.205, p = 0.035) than control group. Analyzing only acromegalics, mean bone entropy was higher and bone uniformity was lower in patients with hypogonadism than patients without hypogonadism (7.28 ± 0.36 vs. 6.74 ± 1.08, p = 0.038 and 0.008 ± 0.002 vs. 0.043 ± 0.079, p = 0.031) respectively. Patients with acromegaly presented higher BMD and Z-score in the femoral neck than control group (1.156 ± 0.108 vs. 0.925 ± 0.326 g/cm², p = 0.043 and 0.6 ± 0.6 vs. -0.05 ± 0.8, p = 0.041, respectively). Entropy was negatively correlated with T-score of the lumbar spine (r_p = -0.357, p = 0.033) in control group and uniformity was positively correlated with T-score of the lumbar spine, neck, and total hip, respectively (r_p = 0.371, p = 0.031; r_p = 0.348, p = 0.043 and r_p = 0.341, p = 0.049) in acromegalic group.

CONCLUSIONS

The study identified that entropy and uniformity are a relevant parameters data in bone fragility assessment in acromegalic patients.

Pulmonary adenocarcinoma appearing as ground-glass opacity nodules identified using non-enhanced and contrast-enhanced CT texture analysis: A retrospective analysis

The present study aimed to investigate the ability of CT-based texture analysis to differentiate invasive adenocarcinoma (IA) from pre-invasive lesions (PIL) or minimally IA (MIA) appearing as ground-glass opacity (GGO) nodules, and to further compare the performance of non-enhanced CT (NECT) images with that of contrast-enhanced CT (CECT) images. A total of 77 patients with GGO nodules and surgically confirmed pulmonary adenocarcinoma were included in the present retrospective study. Each GGO nodule was manually segmented and its texture features were extracted from NECT and CECT images using in-house developed software coded in MATLAB (MathWorks). The independent-samples t-test was used to select the texture features with statistically significant differences between IA and MIA/PIL. Multivariate logistic regression and receiver operating characteristics (ROC) curve analyses were performed to identify predictive features. Of the 77 GGO nodules, 12 were atypical adenomatous hyperplasia or adenocarcinoma in situ (15.6%), 36 were MIA (46.8%) and 29 were IA (37.7%). IA and MIA/PIL exhibited significant differences in most histogram features and gray-level co-occurrence matrix features (P<0.05). Multivariate logistic regression and ROC curve analyses revealed that smaller energy and higher entropy were significant differentiators of IA from MIA and PIL, irrespective of whether NECT images [area under the curve (AUC): 0.839, 0.859] or CECT images (AUC: 0.818, 0.820) are used. Texture analysis of CT images, regardless of whether NECT or CECT is used, has the potential to distinguish IA from PIL or MIA, particularly the parameters of energy and entropy. Furthermore, NECT images were simpler to obtain and no contrast agent was required; thus, analysis with NECT may be a preferred choice.

Whole-Liver Apparent Diffusion Coefficient Histogram Analysis for the Diagnosis and Staging of Liver Fibrosis

BACKGROUND

Conventional diffusion-weighted imaging is limited in the quantitative evaluation of liver fibrosis, and whole-liver apparent diffusion coefficient (ADC) histogram analysis might contribute to the diagnosis and staging of liver fibrosis.

PURPOSE

To explore the value of whole-liver ADC histogram parameters in the diagnosis and staging of liver fibrosis.

STUDY TYPE

Retrospective.

POPULATION

Twenty individuals with no liver disease and 86 patients with liver fibrosis, including 30 with chronic viral hepatitis, 29 with autoimmune hepatitis, and 27 with unexplained liver fibrosis patients.

FIELD STRENGTH/SEQUENCE

3.0T/T₁ -weighted, T₂ -weighted, and diffusion-weighted images.

ASSESSMENT

A region of interest (ROI) was drawn in each slice of the diffusion-weighted images. Whole-liver histogram parameters were obtained with dedicated software by accumulating all ROIs. The effectiveness of the parameters in differentiating stage 1 or greater (≥F1), stage 2 or greater (≥F2), and stage 3 or greater (≥F3) liver fibrosis was assessed.

STATISTICAL TESTS

Mann-Whitney U-test and receiver operating characteristic curve analysis.

RESULTS

Kurtosis, entropy, skewness, mode, and 90^th and 75^th percentiles exhibited significant differences among the pathological fibrosis stages (P < 0.05). Kurtosis was found to be the most meaningful parameter in differentiating fibrosis stages of the viral hepatitis, autoimmune hepatitis, and unexplained liver fibrosis group (area under the curve) (AUC = 0.793, 0.771, 0.798, respectively). In the combined liver fibrosis group, kurtosis achieved the highest AUC (0.801; 95% confidence interval [CI]: 0.702-0.900; sensitivity: 0.750; specificity: 0.850; positive likelihood ratio: 4.953; negative likelihood ratio: 0.302; positive predictive value: 0.946; negative predictive value: 0.486), with a cutoff value of 1.817, in differentiating fibrosis stage ≥F1.

DATA CONCLUSION

Kurtosis, entropy, skewness, mode, and 90^th and 75^th percentiles may contribute to the diagnosis and staging of liver fibrosis, especially kurtosis.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1745-1754.

Exploratory Study of Apparent Diffusion Coefficient Histogram Metrics in Assessing Pancreatic Malignancy

PURPOSE

To evaluate whole-lesion 3D-histogram apparent diffusion coefficient (ADC) metrics for assessment of pancreatic malignancy.

METHODS

Forty-two pancreatic malignancies (36 pancreatic adenocarcinoma [PDAC], 6 pancreatic neuroendocrine [PanNET]) underwent abdominal magnetic resonance imaging (MRI) with diffusion-weighted imaging before endoscopic ultrasound biopsy or surgical resection. Two radiologists independently placed 3D volumes of interest to derive whole-lesion histogram ADC metrics. Mann-Whitney tests and receiver operating characteristic analyses were used to assess metrics' diagnostic performance for lesion histology, T-stage, N-stage, and grade.

RESULTS

Whole-lesion ADC histogram metrics lower in PDACs than PanNETs for both readers (P ≤ .026) were mean ADC (area under the curve [AUC] = 0.787-0.792), mean of the bottom 10th percentile (mean_0-10) (AUC = 0.787-0.880), mean of the 10th-25th percentile (mean_10-25) (AUC = 0.884-0.917) and mean of the 25th-50th percentile (mean_25-50) (AUC = 0.829-0.829). For mean_10-25 (metric with highest AUC for identifying PDAC), for reader 1 a threshold > 0.94 × 10^-3 mm²/s achieved sensitivity 94% and specificity 83%, and for reader 2 a threshold > 0.82 achieved sensitivity 97% and specificity 67%. Metrics lower in nodal status ≥ N₁ than N₀ for both readers (P ≤ .043) were mean_0-10 (AUC = 0.789-0.822) and mean_10-25 (AUC = 0.800-0.822). For mean_10-25 (metric with highest AUC for identifying N₀), for reader 1 a threshold <1.17 achieved sensitivity 87% and specificity 67%, and for reader 2 a threshold <1.04 achieved sensitivity 87% and specificity 83%. No metric was associated with T-stage (P > .195) or grade (P > .215).

CONCLUSION

Volumetric ADC histogram metrics may serve as non-invasive biomarkers of pancreatic malignancy. Mean_10-25 outperformed standard mean for lesion histology and nodal status, supporting the role of histogram analysis.

Intravoxel Incoherent Motion (IVIM) Diffusion-Weighted Imaging (DWI) in Patients with Liver Dysfunction of Chronic Viral Hepatitis: Segmental Heterogeneity and Relationship with Child-Turcotte-Pugh Class at 3 Tesla

Background

Few studies focused on the region of interest- (ROI-) related heterogeneity of liver intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI). The aim of the study was to evaluate the differences of liver IVIM parameters among liver segments in cirrhotic livers (chronic viral hepatitis).

Material and Methods

This was a retrospective study of 82 consecutive patients with chronic liver disease who underwent MRI examination at the Jinan Infectious Diseases Hospital between January 2015 and December 2016. IVIM DWI (seven different b values) was performed on a Siemens 3.0-T MRI scanner. Pure molecular diffusion (D), pseudodiffusion (D^∗), and perfusion fraction (f) in different liver segments were evaluated.

Results

f, D, and D^∗ were different among the liver segments (all p < 0.05), indicating heterogeneity in IVIM parameters among liver segments. f was consistently higher in Child-Turcotte-Pugh (CTP) class A compared with CTP class B + C (p < 0.01). D and D^∗ were higher in CTP class A compared with CTP class B + C (p < 0.05). In patients with mean f value of >0.29, the AUC was 0.88 (95% CI: 0.81-0.96), with 86.8% sensitivity and 81.8% specificity for predicting CTP class A from CTP class B + C.

Conclusion

Liver IVIM could be a promising method for classifying the severity of segmental liver dysfunction of chronic viral hepatitis as evaluated by the CTP class, which provides a noninvasive alternative for evaluating segmental liver dysfunction with accurate selection of ROIs. Potentially it can be used to monitor the progression of CLD and LC in the future.

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Objectives

To evaluate the influence of MRI scanning parameters on texture analysis features.

Methods

Publicly available data from the Reference Image Database to Evaluate Therapy Response (RIDER) project sponsored by The Cancer Imaging Archive included MRIs on a phantom comprised of 18 25‐mm doped, gel‐filled tubes, and 1 20‐mm tube containing 0.25 mM Gd‐DTPA (EuroSpinII Test Object5, Diagnostic Sonar, Ltd, West Lothian, Scotland). MRIs performed on a 1.5 T GE HD, 1.5 T Siemens Espree (VB13), or 3.0 T GE HD with TwinSpeed gradients with an eight‐channel head coil included T1WIs with multiple flip angles (flip‐angle = 2,5,10,15,20,25,30), TR/TE = 4.09–5.47/0.90–1.35 ms, NEX = 1 and DCE with 30° flip‐angle, TR/TE=4.09–5.47/0.90–1.35, and NEX = 1,4. DICOM data were imported into an in‐house developed texture analysis program which extracted 41‐texture features including histogram, gray‐level co‐occurrence matrix (GLCM), and gray‐level run‐length (GLRL). Two‐tailed t tests, corrected for multiple comparisons (Q values) were calculated to compare changes in texture features with variations in MRI scanning parameters (magnet strength, flip‐angle, number of excitations (NEX), scanner platform).

Results

Significant differences were seen in histogram features (mean, median, standard deviation, range) with variations in NEX (Q = 0.003–0.045) and scanner platform (Q < 0.0001), GLCM features (entropy, contrast, energy, and homogeneity) with NEX (Q = 0.001–0.018) and scanner platform (Q < 0.0001), GLRL features (long‐run emphasis, high gray‐level run emphasis, high gray‐level emphasis) with magnet strength (Q = 0.0003), NEX (Q = 0.003–0.022) and scanner platform (Q < 0.0001).

Conclusion

Significant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.

Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging

OBJECTIVES

To study the added value of mean and entropy of apparent diffusion coefficient (ADC) values at standard (800 s/mm²) and high (1500 s/mm²) b-values obtained with diffusion-weighted imaging in identifying histologic phenotypes of invasive ductal breast cancer (IDC) with MR imaging.

METHODS

One hundred thirty-four IDC patients underwent diffusion-weighted imaging with b-values of 800 and 1500 s/mm², and corresponding ADC₈₀₀ and ADC₁₅₀₀ maps were generated. Mean and entropy of volumetric ADC values were compared with molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67). Associations among morphologic features, ADC metrics, and phenotypes (luminal A, luminal B [HER2 negative], luminal B [HER2 positive], HER2 positive, and triple negative) were evaluated.

RESULTS

Mean ADC values were significantly decreased in ER-positive, PR-positive, and HER2-negative tumors (p < 0.01). Ki-67 ≥ 20% tumors demonstrated significantly higher ADC entropy values compared with Ki-67 < 20% tumors (p < 0.001). Luminal A subtype tended to display lower ADC entropy values compared with other subtypes, while HER2-positive subtype tended to display higher mean ADC values. ADC₁₅₀₀ entropy provided superior diagnostic performance over ADC₈₀₀ entropy (p = 0.04). Independent risk factors were ADC₁₅₀₀ entropy (p = 0.002) associated with luminal A, irregular mass shape (p = 0.018) and ADC₁₅₀₀ entropy (p = 0.022) with luminal B (HER2 positive), mean ADC₁₅₀₀ (p = 0.018) with HER2 positive, and smooth mass margin (p = 0.012) and rim enhancement (p = 0.003) with triple negative.

CONCLUSIONS

Mean and entropy of ADC values provided complementary information and added value for evaluating IDC histologic phenotypes. High-b-value ADC₁₅₀₀ may facilitate better phenotype discrimination.

KEY POINTS

• ADC metrics are associated with molecular marker status in IDC. • ADC ₁₅₀₀ improves differentiation of histologic phenotypes compared with ADC ₈₀₀ . • ADC metrics add value to morphologic features in IDC phenotyping.

Histogram Analysis of Diffusion Kurtosis Magnetic Resonance Imaging for Diagnosis of Hepatic Fibrosis

Objective

To investigate the diagnostic value of diffusion kurtosis imaging (DKI) histogram analysis in hepatic fibrosis staging.

Materials and Methods

Thirty-six rats were divided into carbon tetrachloride-induced fibrosis groups (6 rats per group for 2, 4, 6, and 8 weeks) and a control group (n = 12). MRI was performed using a 3T scanner. Histograms of DKI were obtained for corrected apparent diffusion (D), kurtosis (K) and apparent diffusion coefficient (ADC). Mean, median, skewness, kurtosis and 25th and 75th percentiles were generated and compared according to the fibrosis stage and inflammatory activity.

Results

A total of 35 rats were included, and 12, 5, 5, 6, and 7 rats were diagnosed as F0–F4. The mean, median, 25th and 75th percentiles, kurtosis of D map, median, 25th percentile, skewness of K map, and 75th percentile of ADC map demonstrated significant correlation with fibrosis stage (r = −0.767 to 0.339, p < 0.001 to p = 0.039). The fibrosis score was the independent variable associated with histogram parameters compared with inflammatory activity grade (p < 0.001 to p = 0.041), except the median of K map (p = 0.185). Areas under the receiver operating characteristic curve of D were larger than K and ADC maps in fibrosis staging, although no significant differences existed in pairwise comparisons (p = 0.0512 to p = 0.847).

Conclusion

Corrected apparent diffusion of DKI histogram analysis provides added value and better diagnostic performance to detect various liver fibrosis stages compared with ADC.

Staging liver fibrosis with DWI: is there an added value for diffusion kurtosis imaging?

OBJECTIVES

To assess liver fibrosis in patients with chronic liver disease using diffusion kurtosis imaging (DKI) in comparison with conventional diffusion-weighted imaging, with histology as reference standard.

METHODS

This prospective study included 81 patients and DKI with b-values of 0, 200, 500, 1,000, 1,500, 2,000 s/mm² were performed. Mean diffusivity (MD), mean kurtosis (MK) and apparent diffusion coefficient (ADC) maps were calculated. The diagnostic efficacy of MD, MK and ADC for predicting stage 2 fibrosis or greater, and stage 3 fibrosis or greater were compared.

RESULTS

The MD (rho=-0.491, p<0.001), MK (rho=0.537, p<0.001) and ADC (rho=-0.496, p<0.001) correlated significantly with fibrosis stages, and ADC exhibited a strong negative correlation with MK (rho=-0.968; p<0.001) and a moderate association with MD (rho=0.601, p<0.001). Areas under the curves (AUCs) for predicting stage 2 fibrosis or greater were not significantly different (p>0.05) between MK (0.809) and ADC (0.797) as well as between MD (0.715) and ADC. AUCs were also similar for MD (0.710), MK (0.768) and ADC (0.747) for predicting stage 3 fibrosis or greater.

CONCLUSION

Although DKI is feasible for predicting liver fibrosis in patients with chronic liver disease, MD and MK offer similar diagnostic performance to ADC values.

KEY POINTS

• Diffusion kurtosis imaging is feasible for staging liver fibrosis. • Diffusion kurtosis and monoexponential model are highly correlated. • The kurtosis model offers no added value to the conventional, monoexponential model.

Staging of rat liver fibrosis using monoexponential, stretched exponential and diffusion kurtosis models with diffusion weighted imaging- magnetic resonance

Early diagnosis of liver fibrosis is important. The objective of this study was to explore the characteristics and to assess the accuracy of monoexponential, stretched exponential models (SEM), and diffusion kurtosis imaging (DKI) with diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI) in various stages of liver fibrosis in two standard rat models induced by carbon tetrachloride (CCl₄) and biliary duct ligation (BDL). Parameters (ADC, D_app, K_app, DDC, α) were measured with a 3.0T MRI. Liver fibrosis stages (F0–F4) were defined by METAVIR scoring. Parameters (ADC, D_app, DDC) were found to be negatively associated (r: -0.675~-0.789; P<0.05) with advancement of fibrosis stage. The analysis of receiver operating characteristic (ROC) curves illustrated that the areas under the curves (AUC) for ADC, D_app, and DDC were 0.687~0.957, 0.805~0.938 and 0.876~1.000, respectively. The study showed that (ADC, D_app, K_app, DDC, α) from various diffusion models reflected pathological and physiological tissue changes. We conclude that SEM and DKI may provide more accurate information about diffusion, and non-Gaussian diffusion analysis may be a complementary tool for the assessment of liver fibrosis.

Liver fibrosis: in vivo evaluation using intravoxel incoherent motion-derived histogram metrics with histopathologic findings at 3.0 T

PURPOSE

To retrospectively determine the feasibility of intravoxel incoherent motion (IVIM) imaging based on histogram analysis for the staging of liver fibrosis (LF) using histopathologic findings as the reference standard.

METHODS

56 consecutive patients (14 men, 42 women; age range, 15-76, years) with chronic liver diseases (CLDs) were studied using IVIM-DWI with 9 b-values (0, 25, 50, 75, 100, 150, 200, 500, 800 s/mm²) at 3.0 T. Fibrosis stage was evaluated using the METAVIR scoring system. Histogram metrics including mean, standard deviation (Std), skewness, kurtosis, minimum (Min), maximum (Max), range, interquartile (Iq) range, and percentiles (10, 25, 50, 75, 90th) were extracted from apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) maps. All histogram metrics among different fibrosis groups were compared using one-way analysis of variance or nonparametric Kruskal-Wallis test. For significant parameters, receivers operating characteristic curve (ROC) analyses were further performed for the staging of LF.

RESULTS

Based on their METAVIR stage, the 56 patients were reclassified into three groups as follows: F0-1 group (n = 25), F2-3 group (n = 21), and F4 group (n = 10). The mean, Iq range, percentiles (50, 75, and 90th) of D* maps between the groups were significant differences (all P < 0.05). Area under the ROC curve (AUC) of the mean, Iq range, 50, 75, and 90th percentile of D* maps for identifying significant LF (≥F2 stage) was 0.901, 0.859, 0.876, 0.943, and 0.886 (all P < 0.0001), respectively; for diagnosing severe fibrosis or cirrhosis (F4), AUC was 0.917, 0.922, 0.943, 0.985, and 0.939 (all P < 0.0001), respectively. The histogram metrics of ADC, D, and f maps demonstrated no significant difference among the groups (all P > 0.05).

CONCLUSION

Histogram analysis of D* map derived from IVIM can be used to stage liver fibrosis in patients with CLDs and provide more quantitative information beyond the mean value.

Supersonic Shear Wave Imaging of the Spleen for Staging of Liver Fibrosis in Rats

The goal of the work described here was to explore the cause of spleen stiffness (SS) in hepatic fibrogenesis and evaluate the value of SS in liver fibrosis (LF) staging. LF was induced with carbon tetrachloride (CCl₄) in rats (n = 40). Supersonic shear wave imaging and contrast-enhanced ultrasound were performed to determine liver stiffness (LS), SS and splenic hemodynamics. SS, LS and free portal pressure exhibited moderate correlations with fibrosis stage (r = 0.744-0.835, p < 0.001). Time-intensity curves of contrast-enhanced ultrasound for the spleen were presented as decreasing peak intensity and slope of decrease, and increasing time to peak. Splenic sinus dilation and congestion were observed on histopathologic analysis. The area under the receiver operating characteristic curve of SS was higher than that of LS for differentiating LF stages 0-2 from stages 3-4 (Z = 2.293, p = 0.02). SS is a reliable diagnostic marker for the assessment of LF in the CCl₄ model, especially for severe fibrosis. Elevated portal pressure is the cause of increasing SS.

The qualitative and quantitative image analysis of MR imaging in patients with acute-on-chronic liver failure

PURPOSE

To evaluate the distinctive features of ACLF and chronic liver disease (CLD) on MR images using quantitative and qualitative analyses.

METHODS

Twelve patients with ACLF and 36 patients with CLD who had undergone MR images were included. MR imaging findings from both groups were assessed.

RESULTS

Gallbladder edema, esophageal varix, and ascites were significantly more prevalent in the ACLF group (all P-values <0.05). The liver to muscle SI ratio on T2-WI was significantly higher in the ACLF group (P=0.002).

CONCLUSION

MR imaging findings could be helpful in differentiating between patients with ACLF and those with CLD.

Feasibility of histogram analysis of susceptibility-weighted MRI for staging of liver fibrosis

PURPOSE

We aimed to evaluate whether histogram analysis of susceptibility-weighted imaging (SWI) could quantify liver fibrosis grade in patients with chronic liver disease (CLD).

METHODS

Fifty-three patients with CLD who underwent multi-echo SWI (TEs of 2.5, 5, and 10 ms) were included. Histogram analysis of SWI images were performed and mean, variance, skewness, kurtosis, and the 1st, 10th, 50th, 90th, and 99th percentiles were derived. Quantitative histogram parameters were compared. For significant parameters, further receiver operating characteristic (ROC) analyses were performed to evaluate the potential diagnostic performance for differentiating liver fibrosis stages.

RESULTS

The number of patients in each pathologic fibrosis grade was 7, 3, 5, 5, and 33 for F0, F1, F2, F3, and F4, respectively. The results of variance (TE: 10 ms), 90th percentile (TE: 10 ms), and 99th percentile (TE: 10 and 5 ms) in F0-F3 group were significantly lower than in F4 group, with areas under the ROC curves (AUCs) of 0.84 for variance and 0.70-0.73 for the 90th and 99th percentiles, respectively. The results of variance (TE: 10 and 5 ms), 99th percentile (TE: 10 ms), and skewness (TE: 2.5 and 5 ms) in F0-F2 group were smaller than those of F3/F4 group, with AUCs of 0.88 and 0.69 for variance (TE: 10 and 5 ms, respectively), 0.68 for 99th percentile (TE: 10 ms), and 0.73 and 0.68 for skewness (TE: 2.5 and 5 ms, respectively).

CONCLUSION

Magnetic resonance histogram analysis of SWI, particularly the variance, is promising for predicting advanced liver fibrosis and cirrhosis.

Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom

Our aim was to evaluate changes in texture features based on variations in CT parameters on a phantom. Scans were performed with varying milliampere, kilovolt, section thickness, pitch, and acquisition mode. Forty-two texture features were extracted by using an in-house-developed Matlab program. Two-tailed t tests and false-detection analyses were performed with significant differences in texture features based on detector array configurations (Q values = 0.001-0.006), section thickness (Q values = 0.0002-0.001), and acquisition mode (Q values = 0.003-0.006). Variations in milliampere and kilovolt had no significant effect.

Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis

PURPOSE

A meta-analysis was performed to assess the diagnostic performance of diffusion-weighted imaging (DWI) in liver fibrosis (LF) staging.

METHODS

We conducted a comprehensive literature search to identify relevant articles. Diagnostic data were extracted for each METAVIR fibrosis stage (F0-F4). A bivariate binomial model was used to combine sensitivities and specificities. Summary receiver operating characteristics (SROC) curves were performed and areas under SROC curve (AUC) were calculated to indicate diagnostic accuracies. Subgroup analyses were performed between different study characteristics.

RESULTS

Twelve studies met the inclusion criteria for LF ≥F1, 16 for ≥F2, 18 for ≥F3, and 12 for F4. AUCs of DWI were 0.8554, 0.8770, 0.8836, and 0.8596 for ≥F1, ≥F2, ≥F3, and F4, respectively. Subgroup analyses showed that for LF ≥F2 and ≥F3, maximal b values (b _max) ≥ 800 s/mm² performed significantly better than b _max < 800 s/mm². The diagnostic accuracies of 3.0 T and intravoxel incoherent motion (IVIM)-DWI were significantly higher than those of 1.5 T and conventional DWI for diagnosing liver cirrhosis (F4).

CONCLUSIONS

DWI is a reliable noninvasive technique with good diagnostic accuracy for LF staging. Using b _max ≥ 800 s/mm², high-field strength (3.0 T) and IVIM-DWI can optimize the diagnostic performance of DWI.

Characterization of a rat orthotopic pancreatic head tumor model using three-dimensional and quantitative multi-parametric MRI

The purpose of this study was to investigate the reliability of 3D isotropic MRI and quantitative multi-parametric MRI characterization on an orthotopic pancreatic head tumor model in rats. 3D isotropic T₂ -weighted MRI was performed as a routine for tumor longitudinal follow-up and volume estimation. Common bile duct diameter was measured from 3D multiplanar reconstruction. Quantitative multi-parametric measurements including pixel-wise T₂ , T₁ relaxivity, apparent diffusion coefficient (ADC) and apparent diffusion kurtosis mapping were performed twice throughout tumor growth. Semi-quantitative and quantitative analyses based on an extended Tofts model were applied to region-of-interest-based dynamic contrast-enhanced imaging, followed by contrast ratio measurement on standard contrast-enhanced imaging. Moreover, low-level texture-based analysis was inspected for T₂ , T₁ , ADC and contrast ratio measurements. Results indicated that multi-parametric MRI showed good reproducibility for tumor characterization; the measurements were not affected by tumor growth. Tumor growth was further confirmed with histology examinations. To conclude, state-of-the-art clinical MRI techniques were translated to this preclinical tumor model with high reliability, and have paved the way for translational oncology studies on this tumor model.