Semiautomated segmentation of hepatocellular carcinoma tumors with MRI using convolutional neural networks.

OBJECTIVE: To assess the performance of convolutional neural networks (CNNs) for semiautomated segmentation of hepatocellular carcinoma (HCC) tumors on MRI. METHODS: This retrospective single-center study included 292 patients (237 M/55F, mean age 61 years) with pathologically confirmed HCC between 08/2015 and 06/2019 and who underwent MRI before surgery. The dataset was randomly divided into training (n = 195), validation (n = 66), and test sets (n = 31). Volumes of interest (VOIs) were manually placed on index lesions by 3 independent radiologists on different sequences (T2-weighted imaging [WI], T1WI pre-and post-contrast on arterial [AP], portal venous [PVP], delayed [DP, 3 min post-contrast] and hepatobiliary phases [HBP, when using gadoxetate], and diffusion-weighted imaging [DWI]). Manual segmentation was used as ground truth to train and validate a CNN-based pipeline. For semiautomated segmentation of tumors, we selected a random pixel inside the VOI, and the CNN provided two outputs: single slice and volumetric outputs. Segmentation performance and inter-observer agreement were analyzed using the 3D Dice similarity coefficient (DSC). RESULTS: A total of 261 HCCs were segmented on the training/validation sets, and 31 on the test set. The median lesion size was 3.0 cm (IQR 2.0-5.2 cm). Mean DSC (test set) varied depending on the MRI sequence with a range between 0.442 (ADC) and 0.778 (high b-value DWI) for single-slice segmentation; and between 0.305 (ADC) and 0.667 (T1WI pre) for volumetric-segmentation. Comparison between the two models showed better performance in single-slice segmentation, with statistical significance on T2WI, T1WI-PVP, DWI, and ADC. Inter-observer reproducibility of segmentation analysis showed a mean DSC of 0.71 in lesions between 1 and 2 cm, 0.85 in lesions between 2 and 5 cm, and 0.82 in lesions > 5 cm. CONCLUSION: CNN models have fair to good performance for semiautomated HCC segmentation, depending on the sequence and tumor size, with better performance for the single-slice approach. Refinement of volumetric approaches is needed in future studies. KEY POINTS: • Semiautomated single-slice and volumetric segmentation using convolutional neural networks (CNNs) models provided fair to good performance for hepatocellular carcinoma segmentation on MRI. • CNN models' performance for HCC segmentation accuracy depends on the MRI sequence and tumor size, with the best results on diffusion-weighted imaging and T1-weighted imaging pre-contrast, and for larger lesions.

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.

BACKGROUND: Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. PURPOSE: To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms. STUDY TYPE: Prospective. POPULATION: Thirty-three patients prospectively imaged prior to prostatectomy. FIELD STRENGTH/SEQUENCE: 3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence. ASSESSMENT: Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC). STATISTICAL TEST: Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant. RESULTS: The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72-0.76, 0.76-0.81, and 0.76-0.80 respectively) as compared to bi-exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53-0.80, 0.51-0.81, and 0.52-0.80 respectively). Post-processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size. DATA CONCLUSION: We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post-processing decisions on DWI data can affect sensitivity and specificity when applied to radiological-pathological studies in prostate cancer. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

Assessment of HCC response to Yttrium-90 radioembolization with gadoxetate disodium MRI: correlation with histopathology.

BACKGROUND AND AIMS: Transarterial 90Y radioembolization (TARE) is increasingly being used for hepatocellular carcinoma (HCC) treatment. However, tumor response assessment after TARE may be challenging. We aimed to assess the diagnostic performance of gadoxetate disodium MRI for predicting complete pathologic necrosis (CPN) of HCC treated with TARE, using histopathology as the reference standard. METHODS: This retrospective study included 48 patients (M/F: 36/12, mean age: 62 years) with HCC treated by TARE followed by surgery with gadoxetate disodium MRI within 90 days of surgery. Two radiologists evaluated tumor response using RECIST1.1, mRECIST, EASL, and LI-RADS-TR criteria and evaluated the percentage of necrosis on subtraction during late arterial, portal venous, and hepatobiliary phases (AP/PVP/HBP). Statistical analysis included inter-reader agreement, correlation between radiologic and pathologic percentage of necrosis, and prediction of CPN using logistic regression and ROC analyses. RESULTS: Histopathology demonstrated 71 HCCs (2.8 ± 1.7 cm, range: 0.5-7.5 cm) including 42 with CPN, 22 with partial necrosis, and 7 without necrosis. EASL and percentage of tumor necrosis on subtraction at the AP/PVP were independent predictors of CPN (p = 0.02-0.03). Percentage of necrosis, mRECIST, EASL, and LI-RADS-TR had fair to good performance for diagnosing CPN (AUCs: 0.78 - 0.83), with a significant difference between subtraction and LI-RADS-TR for reader 2, and in specificity between subtraction and other criteria for both readers (p-range: 0.01-0.04). Radiologic percentage of necrosis was significantly correlated to histopathologic degree of tumor necrosis (r = 0.66 - 0.8, p < 0.001). CONCLUSIONS: Percentage of tumor necrosis on subtraction and EASL criteria were significant independent predictors of CPN in HCC treated with TARE. Image subtraction should be considered for assessing HCC response to TARE when using MRI. KEY POINTS: • Percentage of tumor necrosis on image subtraction and EASL criteria are significant independent predictors of complete pathologic necrosis in hepatocellular carcinoma treated with90Y radioembolization. • Subtraction, mRECIST, EASL, and LI-RADS-TR have fair to good performance for diagnosing complete pathologic necrosis in hepatocellular carcinoma treated with90Y radioembolization.

Precision of MRI radiomics features in the liver and hepatocellular carcinoma.

OBJECTIVES: To assess the precision of MRI radiomics features in hepatocellular carcinoma (HCC) tumors and liver parenchyma. METHODS: The study population consisted of 55 patients, including 16 with untreated HCCs, who underwent two repeat contrast-enhanced abdominal MRI exams within 1 month to evaluate: (1) test-retest repeatability using the same MRI system (n = 28, 10 HCCs); (2) inter-platform reproducibility between different MRI systems (n = 27, 6 HCCs); (3) inter-observer reproducibility (n = 16, 16 HCCs). Shape and 1st- and 2nd-order radiomics features were quantified on pre-contrast T1-weighted imaging (WI), T1WI portal venous phase (pvp), T2WI, and ADC (apparent diffusion coefficient), on liver regions of interest (ROIs) and HCC volumes of interest (VOIs). Precision was assessed by calculating intraclass correlation coefficient (ICC), concordance correlation coefficient (CCC), and coefficient of variation (CV). RESULTS: There was moderate to excellent test-retest repeatability of shape and 1st- and 2nd-order features for all sequences in HCCs (ICC: 0.53-0.99; CV: 3-29%), and moderate to good test-retest repeatability of 1st- and 2nd-order features for T1WI sequences, and 2nd-order features for T2WI in the liver (ICC: 0.53-0.73; CV: 12-19%). There was poor inter-platform reproducibility for all features and sequences, except for shape and 1st-order features on T1WI in HCCs (CCC: 0.58-0.99; CV: 3-15%). Good to excellent inter-observer reproducibility was found for all features and sequences in HCCs (CCC: 0.80-0.99; CV: 4-15%) and moderate to good for liver (CCC: 0.45-0.86; CV: 6-25%). CONCLUSIONS: MRI radiomics features have acceptable repeatability in the liver and HCC when using the same MRI system and across readers but have low reproducibility across MR systems, except for shape and 1st-order features on T1WI. Data must be interpreted with caution when performing multiplatform radiomics studies. KEY POINTS: • MRI radiomics features have acceptable repeatability when using the same MRI system but less reproducible when using different MRI platforms. • MRI radiomics features extracted from T1 weighted-imaging show greater stability across exams than T2 weighted-imaging and ADC. • Inter-observer reproducibility of MRI radiomics features was found to be good in HCC tumors and acceptable in liver parenchyma.

MR elastography outperforms shear wave elastography for the diagnosis of clinically significant portal hypertension.

OBJECTIVES: Portal hypertension (PH) is associated with complications such as ascites and esophageal varices and is typically diagnosed through invasive hepatic venous pressure gradient (HVPG) measurement, which is not widely available. In this study, we aim to assess the diagnostic performance of 2D/3D MR elastography (MRE) and shear wave elastography (SWE) measures of liver and spleen stiffness (LS and SS) and spleen volume, to noninvasively diagnose clinically significant portal hypertension (CSPH) using HVPG measurement as the reference. METHODS: In this prospective study, patients with liver disease underwent 2D/3D MRE and SWE of the liver and spleen, as well as HVPG measurement. The correlation between MRE/SWE measures of LS/SS and spleen volume with HVPG was assessed. ROC analysis was used to determine the utility of MRE, SWE, and spleen volume for diagnosing CSPH. RESULTS: Thirty-six patients (M/F 22/14, mean age 55 ± 14 years) were included. Of the evaluated parameters, 3D MRE SS had the strongest correlation with HVPG (r = 0.686, p < 0.001), followed by 2D MRE SS (r = 0.476, p = 0.004). 3D MRE SS displayed the best performance for diagnosis of CSPH (AUC = 0.911) followed by 2D MRE SS (AUC = 0.845) and 3D MRE LS (AUC = 0.804). SWE SS showed poor performance for diagnosis of CSPH (AUC = 0.583) while spleen volume was a fair predictor (AUC = 0.738). 3D MRE SS was significantly superior to SWE LS/SS (p ≤ 0.021) for the diagnosis of CSPH. CONCLUSION: SS measured with 3D MRE outperforms SWE for the diagnosis of CSPH. SS appears to be a useful biomarker for assessing PH severity. These results need further validation. KEY POINTS: • Spleen stiffness measured with 2D and 3D MR elastography correlates significantly with hepatic venous pressure gradient measurement. • Spleen stiffness measured with 3D MR elastography demonstrates excellent performance for the diagnosis of clinically significant portal hypertension (AUC 0.911). • Spleen stiffness measured with 3D MR elastography outperforms liver and spleen stiffness measured with shear wave elastography for diagnosis of clinically significant portal hypertension.

Integrated quantitative susceptibility and R2 * mapping for evaluation of liver fibrosis: An ex vivo feasibility study.

To develop a method for noninvasive evaluation of liver fibrosis, we investigated the differential sensitivities of quantitative susceptibility mapping (QSM) and R2 * mapping using corrections for the effects of liver iron. Liver fibrosis is characterized by excessive accumulation of collagen and other extracellular matrix proteins. While collagen increases R2 * relaxation, measures of R2 * for fibrosis are confounded by liver iron, which may be present in the liver over a wide range of concentrations. The diamagnetic collagen contribution to susceptibility values measured by QSM is much less than the contribution of highly paramagnetic iron. In 19 ex vivo liver explants with and without fibrosis, QSM (χ), R2 * and proton density fat fraction (PDFF) maps were constructed from multiecho gradient-recalled echo (mGRE) sequence acquisition at 3 T. Median parameter values were recorded and differences between the MRI parameters in nonfibrotic vs. advanced fibrotic/cirrhotic samples were evaluated using Mann-Whitney U tests and receiver operating characteristic analyses. Logistic regression with stepwise feature selection was employed to evaluate the utility of combined MRI measurements for detection of fibrosis. Median R2 * increased in fibrotic vs. nonfibrotic liver samples (P = .041), while differences in χ and PDFF were nonsignificant (P = .545 and P = .395, respectively). Logistic regression identified the combination of χ and R2 * significant for fibrosis detection (logit [prediction] = -8.45 + 0.23 R2 * - 28.8 χ). For this classifier, a highly significant difference between nonfibrotic vs. advanced fibrotic/cirrhotic samples was observed (P = .002). The model exhibited an AUC of 0.909 (P = .003) for detection of advanced fibrosis/cirrhosis, which was substantially higher compared with AUCs of the individual parameters (AUC 0.591-0.784). An integrated QSM and R2 * analysis of mGRE 3 T imaging data is promising for noninvasive diagnostic assessment of liver fibrosis.

Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions.

BACKGROUND: While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions typically warrant prostate biopsy and PI-RADS 1 and 2 lesions may be safely observed, PI-RADS 3 lesions are equivocal. PURPOSE: To construct and cross-validate a machine learning model based on radiomics features from T2 -weighted imaging (T2 WI) of PI-RADS 3 lesions to identify clinically significant prostate cancer (csPCa), that is, pathological Grade Group ≥ 2. STUDY TYPE: Single-center retrospective study. POPULATION: A total of 240 patients were included (training cohort, n = 188, age range 43-82 years; test cohort, n = 52, age range 41-79 years). Eligibility criteria were 1) magnetic resonance imaging (MRI)-targeted biopsy between 2015 and 2020; 2) PI-RADS 3 index lesion identified on multiparametric MRI; (3) biopsy performed within 1 year of MRI. The percentages of csPCa lesions were 10.6% and 15.4% in the training and test cohorts, respectively. FIELD STRENGTH/SEQUENCE: A 3 T; T2 WI turbo-spin echo, diffusion-weighted spin-echo echo planar imaging, dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging. ASSESSMENT: Multislice volumes-of-interest (VOIs) were drawn in the PI-RADS 3 index lesions on T2 WI. A total of 107 radiomics features (first-order histogram and second-order texture) were extracted from the segmented lesions. STATISTICAL TESTS: A random forest classifier using the radiomics features as input was trained and validated for prediction of csPCa. The performance of the machine learning classifier, prostate specific antigen (PSA) density, and prostate volume for csPCa prediction was evaluated using receiver operating characteristic (ROC) analysis. RESULTS: The trained random forest classifier constructed from the T2 WI radiomics features good and statistically significant area-under-the-curves (AUCs) of 0.76 (P = 0.022) for prediction of csPCa in the test set. Prostate volume and PSA density showed moderate and nonsignificant performance (AUC 0.62, P = 0.275 and 0.61, P = 0.348, respectively) for csPCa prediction in the test set. CONCLUSION: The machine learning classifier based on T2 WI radiomic features demonstrated good performance for prediction of csPCa in PI-RADS 3 lesions. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: 2.

Early effect of 90Y radioembolisation on hepatocellular carcinoma and liver parenchyma stiffness measured with MR elastography: initial experience.

OBJECTIVES: To quantify hepatocellular carcinoma (HCC) and liver parenchyma stiffness using MR elastography (MRE) and serum alpha fetoprotein (AFP), before and 6 weeks (6w) after 90Y radioembolisation (RE), and to assess the value of baseline tumour and liver stiffness (TS/LS) and AFP in predicting response at 6w and 6 months (6 m). METHODS: Twenty-three patients (M/F 18/5, mean age 68.3 ± 9.3 years) scheduled to undergo RE were recruited into this prospective single-centre study. Patients underwent an MRI exam at baseline and 6w following RE (range 39-47 days) which included MRE using a prototype 2D EPI sequence. TS, peritumoural LS/LS remote from the tumour, tumour size, and AFP were measured at baseline and at 6w. Treatment response was determined using mRECIST at 6w and 6 m. RESULTS: MRE was technically successful in 17 tumours which were classified at 6w as complete response (CR, n = 7), partial response (PR, n = 4), and stable disease (SD, n = 6). TS and peritumoural LS were significantly increased following RE (p = 0.016, p = 0.039, respectively), while LS remote from tumour was unchanged (p = 0.245). Baseline TS was significantly lower in patients who achieved CR at 6w (p = 0.014). Baseline TS, peritumoural LS (both AUC = 0.857), and AFP (AUC = 0.798) showed fair/excellent diagnostic performance in predicting CR at 6w, but were not significant predictors of OR or CR at 6 m. CONCLUSION: Our initial results suggest that HCC TS and peritumoural LS increase early after RE. Baseline TS, peritumoural LS, and AFP were all significant predictors of CR to RE at 6w. These results should be confirmed in a larger study. KEY POINTS: • Magnetic resonance elastography-derived tumour stiffness and peritumoural liver stiffness increase significantly at 6 weeks post radioembolisation whereas liver stiffness remote from the tumour is unchanged. • Baseline tumour stiffness and peritumoural liver stiffness are lower in patients who achieve complete response at 6 weeks post radioembolisation. • Baseline tumour size is significantly correlated with baseline tumour stiffness.

Noninvasive diagnosis of portal hypertension using gadoxetate DCE-MRI of the liver and spleen.

OBJECTIVES: To assess the performance of gadoxetate dynamic contrast-enhanced (DCE) MRI of the liver and spleen for noninvasive diagnosis of portal hypertension (PH). METHODS: Thirty-five patients (M/F 22/13, mean age 55 years) with chronic liver disease who underwent hepatic venous pressure gradient (HVPG) measurements were prospectively enrolled in this IRB-approved study. All patients underwent multiparametric MRI including gadoxetate DCE-MRI acquisition. Model-based and model-free DCE-MRI analyses were performed. The correlation between DCE-MRI parameters and HVPG was assessed. ROC analysis was employed to determine the diagnostic performance of DCE-MRI parameters alone and in combination for prediction of PH and clinically significant (CS)PH (HVPG > 5 and ≥ 10 mmHg, respectively). RESULTS: Mean HVPG was 7.0 ± 5.0 mmHg (range 0-18 mmHg). Twenty-one (60%) patients had PH, of whom 9 had CSPH. Modeled liver uptake fraction fi and uptake rate ki and model-free parameters liver upslope and uptake were all significantly negatively correlated with HVPG (r range - 0.490 to - 0.398, p value range 0.003-0.018), while spleen interstitial fraction ve was significantly positively correlated with HVPG (r = 0.336, p = 0.048). For PH diagnosis, liver ki showed the best diagnostic performance with an AUC, sensitivity, and specificity of 0.74 (confidence interval (CI) 0.57-0.91), 71.4%, and 78.6%. The combination of liver ki and spleen ve was selected as the best classifier for diagnosis of CSPH with an AUC, sensitivity, and specificity of 0.87 (CI 0.75-0.99), 100%, and 73.1%. CONCLUSIONS: Our results demonstrate the potential utility of hepatocyte uptake parameters and spleen interstitial fraction obtained with gadoxetate DCE-MRI for the diagnosis of PH and CSPH. KEY POINTS: • Liver uptake and spleen interstitial fraction estimates from gadoxetate DCE-MRI are significantly correlated with portal pressure measurements. • Liver uptake rate shows good diagnostic performance for the diagnosis of portal hypertension. • The combination of liver uptake rate with spleen interstitial fraction exhibits excellent diagnostic performance for the diagnosis of clinically significant portal hypertension.

Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid-enhanced MRI.

OBJECTIVES: To (1) develop a fully automated deep learning (DL) algorithm based on gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and (2) compare the diagnostic performance of DL vs. MR elastography (MRE) for noninvasive staging of liver fibrosis. METHODS: This single-center retrospective study included 355 patients (M/F 238/117, mean age 60 years; training, n = 178; validation, n = 123; test, n = 54) who underwent gadoxetic acid-enhanced abdominal MRI, including HBP and MRE, and pathological evaluation of the liver within 1 year of MRI. Cropped liver HBP images from a custom-written fully automated liver segmentation were used as input for DL. A transfer learning approach based on the ImageNet VGG16 model was used. Different DL models were built for the prediction of fibrosis stages F1-4, F2-4, F3-4, and F4. ROC analysis was performed to evaluate the performance of DL in training, validation, and test sets and of MRE liver stiffness in the test set. RESULTS: AUC values of DL were 0.99/0.70/0.77 (F1-4), 0.92/0.71/0.91 (F2-4), 0.91/0.78/0.90 (F3-4), and 0.98/0.83/0.85 (F4) for training/validation/test sets, respectively. The AUCs of MRE liver stiffness in the test set were 0.86 (F1-4), 0.87 (F2-4), 0.92 (F3-4), and 0.86 (F4). AUCs of MRE and DL were not significantly different for any of the fibrosis stages (p > 0.134). CONCLUSIONS: The fully automated DL models based on HBP gadoxetic acid MRI showed good-to-excellent diagnostic performance for staging of liver fibrosis, with similar diagnostic performance to MRE. After validation in independent sets, the DL algorithm may allow for noninvasive liver fibrosis assessment without the need for additional MRI hardware. KEY POINTS: • The developed deep learning algorithm, based on routine standard-of-care gadoxetic acid-enhanced MRI data, showed good-to-excellent diagnostic performance for noninvasive staging of liver fibrosis. • The diagnostic performance of the deep learning algorithm was equivalent to that of MR elastography in a separate test set.

4D flow MRI for the assessment of renal transplant dysfunction: initial results.

OBJECTIVES: (1) Determine inter-observer reproducibility and test-retest repeatability of 4D flow parameters in renal allograft vessels; (2) determine if 4D flow measurements in the renal artery (RA) and renal vein (RV) can distinguish between functional and dysfunctional allografts; (3) correlate haemodynamic parameters with estimated glomerular filtration rate (eGFR), perfusion measured with dynamic contrast-enhanced MRI (DCE-MRI) and histopathology. METHODS: Twenty-five prospectively recruited renal transplant patients (stable function/chronic renal allograft dysfunction, 12/13) underwent 4D flow MRI at 1.5 T. 4D flow coronal oblique acquisitions were performed in the transplant renal artery (RA) (velocity encoding parameter, VENC = 120 cm/s) and renal vein (RV) (VENC = 45 cm/s). Test-retest repeatability (n = 3) and inter-observer reproducibility (n = 10) were assessed by Cohen's kappa, coefficient of variation (CoV) and Bland-Altman statistics. Haemodynamic parameters were compared between patients and correlated to the estimated glomerular filtration rate, DCE-MRI parameters (n = 10) and histopathology from allograft biopsies (n = 15). RESULTS: For inter-observer reproducibility, kappa was > 0.99 and 0.62 and CoV of flow was 12.6% and 7.8% for RA and RV, respectively. For test-retest repeatability, kappa was > 0.99 and 0.5 and CoV of flow was 27.3% and 59.4%, for RA and RV, respectively. RA (p = 0.039) and RV (p = 0.019) flow were both significantly reduced in dysfunctional allografts. Both identified chronic allograft dysfunction with good diagnostic performance (RA: AUC = 0.76, p = 0.036; RV: AUC = 0.8, p = 0.018). RA flow correlated negatively with histopathologic interstitial fibrosis score ci (ρ = - 0.6, p = 0.03). CONCLUSIONS: 4D flow parameters had better repeatability in the RA than in the RV. RA and RV flow can identify chronic renal allograft dysfunction, with RA flow correlating with histopathologic interstitial fibrosis score. KEY POINTS: • Inter-observer reproducibility of 4D flow measurements was acceptable in both the transplant renal artery and vein, but test-retest repeatability was better in the renal artery than in the renal vein. • Blood flow measurements obtained with 4D flow MRI in the renal artery and renal vein are significantly reduced in dysfunctional renal transplants. • Renal transplant artery flow correlated negatively with histopathologic interstitial fibrosis score.

Dynamic contrast-enhanced MRI perfusion quantification in hepatocellular carcinoma: comparison of gadoxetate disodium and gadobenate dimeglumine.

OBJECTIVES: (1) To assess the quality of the arterial input function (AIF) during dynamic contrast-enhanced (DCE) MRI of the liver and (2) to quantify perfusion parameters of hepatocellular carcinoma (HCC) and liver parenchyma during the first 3 min post-contrast injection with DCE-MRI using gadoxetate disodium compared to gadobenate dimeglumine (Gd-BOPTA) in different patient populations. METHODS: In this prospective study, we evaluated 66 patients with 83 HCCs who underwent DCE-MRI, using gadoxetate disodium (group 1, n = 28) or Gd-BOPTA (group 2, n = 38). AIF qualitative and quantitative features were assessed. Perfusion parameters (based on the initial 3 min post-contrast) were extracted in tumours and liver parenchyma, including model-free parameters (time-to-peak enhancement (TTP), time-to-washout) and modelled parameters (arterial flow (Fa), portal venous flow (Fp), total flow (Ft), arterial fraction, mean transit time (MTT), distribution volume (DV)). In addition, lesion-to-liver contrast ratios (LLCRs) were measured. Fisher's exact tests and Mann-Whitney U tests were used to compare the two groups. RESULTS: AIF quality, modelled and model-free perfusion parameters in HCC were similar between the 2 groups (p = 0.054-0.932). Liver parenchymal flow was lower and liver enhancement occurred later in group 1 vs group 2 (Fp, p = 0.002; Ft, p = 0.001; TTP, MTT, all p < 0.001), while there were no significant differences in tumour LLCR (max. positive LLCR, p = 0.230; max. negative LLCR, p = 0.317). CONCLUSION: Gadoxetate disodium provides comparable AIF quality and HCC perfusion parameters compared to Gd-BOPTA during dynamic phases. Despite delayed and decreased liver enhancement with gadoxetate disodium, LLCRs were equivalent between contrast agents, indicating similar tumour conspicuity. KEY POINTS: • Arterial input function quality, modelled, and model-free dynamic parameters measured in hepatocellular carcinoma are similar in patients receiving gadoxetate disodium or gadobenate dimeglumine during the first 3 min post injection. • Gadoxetate disodium and gadobenate dimeglumine show similar lesion-to-liver contrast ratios during dynamic phases in patients with HCC. • There is lower portal and lower total hepatic flow and longer hepatic mean transit time and time-to-peak with gadoxetate disodium compared to gadobenate dimeglumine.

Multiparametric magnetic resonance imaging shows promising results to assess renal transplant dysfunction with fibrosis.

Here we assessed the diagnostic value of a quantitative multiparametric magnetic resonance imaging (mpMRI) protocol for evaluation of renal allograft dysfunction with fibrosis. Twenty-seven renal transplant patients, including 15 with stable functional allografts (eGFR mean 71.5 ml/min/1.73m2), and 12 with chronic dysfunction/established fibrosis (eGFR mean 30.1 ml/min/1.73m2), were enrolled in this prospective single-center study. Sixteen of the patients had renal biopsy (mean 150 days) before the MRI. All patients underwent mpMRI at 1.5T including intravoxel-incoherent motion diffusion-weighted imaging, diffusion tensor imaging, blood oxygen level dependent (BOLD R2*) and T1 quantification. True diffusion D, pseudodiffusion D*, perfusion fraction PF, apparent diffusion coefficient (ADC), fractional anisotropy (FA), R2* and T1 were calculated for cortex and medulla. ΔT1 was calculated as (100x(T1 Cortex-T1 Medulla)/T1 Cortex). Test-retest repeatability and inter-observer reproducibility were assessed in four and ten patients, respectively. mpMRI parameters had substantial test-retest and interobserver repeatability (coefficient of variation under 15%), except for medullary PF and D* (coefficient of variation over 25%). Cortical ADC, D, medullary ADC and ΔT1 were all significantly decreased, while cortical T1 was significantly elevated in fibrotic allografts. Cortical T1 showed positive correlation to the Banff fibrosis and tubular atrophy scores. The combination of ΔT1 and cortical ADC had excellent cross-validated diagnostic performance for detection of chronic dysfunction with fibrosis. Cortical ADC and T1 had good performance for predicting eGFR decline at 18 months (4 or more ml/min/1.73m2/year). Thus, the combination of cortical ADC and T1 measurements shows promising results for the non-invasive assessment of renal allograft histology and outcomes.

Luminal Water Imaging: Comparison With Diffusion-Weighted Imaging (DWI) and PI-RADS for Characterization of Prostate Cancer Aggressiveness.

BACKGROUND: Luminal water imaging (LWI), a multicomponent T2 mapping technique, has shown promise for prostate cancer (PCa) detection and characterization. PURPOSE: To 1) quantify LWI parameters and apparent diffusion coefficient (ADC) in PCa and benign peripheral zone (PZ) tissues; and 2) evaluate the diagnostic performance of LWI, ADC, and PI-RADS parameters for differentiation between low- and high-grade PCa lesions. STUDY TYPE: Prospective. SUBJECTS: Twenty-six PCa patients undergoing prostatectomy (mean age 59 years, range 46-72 years). FIELD STRENGTH/SEQUENCE: Multiparametric MRI at 3.0T, including diffusion-weighted imaging (DWI) and LWI T2 mapping. ASSESSMENT: LWI parameters and ADC were quantified in index PCa lesions and benign PZ. STATISTICAL TESTS: Differences in MRI parameters between PCa and benign PZ were assessed using Wilcoxon signed tests. Spearman correlation of pathological grade group (GG) with LWI parameters, ADC, and PI-RADS was evaluated. The utility of each of the parameters for differentiation between low-grade (GG ≤2) and high-grade (GG ≥3) PCa was determined by Mann-Whitney U tests and ROC analyses. RESULTS: Twenty-six index lesions were analyzed (mean size 1.7 ± 0.8 cm, GG: 1 [n = 1; 4%], 2 [n = 14, 54%], 3 [n = 8, 31%], 5 [n = 3, 12%]). LWI parameters and ADC both showed high diagnostic performance for differentiation between benign PZ and PCa (highest area under the curve [AUC] for LWI parameter T2,short [AUC = 0.98, P < 0.001]). The LWI parameters luminal water fraction (LWF) and amplitude of long T2 component Along significantly correlated with GG (r = -0.441, P = 0.024 and r = -0.414, P = 0.036, respectively), while PI-RADS, ADC, and the other LWI parameters did not (P = 0.132-0.869). LWF and Along also showed significant differences between low-grade and high-grade PCa (AUC = 0.776, P = 0.008 and AUC = 0.758, P = 0.027, respectively). Maximum diagnostic performance for discrimination of high-grade PCa was found with combined LWI parameters (AUC 0.891, P = 0.001). DATA CONCLUSION: LWI parameters, in particular in combination, showed superior diagnostic performance for differentiation between low-grade and high-grade PCa compared to ADC and PI-RADS assessment. J. Magn. Reson. Imaging 2020;52:271-279.

Splenic T1ρ as a noninvasive biomarker for portal hypertension.

BACKGROUND: There is a need for noninvasive methods for the diagnosis and monitoring of portal hypertension (PH). PURPOSE: To 1) assess the correlation of liver and spleen T1 and T1ρ measurements with portal pressures in patients with chronic liver disease, and 2) to compare the diagnostic performance of the relaxation parameters with radiological assessment of PH. STUDY TYPE: Prospective. SUBJECTS: Twenty-five patients (M/F 16/9, mean age 56 years, range 21-78 years) undergoing portal pressure (hepatic venous pressure gradient [HVPG]) measurements. FIELD STRENGTH/SEQUENCE: 1.5T abdominal MRI scan, including T1ρ and T1 mapping. ASSESSMENT: Liver and spleen T1ρ and T1 , radiological PH score, and (normalized) spleen length were evaluated. STATISTICAL TESTS: Spearman correlation of all MRI parameters with HVPG was assessed. The diagnostic performance of the assessed parameters for prediction of PH (HVPG ≥5 mmHg) and clinically significant PH (CSPH, HVPG ≥10 mmHg) was determined by receiver operating characteristic (ROC) analysis. RESULTS: The mean HVPG measurement was 7.8 ± 5.3 mmHg (PH, n = 18 [72%] including CSPH, n = 9 [36%]). PH score, (normalized) spleen length and spleen T1ρ significantly correlated with HVPG, with the strongest correlation found for spleen T1ρ (r = 0.613, P = 0.001). Spleen T1ρ was the only parameter that showed significant diagnostic performance for assessment of PH (area under the curve [AUC] 0.817, P = 0.015) and CSPH (AUC = 0.778, P = 0.024). Normalized spleen length also showed significant diagnostic performance for prediction of CSPH, with a slightly lower AUC (= 0.764, P = 0.031). The radiological PH score, T1ρ and T1 of the liver and T1 of the spleen, did not show significant diagnostic performance for assessment of CSPH (P > 0.075). DATA CONCLUSION: Spleen T1ρ showed a significant correlation with portal pressure and showed improved diagnostic performance for prediction of CSPH compared to radiological assessment. These initial results need confirmation in a larger cohort. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:787-794.

MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma.

OBJECTIVE: To assess the value of qualitative and quantitative MRI radiomics features for noninvasive prediction of immuno-oncologic characteristics and outcomes of hepatocellular carcinoma (HCC). METHODS: This retrospective, IRB-approved study included 48 patients with HCC (M/F 35/13, mean age 60y) who underwent hepatic resection or transplant within 4 months of abdominal MRI. Qualitative imaging traits, quantitative nontexture related and texture features were assessed in index lesions on contrast-enhanced T1-weighted and diffusion-weighted images. The association of imaging features with immunoprofiling and genomics features was assessed using binary logistic regression and correlation analyses. Binary logistic regression analysis was also employed to analyse the association of radiomics, histopathologic and genomics features with radiological early recurrence of HCC at 12 months. RESULTS: Qualitative (r = - 0.41-0.40, p < 0.042) and quantitative (r = - 0.52-0.45, p < 0.049) radiomics features correlated with immunohistochemical cell type markers for T-cells (CD3), macrophages (CD68) and endothelial cells (CD31). Radiomics features also correlated with expression of immunotherapy targets PD-L1 at protein level (r = 0.41-0.47, p < 0.029) as well as PD1 and CTLA4 at mRNA expression level (r = - 0.48-0.47, p < 0.037). Finally, radiomics features, including tumour size, showed significant diagnostic performance for assessment of early HCC recurrence (AUC 0.76-0.80, p < 0.043), while immunoprofiling and genomic features did not (p = 0.098-0929). CONCLUSIONS: MRI radiomics features may serve as noninvasive predictors of HCC immuno-oncological characteristics and tumour recurrence and may aid in treatment stratification of HCC patients. These results need prospective validation. KEY POINTS: • MRI radiomics features showed significant associations with immunophenotyping and genomics characteristics of hepatocellular carcinoma. • Radiomics features, including tumour size, showed significant associations with early hepatocellular carcinoma recurrence after resection.

Radiomics Features Measured with Multiparametric Magnetic Resonance Imaging Predict Prostate Cancer Aggressiveness.

PURPOSE: We sought to 1) assess the association of radiomics features based on multiparametric magnetic resonance imaging with histopathological Gleason score, gene signatures and gene expression levels in prostate cancer and 2) build machine learning models based on radiomics features to predict adverse histopathological scores and the Decipher® genomics metastasis risk score. MATERIALS AND METHODS: We retrospectively analyzed the records of 64 patients with prostate cancer with a mean age of 64 years (range 41 to 76) who underwent magnetic resonance imaging between January 2016 and January 2017 before radical prostatectomy. A total of 226 magnetic resonance imaging radiomics features, including histogram and texture features in addition to lesion size and the PI-RADS™ (Prostate Imaging Reporting and Data System) score, were extracted from T2-weighted, apparent diffusion coefficient and diffusion kurtosis imaging maps. Radiomics features were correlated with the pathological Gleason score, 40 gene expression signatures, including Decipher, and 698 prostate cancer related gene expression levels. Cross-validated, lasso regularized, logistic regression machine learning models based on radiomics features were built and evaluated for the prediction of Gleason score 8 or greater and Decipher score 0.6 or greater. RESULTS: A total of 14 radiomics features significantly correlated with the Gleason score (highest correlation r = 0.39, p = 0.001). A total of 31 texture and histogram features significantly correlated with 19 gene signatures, particularly with the PORTOS (Post-Operative Radiation Therapy Outcomes Score) signature (strongest correlation r = -0.481, p = 0.002). A total of 40 diffusion-weighted imaging features correlated significantly with 132 gene expression levels. Machine learning prediction models showed fair performance to predict a Gleason score of 8 or greater (AUC 0.72) and excellent performance to predict a Decipher score of 0.6 or greater (AUC 0.84). CONCLUSIONS: Magnetic resonance imaging radiomics features are promising markers of prostate cancer aggressiveness on the histopathological and genomics levels.

Hemodynamic measurements with an abdominal 4D flow MRI sequence with spiral sampling and compressed sensing in patients with chronic liver disease.

BACKGROUND: The test-retest/interobserver repeatability and diagnostic value of 4D flow MRI in liver disease is underreported. PURPOSE: To determine the reproducibility/repeatability of flow quantification in abdominal vessels using a spiral 4D flow MRI sequence; to assess the value of 4D flow parameters in diagnosing cirrhosis and degree of portal hypertension. STUDY TYPE: Prospective. SUBJECTS: Fifty-two patients with chronic liver disease. FIELD STRENGTH/SEQUENCE: 1.5T/spiral 4D flow acquired in one breath-hold. ASSESSMENT: Thirteen abdominal vessels were identified and segmented by two independent observers to measure maximum and time-averaged through-plane velocity, net flow, and vessel cross-section area. Interobserver agreement and test-retest repeatability were evaluated in 15 and 4 cases, respectively. Prediction of the presence and severity of cirrhosis and portal hypertension was assessed using 4D flow parameters. STATISTICAL TESTS: Cohen's kappa coefficient, coefficient of variation (CV), Bland-Altman, Mann-Whitney tests, logistic regression. RESULTS: For all vessels combined, measurements showed acceptable agreement between observers, with Cohen's kappa = 0.70 (P < 0.001), CV < 21%, Bland-Altman bias <5%, but high limits of agreement ([-75%,75%]). Test-retest repeatability was excellent in large vessels (CV = 1-15%, bias = 1-25%, Bland-Altman limits of agreement [BALA] = [4%,150%]), and poor in small vessels (CV = 7-130%, bias = 10-200%, BALA = [8%,190%]). Average velocity in the right hepatic vein and average area of the splenic vein were higher in cirrhosis (P = 0.027/0.0039). Flow in the middle hepatic vein strongly correlated with Child-Pugh score (ρ = 0.84, P = 0.0238), while flow in the splenic vein (ρ = 0.43, P = 0.032), time-average (ρ = 0.46, P = 0.02) and peak velocity in the superior mesenteric vein (ρ = 0.45, P = 0.032), and peak velocity in the infrarenal IVC (ρ = 0.39, P = 0.032) positively correlated with an imaging-based portal hypertension score. Average area of the splenic vein predicted cirrhosis (P = 0.019; area under the curve AUC [95% confidence interval, CI] = 0.87 [0.71,1.00]) and clinically significant portal hypertension (P = 0.042; AUC [95% CI] = 0.78 [0.57-0.99]). DATA CONCLUSION: Spiral 4D flow allows comprehensive assessment of abdominal vessels in one breath-hold, with substantial interobserver reproducibility, but variable test-retest repeatability. 4D flow may potentially reflect vascular changes due to cirrhosis and portal hypertension. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:994-1005.

T1ρ mapping for assessment of renal allograft fibrosis.

BACKGROUND: There is an unmet need for noninvasive methods to diagnose and stage renal allograft fibrosis. PURPOSE: To investigate the utility of T1ρ measured with MRI for the assessment of fibrosis in renal allografts. STUDY TYPE: Institutional Review Board (IRB)-approved prospective. SUBJECTS: Fifteen patients with stable functional allograft (M/F 9/6, mean age 56 years) and 12 patients with allograft dysfunction and established fibrosis (M/F 6/6, mean age 51 years). FIELD STRENGTH/SEQUENCE: T1ρ imaging at 1.5T using a custom-developed sequence. ASSESSMENT: Average T1ρ in the cortex and medulla was quantified and T1ρ repeatability (expressed by the coefficient of variation [CV]) was measured in four patients. STATISTICAL TESTS: Differences in T1ρ values between the 2 groups were assessed using Mann-Whitney U-tests. Diagnostic performance of T1ρ for differentiation between functional and fibrotic allografts was evaluated using receiver operating characteristic (ROC) analysis. Spearman correlations of T1ρ with Masson's trichrome-stained fractions and serum estimated glomerular filtration rate (eGFR) were assessed. RESULTS: Higher T1ρ repeatability was found for cortex compared with medulla (mean CV T1ρ cortex 7.4%, medulla 13.3%). T1ρ values were significantly higher in the cortex of fibrotic vs. functional allografts (111.8 ± 17.2 msec vs. 99.0 ± 11.0 msec, P = 0.027), while there was no difference in medullary T1ρ values (122.6 ± 20.8 msec vs. 124.3 ± 20.8 msec, P = 0.789). Cortical T1ρ significantly correlated with Masson's trichrome-stained fractions (r = 0.515, P = 0.044) and eGFR (r = -0.546, P = 0.004), and demonstrated an area under the curve (AUC) of 0.77 for differentiating between functional and fibrotic allografts (sensitivity and specificity of 75.0% and 86.7%, using threshold of 106.9 msec). DATA CONCLUSION: Our preliminary results suggest that T1ρ is a potential imaging biomarker of renal allograft fibrosis. These results should be verified in a larger study. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1085-1091.

Diffusion and perfusion MRI quantification in ileal Crohn's disease.

OBJECTIVES: To quantify intravoxel incoherent motion (IVIM)-DWI and dynamic contrast-enhanced (DCE)-MRI parameters in normal and abnormal ileal segments in Crohn's disease (CD) patients and to assess the association of these parameters with clinical and MRI-based measurements of CD activity. METHODS: In this prospective study, 27 CD patients (M/F 18/9, mean age 42 years) underwent MR enterography, including IVIM-DWI and DCE-MRI. IVIM-DWI and DCE-MRI parameters were quantified in normal and abnormal small bowel segments, the latter identified by the presence of inflammatory changes. MRI parameter differences between normal and abnormal bowel were tested using Wilcoxon signed-rank tests. IVIM-DWI and DCE-MRI parameters were correlated with clinical data (C-reactive protein, Harvey-Bradshaw Index), conventional MRI parameters (wall thickness, length of involvement) and MRI activity scores (MaRIA, Clermont). Diagnostic performance of (combined) parameters for differentiation between normal and abnormal bowel was determined using ROC analysis. RESULTS: The DCE-MRI parameters peak concentration Cpeak, upslope, area-under-the-curve at 60s (AUC60), Ktrans and ve were significantly increased (p<0.023), while IVIM-DWI parameters perfusion fraction (PF) and ADC were significantly decreased (p<0.001) in abnormal bowel segments. None of the DCE-MRI and IVIM-DWI parameters correlated with clinical parameters (p>0.105). DCE-MRI parameters exhibited multiple significant correlations with wall thickness (Cpeak, upslope, AUC60, Ktrans; r range 0.431-0.664, p<0.025) and MaRIA/Clermont scores (Cpeak, AUC60, Ktrans; r range 0.441-0.617, p<0.021). Combined Ktrans+ve+PF+ADC showed highest AUC (0.963) for differentiation between normal and abnormal bowel, while ADC performed best for individual parameters (AUC=0.800). CONCLUSIONS: DCE-MRI and IVIM-DWI, particularly when used in combination, are promising for non-invasive evaluation of small bowel CD. KEY POINTS: • IVIM-DWI and DCE-MRI parameters were significantly different between normal and abnormal bowel segments in CD patients. • DCE-MRI parameters showed a significant association with wall thickness and MRI activity scores. • Combination of IVIM-DWI and DCE-MRI parameters led to the highest diagnostic performance for differentiation between normal and abnormal bowel segments, while ADC showed the highest diagnostic performance of individual parameters.