Fast and accessible T2 mapping using off-resonance corrected DESPOT2 with application to 3D prostate.

PURPOSE: Most T1 and T2 mapping take long acquisitions or needs specialized sequences not widely accessible on clinical scanners. An available solution is DESPOT1/T2 (Driven equilibrium single pulse observation of T1/T2). DESPOT1/T2 uses Spoiled gradient-echo (SPGR) and balanced Steady-State Free Precession (bSSFP) sequences, offering an accessible and reliable way for 3D accelerated T1/T2 mapping. However, bSSFP is prone to off-resonance artifacts, limiting the application of DESPOT2 in regions with high susceptibility contrasts, like the prostate. Our proposal, DESPO+, employs the full bSSFP and SPGR models with a dictionary-based method to reconstruct 3D T1/T2 maps in the prostate region without off-resonance banding. METHODS: DESPO+ modifies the bSSFP acquisition of the original variable flip angle DESPOT2. DESPO+ uses variable repetition and echo times, employing a dictionary-based method of the full bSSFP and SPGR models to reconstruct T1, T2, and Proton Density (PD) simultaneously. The proposed DESPO+ method underwent testing through simulations, T1/T2 phantoms, and on fourteen healthy subjects. RESULTS: The results reveal a significant reduction in T2 map banding artifacts compared to the original DESPOT2 method. DESPO+ approach reduced T2 errors by up to seven times compared to DESPOT2 in simulations and phantom experiments. We also synthesized in-vivo T1-weighted/T2-weighted images from the acquired maps using a spin-echo model to verify the map's quality when lacking a reference. For in-vivo imaging, the synthesized images closely resemble those from the clinical MRI protocol, reducing scan time by around 50% compared to traditional spin-echo T1-weighted/T2-weighted acquisitions. CONCLUSION: DESPO+ provides an off-resonance insensitive and clinically available solution, enabling high-resolution 3D T1/T2 mapping and synthesized T1-weighted/T2-weighted images for the entire prostate, all achieved within a short scan time of 3.6 min, similar to DESPOT1/T2.

MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features.

This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information. METHODS: This retrospective study included 86 adult Hispanic men (60 ± 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL2) with PCa who underwent prebiopsy 3T MRI followed by targeted MRI-ultrasound fusion and systematic biopsy. Two observers performed 2D segmentation of lesions in T2WI/ADC images. We classified csPCa (GS ≥ 7) vs. non-csPCa (GS = 6). Univariate statistical tests were performed for different parameters, including prostate volume (PV), PSA-D, PI-RADS, and radiomic features. Multivariate models were built using the automatic feature selection algorithm Recursive Feature Elimination (RFE) and different classifiers. A stratified split separated the train/test (80%) and validation (20%) sets. RESULTS: Radiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78). CONCLUSION: Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics' (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients.

Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes.

OBJECTIVE: To accurately estimate liver PDFF from chemical shift-encoded (CSE) MRI using a deep learning (DL)-based Multi-Decoder Water-Fat separation Network (MDWF-Net), that operates over complex-valued CSE-MR images with only 3 echoes. METHODS: The proposed MDWF-Net and a U-Net model were independently trained using the first 3 echoes of MRI data from 134 subjects, acquired with conventional 6-echoes abdomen protocol at 1.5 T. Resulting models were then evaluated using unseen CSE-MR images obtained from 14 subjects that were acquired with a 3-echoes CSE-MR pulse sequence with a shorter duration compared to the standard protocol. Resulting PDFF maps were qualitatively assessed by two radiologists, and quantitatively assessed at two corresponding liver ROIs, using Bland Altman and regression analysis for mean values, and ANOVA testing for standard deviation (STD) (significance level: .05). A 6-echo graph cut was considered ground truth. RESULTS: Assessment of radiologists demonstrated that, unlike U-Net, MDWF-Net had a similar quality to the ground truth, despite it considered half of the information. Regarding PDFF mean values at ROIs, MDWF-Net showed a better agreement with ground truth (regression slope = 0.94, R2 = 0.97) than U-Net (regression slope = 0.86, R2 = 0.93). Moreover, ANOVA post hoc analysis of STDs showed a statistical difference between graph cuts and U-Net (p < .05), unlike MDWF-Net (p = .53). CONCLUSION: MDWF-Net showed a liver PDFF accuracy comparable to the reference graph cut method, using only 3 echoes and thus allowing a reduction in the acquisition times. CLINICAL RELEVANCE STATEMENT: We have prospectively validated that the use of a multi-decoder convolutional neural network to estimate liver proton density fat fraction allows a significant reduction in MR scan time by reducing the number of echoes required by 50%. KEY POINTS: • Novel water-fat separation neural network allows for liver PDFF estimation by using multi-echo MR images with a reduced number of echoes. • Prospective single-center validation demonstrated that echo reduction leads to a significant shortening of the scan time, compared to standard 6-echo acquisition. • Qualitative and quantitative performance of the proposed method showed no significant differences in PDFF estimation with respect to the reference technique.

Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish.

Resources for Natural Language Processing (NLP) are less numerous for languages different from English. In the clinical domain, where these resources are vital for obtaining new knowledge about human health and diseases, creating new resources for the Spanish language is imperative. One of the most common approaches in NLP is word embeddings, which are dense vector representations of a word, considering the word's context. This vector representation is usually the first step in various NLP tasks, such as text classification or information extraction. Therefore, in order to enrich Spanish language NLP tools, we built a Spanish clinical corpus from waiting list diagnostic suspicions, a biomedical corpus from medical journals, and term sequences sampled from the Unified Medical Language System (UMLS). These three corpora can be used to compute word embeddings models from scratch using Word2vec and fastText algorithms. Furthermore, to validate the quality of the calculated embeddings, we adapted several evaluation datasets in English, including some tests that have not been used in Spanish to the best of our knowledge. These translations were validated by two bilingual clinicians following an ad hoc validation standard for the translation. Even though contextualized word embeddings nowadays receive enormous attention, their calculation and deployment require specialized hardware and giant training corpora. Our static embeddings can be used in clinical applications with limited computational resources. The validation of the intrinsic test we present here can help groups working on static and contextualized word embeddings. We are releasing the training corpus and the embeddings within this publication.

Oncological Benefit versus Cardiovascular Risk in Breast Cancer Patients Treated with Modern Radiotherapy.

Radiotherapy (RT) is an essential part of breast cancer (BC) treatments. Unfortunately, heart exposure to radiation can also impair the long-term survival of patients. Our study aimed to quantify the oncological benefit and the cardiovascular (CV) risk associated with modern RT in a real-world cohort of BC patients. Our descriptive study enrolled BC patients who received adjuvant RT. Ten-year overall survival (OS) was estimated using Predict® version 2.1 (National Health Service, London, UK). The basal risk of CV events was estimated using the American Heart Association (ACC/AHA) CV score. Treatment volumes and mean cardiac doses were obtained from RT treatment plan records. The increased risk of CV events due to RT was estimated using a model proposed by Darby. The risk of acute myocardial infarction or stroke mortality was estimated using HeartScore® (European Society of Cardiology, Brussels, Belgium). A total of 256 BC patients were included in the study. The average age of patients was 57 years old (range: 25-91); 49.6% had left BC. The mean cardiac dose was 166 cGy (interquartile range (IQR) 94-273); the estimated hazard ratio (HR) for CV disease was HR 1.12 (confidence interval (CI) 1.04-1.24). The estimated baseline 10-year CV risk was 5.6% (0.2 to 51.2); CV risk increased by 0.9% (range 0.02-35.47%) after RT. The absolute risk of 10-year mortality from CV disease was 2.5% (0.1-9); RT was associated with an estimated 4.9% survival benefit (3.73-6.07) against BC death and a 0.23% (0.17-0.29) estimated increase in CV mortality. Modern RT decreased 10-year BC mortality by 4% but increased CV mortality by 0.2% in this cohort. Our findings encourage the implementation of personalized adjuvant RT treatments that balance risks and benefits to improve long-term BC patient survival.

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.

Hepatocellular carcinoma detection in liver cirrhosis: diagnostic performance of contrast-enhanced CT vs. MRI with extracellular contrast vs. gadoxetic acid.

OBJECTIVES: To evaluate the diagnostic performance of contrast-enhanced CT vs. MRI with extracellular contrast agents (EC-MRI) vs. MRI with gadoxetic acid (EOB-MRI) for HCC detection in patients with liver cirrhosis using liver explant as the reference. The additional value of hepatobiliary phase (HBP) post Gadoxetic acid was also assessed. METHODS: Two-hundred seventy-seven consecutive patients who underwent liver transplantation over a 9 year period and imaging within 90 days of were retrospectively included. Imaging consisted in CT (n = 100), EC-MRI (n = 77) and EOB-MRI (n = 100), the latter subdivided into dynamic EOB-MRI and full EOB-MRI (dynamic+HBP). Three radiologists retrospectively categorized lesions ≥ 1 cm using the LI-RADSv2017 algorithm. Dynamic EOB-MRI was re-evaluated with the addition of HBP. Results were correlated with explant pathology. RESULTS: Pathology demonstrated 265 HCCs (mean size 2.1 ± 1.4 cm) in 177 patients. Per-patient sensitivities were 86.3% for CT, 89.5% for EC-MRI, 92.8% for dynamic EOB-MRI and 95.2% for full EOB-MRI (pooled reader data), with a significant difference between CT and dynamic/full EOB-MRI (p = 0.032/0.002), and between EC-MRI and full EOB-MRI (p = 0.047). Per-lesion sensitivities for CT, EC-MRI, dynamic EOB-MRI and full EOB-MRI were 59.5%,78.5%,69.7% and 76.8%, respectively, with a significant difference between MRI groups and CT (p-range:0.001-0.04), and no difference between EC-MRI and dynamic EOB-MRI (p = 0.949). For HCCs 1-1.9 cm, sensitivities were 34.4%, 64.6%, 57.3% and 67.3%, respectively, with all MRI groups significantly superior to CT (p ≤ 0.01) and full EOB-MRI superior to dynamic EOB-MRI (p = 0.002). CONCLUSIONS: EOB-MRI outperforms CT and EC-MRI for per-patient HCC detection sensitivity, and is equivalent to EC-MRI for per-lesion sensitivity. MRI methods outperform CT for detection of HCCs 1-1.9 cm. KEY POINTS: • MRI is superior to CT for HCC detection in patients with liver cirrhosis. • EOB-MRI outperforms CT and MRI using extracellular contrast agents (EC-MRI) for per-patient HCC detection sensitivity, and is equivalent to EC-MRI for per-lesion sensitivity. • The addition of hepatobiliary phase images improves HCC detection when using gadoxetic acid.

Prediction of hepatocellular carcinoma response to 90Yttrium radioembolization using volumetric ADC histogram quantification: preliminary results.

PURPOSE: To assess the predictive value of volumetric apparent diffusion coefficient (vADC) histogram quantification obtained before and 6 weeks (6w) post-treatment for assessment of hepatocellular carcinoma (HCC) response to 90Yttrium radioembolization (RE). METHODS: In this retrospective study, 22 patients (M/F 15/7, mean age 65y) who underwent lobar RE were included between October 2013 and November 2014. All patients underwent routine liver MRI pre-treatment and 6w after RE. Two readers assessed index tumor response at 6 months after RE in consensus, using mRECIST criteria. vADC histogram parameters of index tumors at baseline and 6w, and changes in vADC (ΔvADC) histogram parameters were calculated. The predictive value of ADC metrics was assessed by logistic regression with stepwise parameter selection and ROC analyses. RESULTS: Twenty two HCC lesions (mean size 3.9 ± 2.9 cm, range 1.2-12.3 cm) were assessed. Response at 6 months was as follows: complete response (CR, n = 6), partial response (PR, n = 3), stable disease (SD, n = 12) and progression (PD, n = 1). vADC median/mode at 6w (1.81-1.82 vs. 1.29-1.35 × 10- 3 mm2/s) and ΔvADC median/max (27-44% vs. 0-10%) were significantly higher in CR/PR vs. SD/PD (p = 0.011-0.036), while there was no significant difference at baseline. Logistic regression identified vADC median at 6w as an independent predictor of response (CR/PR) with odds ratio (OR) of 3.304 (95% CI: 1.099-9.928, p = 0.033) and AUC of 0.77. ΔvADC mean was identified as an independent predictor of CR with OR of 4.153 (95%CI: 1.229-14.031, p = 0.022) and AUC of 0.91. CONCLUSION: Diffusion histogram parameters obtained at 6w and early changes in ADC from baseline are predictive of subsequent response of HCCs treated with RE, while pre-treatment vADC histogram parameters are not. These results need confirmation in a larger study. TRIAL REGISTRATION: This retrospective study was IRB-approved and the requirement for informed consent was waived.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.

Multiparametric FDG-PET/MRI of Hepatocellular Carcinoma: Initial Experience.

Purpose: To compare multiparametric (mp)FDG-PET/MRI metrics between hepatocellular carcinoma (HCC) and liver parenchyma and to assess the correlation between mpMRI and FDG-PET standard uptake values (SUVs) in liver parenchyma and HCC. Methods: This prospective, institutional review board-approved study enrolled 15 patients (M/F 12/3; mean age 61 y) with HCC. mpMRI including blood-oxygen-level-dependent (BOLD) MRI, intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), and dynamic contrast-enhanced-(DCE-) MRI was performed simultaneously with 18F-FDG-PET on a 3T PET/MRI hybrid system. Quantitative BOLD, IVIM and DCE-MRI parameters (Tofts model (TM) and shutter-speed model (SSM)), and PET parameters (SUVmean and SUVmax) were quantified and compared between HCC lesions and liver parenchyma using Wilcoxon signed-rank tests. SUV ratios between HCCs and liver were also calculated (SUVmean T/L and SUVmax T/L). Diagnostic performance of (combined) mp-PET/MRI parameters for characterization of HCC was assessed using ROC analysis. Spearman correlations between PET and mpMRI parameters in HCC tumors and liver parenchyma were evaluated. Results: 21 HCC lesions (mean size 4.0 ± 2.4 cm; range 2-13 cm) were analyzed. HCCs exhibited significantly higher arterial fraction (from DCE-MRI) and lower R 2 ∗ pre-O2 and post-O2 (from BOLD-MRI) versus liver parenchyma (P < 0.032). The highest diagnostic performance for differentiation between HCC and liver parenchyma was achieved for combined ART SSM and R 2 ∗ post-O2 (AUC = 0.91). SUVmax showed reasonable performance for differentiation of HCC versus liver (AUC = 0.75). In HCC, DCE-MRI parameters K trans (TM and SSM) and v e TM exhibited significant negative correlations with SUVmax T/L (r ranges from -0.624 to -0.566; FDR-adjusted P < 0.050). Conclusions: Despite the observed reasonable diagnostic performance of FDG-PET SUVmax for HCC detection and several significant correlations between FDG-PET SUV and DCE-MRI parameters, FDG-PET did not provide clear additional value for HCC characterization compared to mpMRI in this pilot study.

Noninvasive prediction of portal pressure with MR elastography and DCE-MRI of the liver and spleen: Preliminary results.

BACKGROUND: Portal hypertension (PH), defined by hepatic venous pressure gradient (HVPG) ≥5 mmHg and clinically significant PH, defined by HVPG ≥10 mmHg, are complications of chronic liver disease. PURPOSE: To assess the diagnostic performance of MR elastography (MRE) and dynamic contrast-enhanced MRI (DCE-MRI) of the liver and spleen for the prediction of PH and clinically significant PH, in comparison with a qualitative PH imaging scoring system. STUDY TYPE: IRB-approved prospective study. POPULATION: In all, 34 patients with chronic liver disease who underwent HVPG measurement. FIELD STRENGTH/SEQUENCE: 1.5/3T examination including 2D-GRE MRE (n = 33) and DCE-MRI of the liver/spleen (n = 28). ASSESSMENT: Liver and spleen stiffness were calculated from elastogram maps. DCE-MRI was analyzed using model-free parameters and pharmacokinetic modeling. Two observers calculated qualitative PH imaging scores based on routine images. STATISTICAL TESTS: Imaging parameters were correlated with HVPG. Receiver operating characteristic (ROC) analysis was performed for prediction of PH and clinically significant PH. RESULTS: There were significant correlations between DCE-MRI parameters (liver time-to-peak, r = 0.517 / P = 0.006, liver distribution volume, r = 0.494 / P = 0.009, liver upslope, r = -0.567 / P = 0.002), liver stiffness (r = 0.478 / P = 0.016), PH imaging score (r = 0.441 / P = 0.009), and HVPG. ROC analysis provided significant area under the ROC (AUROCs) for PH (liver upslope 0.765, liver stiffness 0.809, spleen volume/diameter 0.746-0.731, PH imaging score 0.756) and for clinically significant PH (liver and spleen perfusion parameters 0.733-0.776, liver stiffness 0.742, PH imaging score 0.742). The ratio of liver stiffness to liver upslope had the highest AUROC for diagnosing PH (0.903) and clinically significant PH (0.785). DATA CONCLUSION: These preliminary results suggest that the combination of liver stiffness and perfusion metrics provide excellent accuracy for diagnosing PH, and fair accuracy for clinically significant PH. Combined MRE and DCE-MRI outperformed qualitative imaging scores for prediction of PH. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1091-1103.

Magnetic Resonance Imaging Predicts Histopathological Composition of Ileal Crohn's Disease.

BACKGROUND AND AIMS: Recently, smooth muscle hypertrophy has been suggested to be a contributor to small bowel lesions secondary to Crohn's disease [CD], in addition to inflammation and fibrosis. Here, we assess the value of magnetic resonance imaging [MRI] for the characterisation of histopathological tissue composition of small bowel CD, including inflammation, fibrosis, and smooth muscle hypertrophy. METHODS: A total of 35 consecutive patients [male/female 17/18, mean age 33 years] with ileal CD, who underwent small bowel resection and a preoperative contrast-enhanced MRI examination within 1 month before surgery, were retrospectively included. Image assessment included qualitative [pattern/degree of enhancement, presence of ulcerations/fistulas/abscesses] and quantitative parameters [wall thickness on T2/T1-weighted images [WI], enhancement ratios, apparent diffusion coefficient [ADC], Clermont and Magnetic Resonance Index of Activity [MaRIA] scores). MRI parameters were compared with histopathological findings including active inflammation, collagen deposition, and muscle hypertrophy using chi square/Fisher or Mann-Whitney tests and univariate/multivariate logistic/linear regression analyses. RESULTS: Forty ileal segments were analysed in 35 patients. Layered pattern at early-post-contrast phase was more prevalent (odds ratio [OR] = 8; p = 0.008), ADC was significantly lower [OR = 0.005; p = 0.022], and MaRIA score was significantly higher [OR = 1.125; p = 0.022] in inflammation grades 2-3 compared with grade 1. Wall thickness on T2WI was significantly increased [OR = 1.688; p = 0.043], and fistulas [OR = 14.5; p = 0.017] were more prevalent in segments with disproportionately increased muscle hypertrophy versus those with disproportionately increased fibrosis. MaRIA/Clermont scores, wall thickness on T1WI and T2WI, and ADC were all significantly correlated with degree of muscular hypertrophy. CONCLUSIONS: MRI predicts the degree of inflammation, and can distinguish prominent muscle hypertrophy from prominent fibrosis in ileal CD with reasonable accuracy (area under receiver operating characteristic curve [AUROC] > 0.7).

Detection of liver fibrosis using qualitative and quantitative MR elastography compared to liver surface nodularity measurement, gadoxetic acid uptake, and serum markers.

BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) combining different techniques such as MR elastography (MRE) has emerged as a noninvasive approach to diagnose and stage liver fibrosis with high accuracy allowing for anatomical and functional information. PURPOSE: To assess the diagnostic performance of mpMRI including qualitative and quantitative assessment of MRE, liver surface nodularity (LSN) measurement, hepatic enhancement ratios postgadoxetic acid, and serum markers (APRI, FIB-4) for the detection of liver fibrosis. STUDY TYPE: IRB-approved retrospective. SUBJECTS: Eighty-three adult patients. FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T MR systems. MRE and T1 -weighted postgadoxetic acid sequences. ASSESSMENT: Two independent observers analyzed qualitative color-coded MRE maps on a scale of 0-3. Regions of interest were drawn to measure liver stiffness on MRE stiffness maps and on pre- and postcontrast T1 -weighted images to measure hepatic enhancement ratios. Software was used to generate LSN measurements. Histopathology was used as the reference standard for diagnosis of liver fibrosis in all patients. STATISTICAL TESTS: A multivariable logistic analysis was performed to identify independent predictors of liver fibrosis. Receiver operating characteristic (ROC) analysis evaluated the performance of each imaging technique for detection of fibrosis, in comparison with serum markers. RESULTS: Liver stiffness measured with MRE provided the strongest correlation with histopathologic fibrosis stage (r = 0.74, P < 0.001), and the highest diagnostic performance for detection of stages F2-F4, F3-F4, and F4 (areas under the curve [AUCs] of 0.87, 0.91, and 0.89, respectively, P < 0.001) compared to other methods. Qualitative assessment of MRE maps showed fair to good accuracy for detection of fibrosis (AUC range 0.76-0.84). Multivariable logistic analysis identified liver stiffness and FIB-4 as independent predictors of fibrosis with AUCs of 0.90 (F2-F4), 0.93 (F3-F4) and 0.92 (F4) when combined. DATA CONCLUSION: Liver stiffness measured with MRE showed the best performance for detection of liver fibrosis compared to LSN and gadoxetic acid uptake, with slight improvement when combined with FIB-4. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1552-1561.

Prediction of the histopathologic findings of intrahepatic cholangiocarcinoma: qualitative and quantitative assessment of diffusion-weighted imaging.

OBJECTIVE: To correlate qualitative and quantitative diffusion weighted imaging (DWI) characteristics of intrahepatic cholangiocarcinoma (ICC) with histopathologic tumour grade and fibrosis content. METHODS: Fifty-one patients (21M/30F; mean age 61y) with ICC and MRI including DWI were included in this IRB-approved multicentre retrospective study. Qualitative tumour features were assessed. Tumour apparent diffusion coefficient (ADC) mean, minimum, and normalized (nADCliver) values were computed. Tumour grade [well(G1), moderately(G2), or poorly differentiated(G3)] and tumour fibrosis content [minimal(1), moderate(2), or abundant(3)] were categorized pathologically. Imaging findings and ADC values were compared with pathologic measures. Utility of ADC values for predicting tumour grade was assessed using ROC analysis. RESULTS: 51 ICCs (mean size 6.5±1.1 cm) were assessed. 33/51(64%) of ICCs demonstrated diffuse hyperintensity and 15/51(29%) demonstrated target appearance on DWI. Infiltrative morphology (p=0.02) and tumour size (p=0.04) were associated with G3. ADCmean and nADCmean of G3 (1.32±0.47x10-3 mm2/sec and 0.97±0.95) were lower than G1+G2 (1.57±0.39x10-3 mm2/sec and 1.24±0.49; p=0.03 and p=0.04). ADCmean and nADCmean were inversely correlated with tumour grade (p<0.025). No correlation was found between ADC and tumour fibrosis content. AUROC, sensitivity and specificity of nADCmean for G3 versus G1+G2 were 0.71, 89.5% and 55.5%. CONCLUSION: ADC quantification has reasonable accuracy for predicting ICC grade. KEY POINTS: • ADC quantification was useful for predicting ICC tumour grade. • Infiltrative tumour morphology and size were associated with poorly differentiated ICCs. • ADC values depended more on ICC tumour grade than fibrosis content. • Ability to predict ICC tumour grade non-invasively could impact patient management.

Evaluation of HCC response to locoregional therapy: Validation of MRI-based response criteria versus explant pathology.

BACKGROUND AND AIMS: This study evaluates the performance of various magnetic resonance imaging (MRI) response criteria for the prediction of complete pathologic necrosis (CPN) of hepatocellular carcinoma (HCC) post locoregional therapy (LRT) using explant pathology as a reference. METHODS: We included 61 patients (male/female 46/15; mean age 60years) who underwent liver transplantation after LRT with transarterial chemoembolization plus radiofrequency or microwave ablation (n=56), or 90Yttrium radioembolization (n=5). MRI was performed <90days before liver transplantation. Three independent readers assessed the following criteria: RECIST, EASL, modified RECIST (mRECIST), percentage of necrosis on subtraction images, and diffusion-weighted imaging (DWI), both qualitative (signal intensity) and quantitative (apparent diffusion coefficient [ADC]). The degree of necrosis was retrospectively assessed at histopathology. Intraclass correlation coefficient (ICC) and Cohen's kappa were used to assess inter-reader agreement. Logistic regression and receiver operating characteristic analyses were used to determine imaging predictors of CPN. Pearson correlation was performed between imaging criteria and pathologic degree of tumor necrosis. RESULTS: A total of 97HCCs (mean size 2.3±1.3cm) including 28 with CPN were evaluated. There was excellent inter-reader agreement (ICC 0.77-0.86, all methods). EASL, mRECIST, percentage of necrosis and qualitative DWI were all significant (p<0.001) predictors of CPN, while RECIST and ADC were not. EASL, mRECIST and percentage of necrosis performed similarly (area under the curves [AUCs] 0.810-0.815) while the performance of qualitative DWI was lower (AUC 0.622). Image subtraction demonstrated the strongest correlation (r=0.71-0.72, p<0.0001) with pathologic degree of tumor necrosis. CONCLUSIONS: EASL/mRECIST criteria and image subtraction have excellent diagnostic performance for predicting CPN in HCC treated with LRT, with image subtraction correlating best with pathologic degree of tumor necrosis. Thus, MR image subtraction is recommended for assessing HCC response to LRT. LAY SUMMARY: The assessment of hepatocellular carcinoma (HCC) tumor necrosis after locoregional therapy is essential for additional treatment planning and estimation of outcome. In this study, we assessed the performance of various magnetic resonance imaging (MRI) response criteria (RECIST, mRECIST, EASL, percentage of necrosis on subtraction images, and diffusion-weighted imaging) for the prediction of complete pathologic necrosis of HCC post locoregional therapy on liver explant. Patients who underwent liver transplantation after locoregional therapy were included in this retrospective study. All patients underwent routine liver MRI within 90days of liver transplantation. EASL/mRECIST criteria and image subtraction had excellent diagnostic performance for predicting complete pathologic necrosis in treated HCC, with image subtraction correlating best with pathologic degree of tumor necrosis.

Feasibility and reproducibility of liver surface nodularity quantification for the assessment of liver cirrhosis using CT and MRI.

PURPOSE: To assess intra-observer, inter-observer and inter-modality (CT vs. MRI) reproducibility of liver surface nodularity (LSN) scores measured with software used for detection of liver fibrosis. METHODS: This IRB-approved retrospective study included patients with both abdominal CT and MRI within 6 months of histopathologic sampling. Two independent observers used post-processing software to quantify LSN scores on axial non-contrast CT (NCT), axial contrast-enhanced CT (CECT), axial T2-weighted (T2W) HASTE, and axial and coronal post-gadoxetic acid T1-weighted (T1W) images obtained during the hepatobiliary phase (HBP). Ten slices were used to acquire the LSN scores. Intra-observer, inter-observer, and inter-modality (CT vs. MRI) reproducibility were assessed with intraclass correlation coefficient (ICC) and coefficients of variability (CV). Accuracy for detection of cirrhosis was evaluated for each technique. RESULTS: 26 patients (M/F 19/7, mean age 57 years), including 7 with cirrhosis (26.9%), were assessed. Technical failure occurred with NCT (1/23, 4.3%) and T2 HASTE (8/28, 28.6%). Intra-observer reproducibility was excellent for NCT, CECT, axial and coronal T1W HBP [ICC ≥ 0.92, CV ≤ 8%]. Inter-observer reproducibility was also excellent for NCT and CECT (ICC ≥ 0.95, CV ≤ 7.3%) and for coronal T1W HBP (ICC = 0.84, CV = 5.6%). There was fair to moderate agreement between CT and MRI (ICC 0.20-0.44). There were significant differences in mean LSN scores between non-cirrhotic and cirrhotic patients with NCT (2.6 vs. 4.2, p = 0.04) and T1W HBP (3.7 vs. 4.6; p = 0.01) images, with AUCs of 0.81 and 0.82, respectively. CONCLUSIONS: LSN measurement is highly reproducible with NCT and post-contrast T1W HBP on MRI, with different results obtained between CT and MRI.