Diagnosis and Monitoring of Nonalcoholic Steatohepatitis: Current State and Future Directions.

Nonalcoholic fatty liver disease (NAFLD) is a common liver disease, with a worldwide prevalence of 25%. NAFLD is a spectrum that includes nonalcoholic fatty liver defined histologically by isolated hepatocytes steatosis without inflammation and nonalcoholic steatohepatitis (NASH) is the inflammatory subtype of NAFLD and is associated with disease progression, development of cirrhosis, and increased rates of liver-specific and overall mortality. The differentiation between NAFLD and NASH as well as staging NASH are important yet challenging clinical problems. Liver biopsy is currently the standard for disease diagnosis and fibrosis staging. However, this procedure is invasive, costly, and cannot be used for longitudinal monitoring. Therefore, several noninvasive quantitative imaging biomarkers have been proposed that can estimate the severity of hepatic steatosis and fibrosis. Despite this, noninvasive diagnosis of NASH and accurate risk stratification remain unmet needs. In this work, the most relevant available imaging biomarkers are reviewed and their application in patients with NAFLD are discussed.

LI-RADS CT and MRI Ancillary Feature Association with Hepatocellular Carcinoma and Malignancy: An Individual Participant Data Meta-Analysis.

Background The independent contribution of each Liver Imaging Reporting and Data System (LI-RADS) CT or MRI ancillary feature (AF) has not been established. Purpose To evaluate the association of LI-RADS AFs with hepatocellular carcinoma (HCC) and malignancy while adjusting for LI-RADS major features through an individual participant data (IPD) meta-analysis. Materials and Methods Medline, Embase, Cochrane Central Register of Controlled Trials, and Scopus were searched from January 2014 to January 2022 for studies evaluating the diagnostic accuracy of CT and MRI for HCC using LI-RADS version 2014, 2017, or 2018. Using a one-step approach, IPD across studies were pooled. Adjusted odds ratios (ORs) and 95% CIs were derived from multivariable logistic regression models of each AF combined with major features except threshold growth (excluded because of infrequent reporting). Liver observation clustering was addressed at the study and participant levels through random intercepts. Risk of bias was assessed using a composite reference standard and Quality Assessment of Diagnostic Accuracy Studies 2. Results Twenty studies comprising 3091 observations (2456 adult participants; mean age, 59 years ± 11 [SD]; 1849 [75.3%] men) were included. In total, 89% (eight of nine) of AFs favoring malignancy were associated with malignancy and/or HCC, 80% (four of five) of AFs favoring HCC were associated with HCC, and 57% (four of seven) of AFs favoring benignity were negatively associated with HCC and/or malignancy. Nonenhancing capsule (OR = 3.50 [95% CI: 1.53, 8.01]) had the strongest association with HCC. Diffusion restriction (OR = 14.45 [95% CI: 9.82, 21.27]) and mild-moderate T2 hyperintensity (OR = 10.18 [95% CI: 7.17, 14.44]) had the strongest association with malignancy. The strongest negative associations with HCC were parallels blood pool enhancement (OR = 0.07 [95% CI: 0.01, 0.49]) and marked T2 hyperintensity (OR = 0.18 [95% CI: 0.07, 0.45]). Seventeen studies (85%) had a high risk of bias. Conclusion Most LI-RADS AFs were independently associated with HCC, malignancy, or benignity as intended when adjusting for major features. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Crivellaro in this issue.

Abbreviated MRI Protocols in the Abdomen and Pelvis.

Abbreviated MRI (AMRI) protocols rely on the acquisition of a limited number of sequences tailored to a specific question. The main objective of AMRI protocols is to reduce exam duration and costs, while maintaining an acceptable diagnostic performance. AMRI is of increasing interest in the radiology community; however, challenges limiting clinical adoption remain. In this review, we will address main abdominal and pelvic applications of AMRI in the liver, pancreas, kidney, and prostate, including diagnostic performance, pitfalls, limitations, and cost effectiveness will also be discussed. Level of Evidence: 3 Technical Efficacy Stage: 3.

Are dilution, slow injection and care bolus technique the causal solution to mitigating arterial-phase artifacts on gadoxetic acid-enhanced MRI? A large-cohort study.

OBJECTIVE: Arterial-phase artifacts are gadoxetic acid (GA)-enhanced MRI's major drawback, ranging from 5 to 39%. We evaluate the effect of dilution and slow injection of GA using automated fluoroscopic triggering on liver MRI arterial-phase (AP) acquisition timing, artifact frequency, and lesion visibility. METHODS AND MATERIALS: Saline-diluted 1:1 GA was injected at 1 ml/s into 1413 patients for 3 T liver MRI. Initially, one senior abdominal radiologist, i.e., principal investigator (PI), assessed all MR exams and compared them to previous and follow-up images, as well as the radiology report on record, determining the standard of reference for lesion detection and characterization. Then, three other readers independently evaluated the AP images for artifact type (truncation (TA), transient severe motion (TSM) or mixed), artifact severity (on a 5-point scale), acquisition timing (on a 4-point scale) and visibility (on a 5-point scale) of hypervascular lesions ≥ 5 mm, selected by the PI. Artifact score ≥ 4 and artifact score ≤ 3 were considered significant and non-significant artifacts, respectively. RESULTS: Of the 1413 exams, diagnostic-quality arterial-phase images included 1100 (77.8%) without artifacts, 220 (15.6%) with minimal, and 77 (5.4%) with moderate artifacts. Only 16 exams (1.1%) had significant artifacts, 13 (0.9%) with severe artifacts (score 4), and three (0.2%) non-diagnostic artifacts (score 5). AP acquisition timing was optimal in 1369 (96.8%) exams. Of the 449 AP hypervascular lesions, 432 (96.2%) were detected. CONCLUSION: Combined dilution and slow injection of GA with MR results in well-timed arterial-phase images in 96.8% and a reduction of exams with significant artifacts to 1.1%. CLINICAL RELEVANCE STATEMENT: Hypervascular lesions, in particular HCC detection, hinge on arterial-phase hyperenhancement, making well-timed, artifact-free arterial-phase images a prerequisite for accurate diagnosis. Saline dilution 1:1, slow injection (1 ml/s), and automated bolus triggering reduce artifacts and optimize acquisition timing. KEY POINTS: • There was substantial agreement among the three readers regarding the presence and type of arterial-phase (AP) artifacts, acquisition timing, and lesion visibility. • Impaired AP hypervascular lesion visibility occurred in 17 (3.8%) cases; in eight lesions due to mistiming and in nine lesions due to significant artifacts. • When AP timing was suboptimal, it was too late in 40 exams (3%) and too early in 4 exams (0.2%) of exams.

Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.

OBJECTIVES: This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to predict postoperative early recurrence (ER) of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: This was a single-center retrospective study enrolling patients who underwent resection for BCLC A and B HCC and preoperative contrast-enhanced MRI. Quantitative total tumor volume (cm3) and total tumor burden (TTB, %) were obtained using a DL automated segmentation tool. Radiologists' visual assessment was used to ensure the quality control of automated segmentation. The prognostic value of clinicopathological variables and tumor burden-related parameters for ER was determined by Cox regression analyses. RESULTS: A total of 592 patients were included, with 525 and 67 patients assigned to BCLC A and B, respectively (2-year ER rate: 30.0% vs. 45.3%; hazard ratio (HR) = 1.8; p = 0.007). TTB was the most important predictor of ER (HR = 2.2; p < 0.001). Using 6.84% as the threshold of TTB, two ER risk strata were obtained in overall (p < 0.001), BCLC A (p < 0.001), and BCLC B (p = 0.027) patients, respectively. The BCLC B low-TTB patients had a similar risk for ER to BCLC A patients and thus were reassigned to a BCLC An stage; whilst the BCLC B high-TTB patients remained in a BCLC Bn stage. The 2-year ER rate was 30.5% for BCLC An patients vs. 58.1% for BCLC Bn patients (HR = 2.8; p < 0.001). CONCLUSIONS: TTB determined by DL-based automated segmentation at MRI was a predictive biomarker for postoperative ER and facilitated refined subcategorization of patients within BCLC stages A and B. CLINICAL RELEVANCE STATEMENT: Total tumor burden derived by deep learning-based automated segmentation at MRI may serve as an imaging biomarker for predicting early recurrence, thereby improving subclassification of Barcelona Clinic Liver Cancer A and B hepatocellular carcinoma patients after hepatectomy. KEY POINTS: Total tumor burden (TTB) is important for Barcelona Clinic Liver Cancer (BCLC) staging, but is heterogenous. TTB derived by deep learning-based automated segmentation was predictive of postoperative early recurrence. Incorporating TTB into the BCLC algorithm resulted in successful subcategorization of BCLC A and B patients.

Individual Participant Data Meta-Analysis of LR-5 in LI-RADS Version 2018 versus Revised LI-RADS for Hepatocellular Carcinoma Diagnosis.

Background A simplification of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 (v2018), revised LI-RADS (rLI-RADS), has been proposed for imaging-based diagnosis of hepatocellular carcinoma (HCC). Single-site data suggest that rLI-RADS category 5 (rLR-5) improves sensitivity while maintaining positive predictive value (PPV) of the LI-RADS v2018 category 5 (LR-5), which indicates definite HCC. Purpose To compare the diagnostic performance of LI-RADS v2018 and rLI-RADS in a multicenter data set of patients at risk for HCC by performing an individual patient data meta-analysis. Materials and Methods Multiple databases were searched for studies published from January 2014 to January 2022 that evaluated the diagnostic performance of any version of LI-RADS at CT or MRI for diagnosing HCC. An individual patient data meta-analysis method was applied to observations from the identified studies. Quality Assessment of Diagnostic Accuracy Studies version 2 was applied to determine study risk of bias. Observations were categorized according to major features and either LI-RADS v2018 or rLI-RADS assignments. Diagnostic accuracies of category 5 for each system were calculated using generalized linear mixed models and compared using the likelihood ratio test for sensitivity and the Wald test for PPV. Results Twenty-four studies, including 3840 patients and 4727 observations, were analyzed. The median observation size was 19 mm (IQR, 11-30 mm). rLR-5 showed higher sensitivity compared with LR-5 (70.6% [95% CI: 60.7, 78.9] vs 61.3% [95% CI: 45.9, 74.7]; P < .001), with similar PPV (90.7% vs 92.3%; P = .55). In studies with low risk of bias (n = 4; 1031 observations), rLR-5 also achieved a higher sensitivity than LR-5 (72.3% [95% CI: 63.9, 80.1] vs 66.9% [95% CI: 58.2, 74.5]; P = .02), with similar PPV (83.1% vs 88.7%; P = .47). Conclusion rLR-5 achieved a higher sensitivity for identifying HCC than LR-5 while maintaining a comparable PPV at 90% or more, matching the results presented in the original rLI-RADS study. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sirlin and Chernyak in this issue.

Automated selection of abdominal MRI series using a DICOM metadata classifier and selective use of a pixel-based classifier.

Accurate, automated MRI series identification is important for many applications, including display ("hanging") protocols, machine learning, and radiomics. The use of the series description or a pixel-based classifier each has limitations. We demonstrate a combined approach utilizing a DICOM metadata-based classifier and selective use of a pixel-based classifier to identify abdominal MRI series. The metadata classifier was assessed alone as Group metadata and combined with selective use of the pixel-based classifier for predictions with less than 70% certainty (Group combined). The overall accuracy (mean and 95% confidence intervals) for Groups metadata and combined on the test dataset were 0.870 CI (0.824,0.912) and 0.930 CI (0.893,0.963), respectively. With this combined metadata and pixel-based approach, we demonstrate accurate classification of 95% or greater for all pre-contrast MRI series and improved performance for some post-contrast series.

A Method for Efficient De-identification of DICOM Metadata and Burned-in Pixel Text.

De-identification of DICOM images is an essential component of medical image research. While many established methods exist for the safe removal of protected health information (PHI) in DICOM metadata, approaches for the removal of PHI "burned-in" to image pixel data are typically manual, and automated high-throughput approaches are not well validated. Emerging optical character recognition (OCR) models can potentially detect and remove PHI-bearing text from medical images but are very time-consuming to run on the high volume of images found in typical research studies. We present a data processing method that performs metadata de-identification for all images combined with a targeted approach to only apply OCR to images with a high likelihood of burned-in text. The method was validated on a dataset of 415,182 images across ten modalities representative of the de-identification requests submitted at our institution over a 20-year span. Of the 12,578 images in this dataset with burned-in text of any kind, only 10 passed undetected with the method. OCR was only required for 6050 images (1.5% of the dataset).

Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study.

PURPOSE: To assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and lesion detection in patients at risk for hepatocellular carcinoma (HCC). METHODS: This single-center prospective study enrolled consecutive at-risk participants who underwent 3.0 T gadoxetate disodium-enhanced MRI. Conventional DWI was acquired using parallel imaging (PI) with SENSE (PI-DWI). In CS-DWI and DL-CS-DWI, CS but not PI with SENSE was used to accelerate the scan with 2.5 as the acceleration factor. Qualitative and quantitative image quality were independently assessed by two masked reviewers, and were compared using the Wilcoxon signed-rank test. The detection rates of clinically-relevant (LR-4/5/M based on the Liver Imaging Reporting and Data System v2018) liver lesions for each DWI sequence were independently evaluated by another two masked reviewers against their consensus assessments based on all available non-DWI sequences, and were compared by the McNemar test. RESULTS: 67 participants (median age, 58.0 years; 56 males) with 197 clinically-relevant liver lesions were enrolled. Among the three DWI sequences, DL-CS-DWI showed the best qualitative and quantitative image qualities (p range, <0.001-0.039). For clinically-relevant liver lesions, the detection rates (91.4%-93.4%) of DL-CS-DWI showed no difference with CS-DWI (87.3%-89.8%, p = 0.230-0.231) but were superior to PI-DWI (82.7%-85.8%, p = 0.015-0.025). For lesions located in the hepatic dome, DL-CS-DWI demonstrated the highest detection rates (94.8%-97.4% vs 76.9%-79.5% vs 64.1%-69.2%, p = 0.002-0.045) among the three DWI sequences. CONCLUSION: In patients at high-risk for HCC, DL-CS-DWI improved image quality and detection for clinically-relevant liver lesions, especially for the hepatic dome.

Design of the phase 3 MAESTRO clinical program to evaluate resmetirom for the treatment of nonalcoholic steatohepatitis.

BACKGROUND: Non-alcoholic steatohepatitis (NASH) is a progressive form of non-alcoholic fatty liver disease (NAFLD) associated with steatosis, hepatocellular injury, inflammation and fibrosis. In a Phase 2 trial in adults with NASH (NCT02912260), resmetirom, an orally administered, liver-targeted thyroid hormone receptor-ß selective agonist, significantly reduced hepatic fat (via imaging) and resolved NASH without worsening fibrosis (via liver biopsy) in a significant number of patients compared with placebo. AIMS: To present the design of the Phase 3 MAESTRO clinical programme evaluating resmetirom for treatment of NASH (MAESTRO-NAFLD-1 [NCT04197479], MAESTRO-NAFLD-OLE [NCT04951219], MAESTRO-NASH [NCT03900429], MAESTRO-NASH-OUTCOMES [NCT05500222]). METHODS: MAESTRO-NASH is a pivotal serial biopsy trial in up to 2000 adults with biopsy-confirmed at-risk NASH. Patients are randomised to a once-daily oral placebo, 80 mg resmetirom, or 100 mg resmetirom. Liver biopsies are conducted at screening, week 52 and month 54. MAESTRO-NAFLD-1 is a 52-week safety trial in ~1400 adults with NAFLD/presumed NASH (based on non-invasive testing); ~700 patients from MAESTRO-NAFLD-1 are enrolled in MAESTRO-NAFLD-OLE, a 52-week active treatment extension to further evaluate safety. MAESTRO-NASH-OUTCOMES is enrolling 700 adults with well-compensated NASH cirrhosis to evaluate the potential for resmetirom to slow progression to hepatic decompensation events. Non-invasive tests (biomarkers, imaging) are assessed longitudinally throughout, in addition to validated patient-reported outcomes. CONCLUSION: The MAESTRO clinical programme was designed in conjunction with regulatory authorities to support approval of resmetirom for treatment of NASH. The surrogate endpoints, based on week 52 liver biopsy, serum biomarkers and imaging, are confirmed by long-term clinical liver-related outcomes in MAESTRO-NASH (month 54) and MAESTRO-NASH-OUTCOMES (time to event).