LI-RADS: Review and updates

Simplified liver imaging reporting and data system for the diagnosis of hepatocellular carcinoma on gadoxetic acid-enhanced magnetic resonance imaging

BACKGROUND

The liver imaging reporting and data system (LI-RADS) diagnostic table has 15 cells and is too complex. The diagnostic performance of LI-RADS for hepatocellular carcinoma (HCC) is not satisfactory on gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI).

AIM

To evaluate the ability of the simplified LI-RADS (sLI-RADS) to diagnose HCC on EOB-MRI.

METHODS

A total of 331 patients with 356 hepatic observations were retrospectively analysed. The diagnostic performance of sLI-RADS A-D using a single threshold was evaluated and compared with LI-RADS v2018 to determine the optimal sLI-RADS. The algorithms of sLI-RADS A-D are as follows: The single threshold for sLI-RADS A and B was 10 mm, that is, classified observations ≥ 10mm using an algorithm of 10-19 mm observations (sLI-RADS A) and ≥ 20 mm observations (sLI-RADS B) in the diagnosis table of LI-RADS v2018, respectively, while the classification algorithm remained unchanged for observations < 10 mm; the single threshold for sLI-RADS C and D was 20 mm, that is, for < 20 mm observations, the algorithms for < 10 mm observations (sLI-RADS C)and 10-19 mm observations (sLI-RADS D) were used, respectively, while the algorithm remained unchanged for observations ≥ 20 mm. With hepatobiliary phase (HBP) hypointensity as a major feature (MF), the final sLI-RADS (F-sLI-RADS) was formed according to the optimal sLI-RADS, and its diagnostic performance was evaluated. The times needed to classify the observations according to F-sLI-RADS and LI-RADS v2018 were compared.

RESULTS

The optimal sLI-RADS was sLI-RADS D (with a single threshold of 20 mm), because its sensitivity was greater than that of LI-RADS v2018 (89.8% vs 87.0%, P = 0.031), and its specificity was not lower (89.4% vs 90.1%, P > 0.999). With HBP hypointensity as an MF, the sensitivity of F-sLI-RADS was greater than that of LI-RADS v2018 (93.0% vs 87.0%, P < 0.001) and sLI-RADS D (93.0% vs 89.8%, P = 0.016), without a lower specificity (86.5% vs 90.1%, P = 0.062; 86.5% vs 89.4%, P = 0.125). Compared with that of LI-RADS v2018, the time to classify lesions according to F-sLI-RADS was shorter (51 ± 21 s vs 73 ± 24 s, P < 0.001).

CONCLUSION

The use of sLI-RADS with HBP hypointensity as an MF may improve the sensitivity of HCC diagnosis and reduce lesion classification time.

Simplified liver imaging reporting and data system for the diagnosis of hepatocellular carcinoma on gadoxetic acid-enhanced magnetic resonance imaging

BACKGROUND

The liver imaging reporting and data system (LI-RADS) diagnostic table has 15 cells and is too complex. The diagnostic performance of LI-RADS for hepatocellular carcinoma (HCC) is not satisfactory on gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI).

AIM

To evaluate the ability of the simplified LI-RADS (sLI-RADS) to diagnose HCC on EOB-MRI.

METHODS

A total of 331 patients with 356 hepatic observations were retrospectively analysed. The diagnostic performance of sLI-RADS A-D using a single threshold was evaluated and compared with LI-RADS v2018 to determine the optimal sLI-RADS. The algorithms of sLI-RADS A-D are as follows: The single threshold for sLI-RADS A and B was 10 mm, that is, classified observations ≥ 10mm using an algorithm of 10-19 mm observations (sLI-RADS A) and ≥ 20 mm observations (sLI-RADS B) in the diagnosis table of LI-RADS v2018, respectively, while the classification algorithm remained unchanged for observations < 10 mm; the single threshold for sLI-RADS C and D was 20 mm, that is, for < 20 mm observations, the algorithms for < 10 mm observations (sLI-RADS C)and 10-19 mm observations (sLI-RADS D) were used, respectively, while the algorithm remained unchanged for observations ≥ 20 mm. With hepatobiliary phase (HBP) hypointensity as a major feature (MF), the final sLI-RADS (F-sLI-RADS) was formed according to the optimal sLI-RADS, and its diagnostic performance was evaluated. The times needed to classify the observations according to F-sLI-RADS and LI-RADS v2018 were compared.

RESULTS

The optimal sLI-RADS was sLI-RADS D (with a single threshold of 20 mm), because its sensitivity was greater than that of LI-RADS v2018 (89.8% vs 87.0%, P = 0.031), and its specificity was not lower (89.4% vs 90.1%, P > 0.999). With HBP hypointensity as an MF, the sensitivity of F-sLI-RADS was greater than that of LI-RADS v2018 (93.0% vs 87.0%, P < 0.001) and sLI-RADS D (93.0% vs 89.8%, P = 0.016), without a lower specificity (86.5% vs 90.1%, P = 0.062; 86.5% vs 89.4%, P = 0.125). Compared with that of LI-RADS v2018, the time to classify lesions according to F-sLI-RADS was shorter (51 ± 21 s vs 73 ± 24 s, P < 0.001).

CONCLUSION

The use of sLI-RADS with HBP hypointensity as an MF may improve the sensitivity of HCC diagnosis and reduce lesion classification time.

Differentiating mass-forming intrahepatic cholangiocarcinoma from atypical hepatocellular carcinoma using Gd-EOB-DTPA-enhanced magnetic resonance imaging combined with serum markers in at-risk patients with hepatitis B virus

Background

The precise differentiation of intrahepatic cholangiocarcinoma (ICC) from atypical hepatocellular carcinoma (HCC) is vital for treatment strategy and prognostic prediction. In clinical practice, nearly 40% of HCCs demonstrate atypical manifestations, particularly HCCs with rim arterial phase hyperenhancement (APHE), which is challenging to differentiate from mass-forming ICC. Thus, we aimed to develop a diagnostic regimen of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) contrast-enhanced magnetic resonance imaging (MRI) combined with serum tumor markers in differentiating mass-forming ICC from atypical HCC in at-risk patients with the hepatitis B virus (HBV).

Methods

This study enrolled 129 patients with pathologically proven mass-forming ICCs (n=53) and atypical HCCs (n=76) who had undergone preoperative Gd-EOB-DTPA contrast-enhanced MRI. The clinical data and imaging findings were analyzed. Univariate and multivariate logistic analyses were performed to identify the independent predictors for differentiating mass-forming ICCs from atypical HCCs. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, and DeLong test was used to compare the areas under curves of all independent predictors.

Results

Univariate logistic regression analysis revealed normal alpha fetoprotein (AFP), elevated carbohydrate antigen 19-9 (CA19-9) level, elevated carcinoma embryonic antigen (CEA) level, central hyperintensity on T2-weighted imaging (T2WI), central hypointensity on T2WI, and targetoid sign on hepatobiliary phase (HBP) and targetoid restriction on diffusion-weighted imaging (DWI) were more likely to be significant predictors favoring mass-forming ICCs (all P values <0.05). In contrast, multifocal hyperintensity on T2WI and capsule sign were more frequently seen in patients with atypical HCC (all P values <0.05). Multivariate analysis revealed normal AFP, elevated CA19-9 level, targetoid sign on HBP, and targetoid restriction on DWI (all P=0.001) were independent predictors for differentiating mass-forming ICCs from atypical HCCs; DeLong test showed that the area under curve (AUC) increased to 0.949 when the above predictors were combined (all P values <0.05), and the sensitivity, specificity, and accuracy of the combined independent predictors were 88.7%, 93.4%, and 91.5%, respectively.

Conclusions

A diagnostic regimen integrating tumor markers (AFP, CA19-9) and imaging biomarkers (targetoid restriction on DWI and/or targetoid sign on HBP) using Gd-EOB-DTPA-enhanced MRI could help to differentiate mass-forming ICCs from atypical HCCs and achieve high diagnostic performance of mass-forming ICCs in at-risk patients with the HBV.

Keywords

Mass-forming intrahepatic cholangiocarcinoma (mass-forming ICC); atypical hepatocellular carcinoma (atypical HCC); magnetic resonance imaging (MRI); gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA); hepatobiliary phase (HBP).

Impact of body mass index on disease progression and outcomes in patients with nonalcoholic fatty liver disease

BACKGROUND

The relationship between body mass index (BMI) and outcomes in patients with nonalcoholic fatty liver disease (NAFLD) is not well defined. This study aimed to assess the presentations, outcomes, and development of liver-related events (LREs) and non-LREs in patients with NAFLD stratified by BMI.

METHODS

Records of NAFLD patients from 2000-2022 were reviewed. Patients were categorized as lean (18.5-22.9 kg/m2), overweight (23-24.9 kg/m2), and obese (>25 kg/m2) based on BMI. Stage of steatosis, fibrosis, and NAFLD activity score were noted in the patients undergoing liver biopsy in each group.

RESULTS

Out of 1051 NAFLD patients, 127 (12.1%) had normal BMI, 177 (16.8%) and 747 (71.1%) were overweight and obese, respectively. Median [interquartile range] BMI was 21.9 [20.6-22.5], 24.2 [23.7-24.6], and 28.3 [26.6-30.6] kg/m2 in each group, respectively. Prevalence of metabolic syndrome and dyslipidemia were significantly higher in the obese. Obese patients had significantly higher median [interquartile range] liver stiffness (6.4 [4.9-9.4] kPa) than overweight and lean subjects. A higher proportion of obese patients had significant and advanced liver fibrosis. At follow-up, there were no significant differences in the progression of liver disease, new LREs, coronary artery disease, or hypertension across the BMI groups. Overweight and obese patients were more likely to develop new-onset diabetes by follow-up. The mortality rates in the three groups were comparable (0.47, 0.68, and 0.49 per 100 person-years, respectively), with similar causes of death (liver-related vs non-liver-related).

CONCLUSIONS

Patients with lean NAFLD have similar disease severity and rates of progression as the obese. BMI is not a reliable determinant of outcomes in NAFLD patients.

KEY MESSAGES

Refining Liver Biopsy in Hepatocellular Carcinoma: An In-Depth Exploration of Shifting Diagnostic and Therapeutic Applications

The field of hepatocellular carcinoma (HCC) has faced significant change on multiple levels in the past few years. The increasing emphasis on the various HCC phenotypes and the emergence of novel, specific therapies have slowly paved the way for a personalized approach to primary liver cancer. In this light, the role of percutaneous liver biopsy of focal lesions has shifted from a purely confirmatory method to a technique capable of providing an in-depth characterization of any nodule. Cancer subtype, gene expression, the mutational profile, and tissue biomarkers might soon become widely available through biopsy. However, indications, expectations, and techniques might suffer changes as the aim of the biopsy evolves from providing minimal proof of the disease to high-quality specimens for extensive analysis. Consequently, a revamped position of tissue biopsy is expected in HCC, following the reign of non-invasive imaging-only diagnosis. Moreover, given the advances in techniques that have recently reached the spotlight, such as liquid biopsy, concomitant use of all the available methods might gather just enough data to improve therapy selection and, ultimately, outcomes. The current review aims to discuss the changing role of liver biopsy and provide an evidence-based rationale for its use in the era of precision medicine in HCC.

Quantum iterative reconstruction on a photon-counting detector CT improves the quality of hepatocellular carcinoma imaging

BACKGROUND

Excellent image quality is crucial for workup of hepatocellular carcinoma (HCC) in patients with liver cirrhosis because a signature tumor signal allows for non-invasive diagnosis without histologic proof. Photon-counting detector computed tomography (PCD-CT) can enhance abdominal image quality, especially in combination with a novel iterative reconstruction algorithm, quantum iterative reconstruction (QIR). The purpose of this study was to analyze the impact of different QIR levels on PCD-CT imaging of HCC in both phantom and patient scans.

METHODS

Virtual monoenergetic images at 50 keV were reconstructed using filtered back projection and all available QIR levels (QIR 1-4). Objective image quality properties were investigated in phantom experiments. The study also included 44 patients with triple-phase liver PCD-CT scans of viable HCC lesions. Quantitative image analysis involved assessing the noise, contrast, and contrast-to-noise ratio of the lesions. Qualitative image analysis was performed by three raters evaluating noise, artifacts, lesion conspicuity, and overall image quality using a 5-point Likert scale.

RESULTS

Noise power spectra in the phantom experiments showed increasing noise suppression with higher QIR levels without affecting the modulation transfer function. This pattern was confirmed in the in vivo scans, in which the lowest noise levels were found in QIR-4 reconstructions, with around a 50% reduction in median noise level compared with the filtered back projection images. As contrast does not change with QIR, QIR-4 also yielded the highest contrast-to-noise ratios. With increasing QIR levels, rater scores were significantly better for all qualitative image criteria (all p < .05).

CONCLUSIONS

Without compromising image sharpness, the best image quality of iodine contrast optimized low-keV virtual monoenergetic images can be achieved using the highest QIR level to suppress noise. Using these settings as standard reconstruction for HCC in PCD-CT imaging might improve diagnostic accuracy and confidence.

Evaluating treatment response following locoregional therapy for hepatocellular carcinoma: A review of the available serological and radiological tools for assessment

Hepatocellular carcinoma (HCC) is an aggressive primary malignancy of the liver and is the third most common cause of cancer-related global mortality. There has been a steady increase in treatment options for HCC in recent years, including innovations in both curative and non-curative therapies. These advances have brought new challenges and necessary improvements in strategies of disease monitoring, to allow early detection of HCC recurrence. Current serological and radiological strategies for post-treatment monitoring and prognostication and their limitations will be discussed and evaluated in this review.

Spontaneously Ruptured Hepatocellular Carcinoma: Computed Tomography-Based Assessment

Spontaneously ruptured hepatocellular carcinoma (SRHCC) is an uncommon and life-threatening complication in patients with hepatocellular carcinoma (HCC). It is usually associated with chronic liver disease and has a poor prognosis with a high mortality rate during the acute phase. SRHCC can cause a severe and urgent condition of acute abdomen disease and requires a correct diagnosis to achieve adequate treatment. Clinical presentation is related to the presence of hemoperitoneum, and abdominal pain is the most common symptom (66–100% of cases). Although the treatment approach is not unique, trans-arterial (chemo)embolization (TAE/TACE) followed by staged hepatectomy has shown better results in long-term survival. A multi-phase contrast-enhanced CT (CECT) scan is a pivotal technique in the diagnosis of SRHCC due to its diagnostic accuracy and optimal temporal resolution. The correct interpretation of the main CT findings in SRHCC, such as active contrast extravasation and the sentinel clot sign, is fundamental for a prompt and correct diagnosis. Furthermore, CT also plays a role as a post-operative control procedure, especially in patients treated with TAE/TACE. Therefore, a multi-phase CECT scan should be the diagnostic tool of choice in SRHCC since it suggests an immediate need for treatment with a consequent improvement in prognosis.

Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques

Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor and the third cause of cancer-related deaths worldwide. For many years, the golden standard for HCC diagnosis has been the needle biopsy, which is invasive and carries risks. Computerized methods are due to achieve a noninvasive, accurate HCC detection process based on medical images. We developed image analysis and recognition methods to perform automatic and computer-aided diagnosis of HCC. Conventional approaches that combined advanced texture analysis, mainly based on Generalized Co-occurrence Matrices (GCM) with traditional classifiers, as well as deep learning approaches based on Convolutional Neural Networks (CNN) and Stacked Denoising Autoencoders (SAE), were involved in our research. The best accuracy of 91% was achieved for B-mode ultrasound images through CNN by our research group. In this work, we combined the classical approaches with CNN techniques, within B-mode ultrasound images. The combination was performed at the classifier level. The CNN features obtained at the output of various convolution layers were combined with powerful textural features, then supervised classifiers were employed. The experiments were conducted on two datasets, acquired with different ultrasound machines. The best performance, above 98%, overpassed our previous results, as well as representative state-of-the-art results.

Follow Up Imaging in Hepatocellular Cancer Ultrasound Screening Exams With Poor Visualization Scores

OBJECTIVES

The Ultrasound Liver Imaging Reporting and Data Systems (LI-RADS) provides standardized terminology and reporting for ultrasound (US) examinations performed for hepatocellular cancer (HCC) screening. However, there are no recommendations regarding follow up imaging for visualization scores with suboptimal visualization. Therefore, the aim of this study is to examine follow up imaging practices in the setting of US studies scored as B (moderate limitations) and C (severe limitations).

METHODS

A single center retrospective analysis of studies from 2017 to 2021 with HCC US screening visualization scores of B and C was performed. Follow up imaging with US, CT, or MRI within 6 months with visualization score B or C on initial US were included.

RESULTS

Five hundred and sixty HCC US studies with suboptimal imaging were reviewed. Of those with follow up imaging, patients with a visualization score of B underwent US in more than half (58%) of the cases while those with visualization score of C underwent more CT/MRI studies (62.5%, P = .12) Patients with visualization score of B had more MRI exams performed (55%) while patients with a visualization score of C underwent more CT exams (70%, P = .16).

CONCLUSIONS

Currently, there are no guidelines instructing follow up imaging on HCC screening ultrasounds with poor visualization, and the data suggests that providers have taken a heterogeneous approach. This suggests a need for society recommendations on how to approach HCC screening ultrasounds in patients with suboptimal studies.