Identification of Imaging Predictors Discriminating Different Primary Liver Tumours in Patients with Chronic Liver Disease on Gadoxetic Acid-enhanced MRI: a Classification Tree Analysis

A Nomogram Based on MRI Visual Decision Tree to Evaluate Vascular Endothelial Growth Factor in Hepatocellular Carcinoma

BACKGROUNDS

Anti-vascular endothelial growth factor (VEGF) therapy has been developed and recognized as an effective treatment for hepatocellular carcinoma (HCC). However, there remains a lack of noninvasive methods in precisely evaluating VEGF expression in HCC.

PURPOSE

To establish a visual noninvasive model based on clinical indicators and MRI features to evaluate VEGF expression in HCC.

STUDY TYPE

Retrospective.

POPULATION

One hundred forty HCC patients were randomly divided into a training (N = 98) and a test cohort (N = 42).

FIELD STRENGTH/SEQUENCE

3.0 T, T2WI, T1WI including pre-contrast, dynamic, and hepatobiliary phases.

ASSESSMENT

The fusion model constructed by history of smoking, albumin-to-globulin ratio (AGR) and the Radio-Tree model was visualized by a nomogram.

STATISTICAL TESTS

Performances of models were assessed by receiver operating characteristic (ROC) curves. Student's t-test, Mann-Whitney U-test, chi-square test, Fisher's exact test, univariable and multivariable logistic regression analysis, DeLong's test, integrated discrimination improvement (IDI), Hosmer-Lemeshow test, and decision curve analysis were performed. P < 0.05 was considered statistically significant.

RESULTS

History of smoking and AGR ≤1.5 were clinical independent risk factors of the VEGF expression. In training cohorts, values of area under the curve (AUCs) of Radio-Tree model, Clinical-Radiological (C-R) model, fusion model which combined history of smoking and AGR with Radio-Tree model were 0.821, 0.748, and 0.871. In test cohort, the fusion model showed highest AUC (0.844) than Radio-Tree and C-R models (0.819, 0.616, respectively). DeLong's test indicated that the fusion model significantly differed in performance from the C-R model in training cohort (P = 0.015) and test cohort (P = 0.007).

DATA CONCLUSION

The fusion model combining history of smoking, AGR and Radio-Tree model established with ML algorithm showed the highest AUC value than others.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Management of intrahepatic cholangiocarcinoma: a review for clinicians

Intrahepatic cholangiocarcinoma (iCCA) is an aggressive liver malignancy that arises from second-order biliary epithelial cells. Its incidence is gradually increasing worldwide. Well-known risk factors have been described, although in many cases, they are not identifiable. Treatment options are continuously expanding, but the prognosis of iCCA remains dismal. R0 liver resection remains the only curative treatment, but only a limited number of patients can benefit from it. Frequently, major hepatectomies are needed to completely remove the tumour. This could contraindicate surgery or increase postoperative morbidity in patients with chronic liver disease and small remnant liver volume. In cases of anticipated inadequate future liver remnant, regenerative techniques may be used to expand resectability. The role and extent of lymphadenectomy in iCCA are still matters of debate. Improvements in iCCA diagnosis and better understanding of genetic profiles might lead to optimized surgical approaches and drug therapies. The role of neoadjuvant and adjuvant therapies is broadening, gaining more and more acceptance in clinical practice. Combining surgery with locoregional therapies and novel drugs, such as checkpoint-inhibitors and molecular-targeted molecules, might improve treatment options and survival rates. Liver transplantation, after very poor initial results, is now receiving attention for the treatment of patients with unresectable very early iCCA (i.e. <2 cm) in cirrhotic livers, showing survival outcomes comparable to those of hepatocellular carcinoma. Ongoing prospective protocols are testing the efficacy of liver transplantation for patients with unresectable, advanced tumours confined to the liver, with sustained response to neoadjuvant treatment. In such a continuously changing landscape, the aim of our work is to review the state-of-the-art in the surgical and medical treatment of iCCA.

Radiologic features of hepatocellular carcinoma related to prognosis

The cross-sectional imaging findings play a crucial role in the diagnosis of hepatocellular carcinoma (HCC). Recent studies have shown that imaging findings of HCC are not only relevant for the diagnosis of HCC, but also for identifying genetic and pathologic characteristics and determining prognosis. Imaging findings such as rim arterial phase hyperenhancement, arterial phase peritumoral hyperenhancement, hepatobiliary phase peritumoral hypointensity, non-smooth tumor margin, low apparent diffusion coefficient, and the LR-M category of the Liver Imaging-Reporting and Data System have been reported to be associated with poor prognosis. In contrast, imaging findings such as enhancing capsule appearance, hepatobiliary phase hyperintensity, and fat in mass have been reported to be associated with a favorable prognosis. Most of these imaging findings were examined in retrospective, single-center studies that were not adequately validated. However, the imaging findings can be applied for deciding the treatment strategy for HCC, if their significance can be confirmed by a large multicenter study. In this literature, we would like to review imaging findings related to the prognosis of HCC as well as their associated clinicopathological characteristics.

Gadoxetate disodium-enhanced MRI for diagnosis of hepatocellular carcinoma in patients with chronic liver disease: late portal venous phase may improve identification of enhancing capsule

PURPOSE

To investigate added value of late portal venous phase (LPVP) for identification of enhancing capsule (EC) on gadoxetate disodium-enhanced MRI (GD-MRI) for diagnosing hepatocellular carcinoma (HCC) in patients with chronic liver disease (CLD).

METHODS

This retrospective study comprised 116 high-risk patients with 128 pathologically proven HCCs who underwent GD-MRI including arterial phase, conventional portal venous phase (CPVP, 60 s), LPVP (mean, 104.4 ± 6.7 s; range, 90-119 s), and transitional phase (TP, 3 min). Two independent radiologists assessed the presence of major HCC features, including EC on CPVP and/or TP (CPVP/TP) and EC on LPVP. The frequency of EC was compared on GD-MRI between with and without inclusion of LPVP. The radiologists assigned Liver Imaging Reporting and Data System (LI-RADS) v2018 categories before and after identifying EC on LPVP.

RESULTS

Of the total 128 HCCs, 74 and 73 revealed EC on CPVP/TP for reviewer 1 and 2, respectively. After inclusion of LPVP, each reviewer identified seven more EC [Reviewer 1, 57.8% (74/128) vs. 63.3% (81/128); Reviewer 2, 57.0% (73/128) vs. 62.5% (80/128); P = 0.016, respectively]. Sensitivities of LR-5 assignment for diagnosing HCCs were not significantly different in GD-MRI with or without LPVP for EC identification [Reviewer 1, 71.9% (92/128) vs. 72.7% (93/128); Reviewer 2, 75.0% (96/128) vs. 75.8% (97/128); P = 1.000, respectively].

CONCLUSION

Including the LPVP in GD-MRI may improve identification of EC of HCC in patients with CLD. However, LI-RADS v2018 using GD-MRI showed comparable sensitivity for diagnosing HCC regardless of applying LPVP for EC.

Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images

Liver cancer is a malignant tumor with high morbidity and mortality, which has a tremendous negative impact on human survival. However, it is a challenging task to recognize tens of thousands of histopathological images of liver cancer by naked eye, which poses numerous challenges to inexperienced clinicians. In addition, factors such as long time-consuming, tedious work and huge number of images impose a great burden on clinical diagnosis. Therefore, our study combines convolutional neural networks with histopathology images and adopts a feature fusion approach to help clinicians efficiently discriminate the differentiation types of primary hepatocellular carcinoma histopathology images, thus improving their diagnostic efficiency and relieving their work pressure. In this study, for the first time, 73 patients with different differentiation types of primary liver cancer tumors were classified. We performed an adequate classification evaluation of liver cancer differentiation types using four pre-trained deep convolutional neural networks and nine different machine learning (ML) classifiers on a dataset of liver cancer histopathology images with multiple differentiation types. And the test set accuracy, validation set accuracy, running time with different strategies, precision, recall and F1 value were used for adequate comparative evaluation. Proved by experimental results, fusion networks (FuNet) structure is a good choice, which covers both channel attention and spatial attention, and suppresses channel interference with less information. Meanwhile, it can clarify the importance of each spatial location by learning the weights of different locations in space, then apply it to the study of classification of multi-differentiated types of liver cancer. In addition, in most cases, the Stacking-based integrated learning classifier outperforms other ML classifiers in the classification task of multi-differentiation types of liver cancer with the FuNet fusion strategy after dimensionality reduction of the fused features by principle component analysis (PCA) features, and a satisfactory result of 72.46% is achieved in the test set, which has certain practicality.

Added-value of ancillary imaging features for differentiating hepatocellular carcinoma from intrahepatic mass-forming cholangiocarcinoma on Gd-BOPTA-enhanced MRI in LI-RADS M

OBJECTIVE

To identify the reliable imaging features and added-value of ancillary imaging features for differentiating hepatocellular carcinoma (HCC) and intrahepatic mass-forming cholangiocarcinoma (IMCC) assigned to LI-RADS M on Gd-BOPTA-enhanced MRI.

METHODS

This retrospective study included 116 liver observations assigned to LI-RADS M, including 82 HCC and 34 IMCC histologically confirmed. Before and after adding ancillary imaging features, all variables with a p-value of < 0.05 in univariable analysis were entered into a multivariable logistic regression analysis to build diagnostic model 1 and model 2 to find reliable predictors of HCC diagnosis. Receiver operating characteristic (ROC) analysis and the DeLong test were used to compare the two models.

RESULTS

Forty-nine of 82(59.8%) HCCs had a considerably higher frequency of enhancing "capsule" compared with IMCCs (p < 0.001). Based on LI-RADS major and LR-M features and clinical-pathologic factors, an elevated AFP level (OR = 10.676, 95%CI = 2.125-4.470, p = 0.004) and enhancing "capsule" (OR = 20.558, 95%CI = 4.470-94.550, p < 0.001) were extracted as independent risk factors in Model 1. After adding ancillary imaging features, Male (OR = 23.452, 95%CI = 1.465-375.404, p = 0.026), enhancing "capsule" (OR = 13.161, 95%CI = 1.725-100.400, p = 0.013), septum (OR = 17.983, 95%CI = 1.049-308.181, p = 0.046), small-scale central HBP hyperintensity (OR = 44.386, 95%CI = 1.610-1223.484, p = 0.025) were confirmed as independent significant variables associated with HCC. Model 2 demonstrated significantly superior AUC (0.918 vs 0.845, p = 0.021) compared with Model 1. When any two or more predictors in model 2 were satisfied, sensitivity was 91.46%, and accuracy was at the top (87.93%).

CONCLUSION

Enhancing "capsule" was a reliable imaging feature to help identify HCC. Adding ancillary imaging features improved sensitivity and accuracy for HCC diagnosis with differentiation from IMCC in LR-M.

Development of a hepatocellular carcinoma imaging database and structured imaging reports based on PACS, HIS, and repository

Purpose

To establish a hepatocellular carcinoma imaging database and structured imaging reports based on PACS, HIS, and repository.

Methods

This study was approved by the Institutional Review Board. The steps of establishing the database are as follows: 1) According to the standards required for the intelligent diagnosis of HCC, it was attempted to design the corresponding functional modules after analyzing the requirements; 2) Based on client/server (C/S) mode, 3-tier architecture model was adopted. A user interface (UI) could receive data entered by users and show handled data. Business logic layer (BLL) could process the business logic of the data, and data access layer (DAL) could save the data in the database. The storage and management of HCC imaging data could be realized by the SQLSERVER database management software, and Delphi and VC++ programming languages were used.

Results

The test results showed that the proposed database could swiftly obtain the pathological, clinical, and imaging data of HCC from the picture archiving and communication system (PACS) and hospital information system (HIS), and perform data storage and visualization of structured imaging reports. According to the HCC imaging data, liver imaging reporting and data system (LI-RADS) assessment, standardized staging, and intelligent imaging analysis were carried out on the high-risk population to establish a one-stop imaging evaluation platform for HCC, strongly supporting clinicians in the diagnosis and treatment of HCC.

Conclusions

The establishment of a HCC imaging database can not only provide a huge amount of imaging data for the basic and clinical research on HCC, but also facilitate the scientific management and quantitative assessment of HCC. Besides, a HCC imaging database is advantageous for personalized treatment and follow-up of HCC patients.

Diagnosis of LI-RADS M lesions on gadoxetate-enhanced MRI: identifying cholangiocarcinoma-containing tumor with serum markers and imaging features

OBJECTIVES

The LI-RADS M (LR-M) category describes hepatic lesions probably or definitely malignant, but not specific for hepatocellular carcinoma in at-risk patients. Differentiation among LR-M entities, particularly detecting cholangiocarcinoma-containing tumors (M-CCs), is essential for treatment and prognosis. Thus, we aimed to develop diagnostic models on gadoxetate disodium-enhanced MRI comprising serum tumor markers and LI-RADS imaging features for M-CC.

METHODS

Consecutive at-risk patients with LR-M lesions exclusively (no co-existing LR-4 and/or LR-5 lesions) were retrieved retrospectively from a prospectively collected database spanning 3 years. Intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular-cholangiocarcinoma (c-HCC-CCA) were classified together as M-CC. LI-RADS features determined by three independent radiologists and clinically relevant serum tumor markers were used to generate M-CC diagnostic models through logistic regression analysis against histology. Per-patient performance was evaluated using area under the receiver operating curve (AUC), sensitivity, and specificity.

RESULTS

Forty-five patients were included, 42.2% (19/45) with hepatocellular carcinoma, 33.3% (15/45) with ICC, 13.3% (6/45) with c-HCC-CCA, and 11.1% (5/45) with other hepatic lesions. Carbohydrate antigen (CA)19-9 > 38 U/mL, α-fetoprotein (AFP) > 4.8 ng/mL, and absence of the LI-RADS feature "blood products in mass" were significant predictors of M-CC. Combining three predictors demonstrated AUC of 0.862, sensitivity of 76%, and specificity of 88%. The risk of M-CC with all three criteria fulfilled was 98% (AUC, 0.690; sensitivity, 38%; specificity, 100%).

CONCLUSIONS

In at-risk patients with LR-M lesions, integrating CA19-9, AFP, and the LI-RADS feature "blood products in mass" achieved high diagnostic performance for M-CC. When all three criteria were fulfilled, the specificity for M-CC was 100%.

KEY POINTS

• In at-risk patients who had LR-M lesions exclusively (no concomitant LR-4/5 lesions), a model with carbohydrate antigen > 38 U/mL, α-fetoprotein > 4.8 ng/mL, and absence of the LI-RADS feature "blood products in mass" achieved high accuracy for diagnosing cholangiocarcinoma-containing tumors. • In patients of whom all three criteria were fulfilled, the specificity for M-CC was 100%, which might reduce or eliminate the need for biopsy confirmation.

An overview of hepatocellular carcinoma with atypical enhancement pattern: spectrum of magnetic resonance imaging findings with pathologic correlation

BACKGROUND

In the setting of cirrhotic liver, the diagnosis of hepatocellular carcinoma (HCC) is straightforward when typical imaging findings consisting of arterial hypervascularity followed by portal-venous washout are present in nodules larger than 1 cm. However, due to the complexity of hepatocarcinogenesis, not all HCCs present with typical vascular behaviour. Atypical forms such as hypervascular HCC without washout, isovascular or even hypovascular HCC can pose diagnostic dilemmas. In such cases, it is important to consider also the appearance of the nodules on diffusion-weighted imaging and hepatobiliary phase. In this regard, diffusion restriction and hypointensity on hepatobiliary phase are suggestive of malignancy. If both findings are present in hypervascular lesion without washout, or even in iso- or hypovascular lesion in cirrhotic liver, HCC should be considered. Moreover, other ancillary imaging findings such as the presence of the capsule, fat content, signal intensity on T2-weighted image favour the diagnosis of HCC. Another form of atypical HCCs are lesions which show hyperintensity on hepatobiliary phase. Therefore, the aim of the present study was to provide an overview of HCCs with atypical enhancement pattern, and focus on their magnetic resonance imaging (MRI) features.

CONCLUSIONS

In order to correctly characterize atypical HCC lesions in cirrhotic liver it is important to consider not only vascular behaviour of the nodule, but also ancillary MRI features, such as diffusion restriction, hepatobiliary phase hypointensity, and T2-weighted hyperintensity. Fat content, corona enhancement, mosaic architecture are other MRI feautures which favour the diagnosis of HCC even in the absence of typical vascular profile.

Targetoid hepatic observations on gadoxetic acid-enhanced MRI using LI-RADS version 2018: emphasis on hepatocellular carcinomas assigned to the LR-M category

AIM

To determine useful imaging features for differentiating hepatocellular carcinoma (HCC) categorised as LR-M from non-HCC malignancies in using the Liver Imaging-Reporting and Data System (LI-RADS) version 2018 on gadoxetic acid-enhanced magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Patients at high-risk for HCC with surgically confirmed HCCs (n=131) and non-HCC malignancies (n=90) and who had undergone gadoxetic acid-enhanced MRI were included. LI-RADS categories were assigned to identify hepatic observations defined as LR-M by two radiologists. Major and ancillary imaging features of hepatic observation with targetoid appearance including intratumoural septa were compared between HCCs and non-HCC malignancies. A classification tree analysis (CTA) was applied to differentiate high-risk HCCs from non-HCC malignancies in the LR-M category.

RESULTS

A total of 36 HCCs (27.5%) and 70 non-HCC malignancies (77.8%) were assigned as LR-M. An enhancing capsule (p=0.0293), blood products in the mass (p=0.0393), non-targetoid restriction (p=0.018), and a septum (p=0.0053) were significantly predictive of HCC. On CTA, the presence of a septum was an initial predictor for a high probability of HCC followed by non-targetoid restriction. The CTA model has a sensitivity of 63.9%, specificity of 90%, and accuracy of 81.1% for differentiating HCC assigned LR-M from non-HCC malignancy.

CONCLUSION

A considerable proportion of HCCs could have been categorised as LR-M as they had a targetoid appearance on gadoxetic acid-enhanced MRI. An intratumoural septum and non-targetoid restriction as well as enhancing capsule and blood products in the mass may be useful for differentiating HCC assigned to LR-M from non-HCC malignancy on gadoxetic acid-enhanced MRI.

LRRK2 Is Associated with Recurrence-Free Survival in Intrahepatic Cholangiocarcinoma and Downregulation of LRRK2 Suppresses Tumor Progress In Vitro

BACKGROUND

The leucine-rich repeat kinase 2 (LRRK2) gene was confirmed to be associated with a variety of diseases, while the physiological function of LRRK2 remains poorly understood. Intrahepatic cholangiocarcinoma (ICC) has over the last 10 years become the focus of increasing concern largely. Despite recent progress in the standard of care and management options for ICC, the prognosis for this devastating cancer remains dismal.

METHODS

A total of 57 consecutive ICC patients who underwent curative hepatectomy in our institution were included in our study. We conduct a retrospective study to evaluate the prognostic value of LRRK2 in ICC after resection. The mechanism of LRRK2 in ICC development was also investigated in vitro.

RESULTS

All patients were divided into two groups according to the content of LRRK2 in the tissue microarray blocks via immunohistochemistry: low-LRRK2 group (n = 33) and high-LRRK2 group (n = 24). The recurrence-free survival rate of high-LRRK2 group was significantly poorer than that of low-LRRK2 group (P = 0.010). Multivariate analysis showed high-LRRK2 was the prognostic factor for recurrence-free survival after hepatectomy. We demonstrated that downregulation of LRRK2 depressed the proliferation and metastasis of ICC cells in vitro.

CONCLUSION

We provide evidence that LRRK2 was an independent prognostic factor for ICC in humans by participating in the proliferation and metastasis of ICC cells.

Deep Learning-Based Classification of Liver Cancer Histopathology Images Using Only Global Labels

Liver cancer is a leading cause of cancer deaths worldwide due to its high morbidity and mortality. Histopathological image analysis (HIA) is a crucial step in the early diagnosis of liver cancer and is routinely performed manually. However, this process is time-consuming, error-prone, and easily affected by the expertise of pathologists. Recently, computer-aided methods have been widely applied to medical image analysis; however, the current medical image analysis studies have not yet focused on the histopathological morphology of liver cancer due to its complex features and the insufficiency of training images with detailed annotations. This paper proposes a deep learning method for liver cancer histopathological image classification using only global labels. To compensate for the lack of detailed cancer region annotations in those images, patch features are extracted and fully utilized. Transfer learning is used to obtain the patch-level features and then combined with multiple-instance learning to acquire the image-level features for classification. The method proposed here solves the processing of large-scale images and training sample insufficiency in liver cancer histopathological images for image classification. The proposed method can distinguish and classify liver histopathological images as abnormal or normal with high accuracy, thus providing support for the early diagnosis of liver cancer.

Hepatocellular carcinoma (HCC) versus non-HCC: accuracy and reliability of Liver Imaging Reporting and Data System v2018

PURPOSE

The Liver Imaging Reporting and Data System (LI-RADS) was created to standardize the diagnostic criteria for hepatocellular carcinoma (HCC) and has undergone multiple revisions including a recent update in 2018 (v2018). The primary aim of this study was to determine the diagnostic performance and interrater reliability (IRR) of LI-RADS v2018 for distinguishing HCC from non-HCC primary hepatic malignancy in patients 'at-risk' for HCC. A secondary aim was to assess the impact of changes introduced in the v2018 diagnostic algorithm.

METHODS

This retrospective study combined a 10-year experience of pathologically proven primary liver malignancies from two large liver transplant centers. Two blinded readers independently evaluated each lesion and assigned a LI-RADS diagnostic category, additionally scoring all relevant imaging features. Changes in category based on the reader-provided features and the new v2018 criteria were assessed by a study coordinator.

RESULTS

The final study cohort comprised 105 HCCs and 73 non-HCC primarily liver malignancies. LI-RADS had a high specificity for distinguishing HCC from non-HCC (89% and 90% for reader 1 and reader 2, respectively), and IRR was moderate to substantial for final LI-RADS category and most features. Revision of the LI-RADS v2018 diagnostic algorithm resulted in very few changes [5 (2.8%) and 3 (1.7%) for reader 1 and reader 2, respectively] in overall lesion classification.

CONCLUSION

LI-RADS diagnostic categories and features had moderate to substantial IRR and high specificity for distinguishing HCC from non-HCC primary liver malignancy. Revision of LI-RADS v2018 diagnostic algorithm resulted in reclassification of very few lesions.

Predicting peritumoral Glisson's sheath invasion of intrahepatic cholangiocarcinoma with preoperative CT imaging

BACKGROUND

To investigate the differences of clinicopathological characteristics and computed tomography (CT) features between intrahepatic cholangiocarcinomas (ICC) with and without peritumoral Glisson's sheath invasion (PGSI), and to construct a nomogram to predict PGSI of ICCs preoperatively.

METHODS

The clinicopathological characteristics and CT features of 84 ICCs were retrospectively analyzed and compared between ICCs with (30/84, 35.7%) and without PGSI (54/84, 64.3%). Multivariate logistic regression analysis was used to identify preoperative independent predictors of PGSI in ICCs. A nomogram was constructed to predict PGSI preoperatively.

RESULTS

ICCs with and without PGSI differed significantly in the presence of abdominal pain, serum carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) levels, TNM and T stages, tumor location, intratumoral calcifications, intrahepatic bile duct dilatation, intrahepatic bile duct calculus, morphologic type and dynamic enhancement pattern on CT images (all P<0.05). Abdominal pain, serum CEA level, intrahepatic bile duct dilatation, and morphologic type were independent predictors of PGSI in ICCs. A nomogram based on those predictors was constructed to predict PGSI preoperatively with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.908 (P<0.001).

CONCLUSIONS

Clinicopathological characteristics and CT features differed significantly between ICCs with and without PGSI. A nomogram including abdominal pain, serum CEA level, intrahepatic bile duct dilatation, and morphologic type could predict PGSI accurately.

How to utilize LR-M features of the LI-RADS to improve the diagnosis of combined hepatocellular-cholangiocarcinoma on gadoxetate-enhanced MRI?

OBJECTIVES

To investigate the diagnostic accuracy of each LR-M feature defined in version 2017 of the Liver Imaging Reporting and Data System (LI-RADS) and determine the optimal LR-M feature for differentiating combined hepatocellular-cholangiocarcinoma (cHCC-CCA) and hepatocellular carcinoma (HCC) on gadoxetate-enhanced magnetic resonance imaging (MRI).

METHODS

Ninety-nine patients with pathologically proven cHCC-CCA (n = 33) or HCC (n = 66) after surgery were identified. Two radiologists retrospectively assessed preoperative gadoxetate-enhanced MRI for features favoring non-HCC malignancies (LR-M features) according to LI-RADS version 2017. Multivariate logistic regression analysis was performed to determine the independent differential features. The sensitivity and specificity for diagnosing cHCC-CCA were calculated for each LR-M feature.

RESULTS

Targetoid appearance showed the highest sensitivity (75.8%, 95% confidence interval [CI] 60.6%, 87.3%) to correctly identify cHCC-CCA as LR-M. At least one LR-M feature was observed in 31 (93.9%) patients with cHCC-CCA and 34 (51.5%) patients with HCC. The sensitivity and specificity for diagnosing cHCC-CCA using the presence of any one of the LR-M features were 93.9% (95% CI 80.7, 98.9) and 48.5% (95% CI 41.9, 51.0), respectively. The presence of three LR-M features yielded the highest diagnostic accuracy of 80.8% (95% CI 72.1, 86.1) with a reduced sensitivity of 54.5% (95% CI 41.4, 62.5).

CONCLUSION

The majority of cHCC-CCA cases can be properly categorized as LR-M when any one of the LR-M features defined in the LI-RADS version 2017 is used as a determiner. However, approximately half of HCC cases also show at least one LR-M feature.

KEY POINTS

• Targetoid appearance, including rim APHE, peripheral "washout" appearance, and delayed central enhancement, was the LR-M feature that identified cHCC-CCA as a non-HCC malignancy with the highest sensitivity. • Most cHCC-CCA cases can be properly categorized as LR-M when the presence of any one of the LR-M features was used as the determiner. • Approximately half of HCC cases also showed at least one LR-M feature.

LI-RADS M (LR-M): definite or probable malignancy, not specific for hepatocellular carcinoma

LI-RADS v2017 introduces major changes to the diagnostic criteria for LR-M observations to better guide radiologists in the use of this malignant category designation. LR-M is intended to preserve the specificity of the LI-RADS algorithm for diagnosis of HCC while not losing sensitivity for diagnosis of malignancy. The purpose of this paper is to provide a brief background on LR-M, discuss the diagnostic criteria new to v2017, special considerations for its application, and management implications.

Imaging features of hepatocellular carcinoma compared to intrahepatic cholangiocarcinoma and combined tumor on MRI using liver imaging and data system (LI-RADS) version 2014

PURPOSE

To evaluate the prevalence of major and ancillary imaging features from liver imaging reporting and data systems (LI-RADS) version 2014 and their interreader agreement when comparing hepatocellular carcinoma (HCC) to intrahepatic cholangiocarcinoma (ICC) and combined tumor (cHCC-CC).

METHODS

The Institutional Review Board approved this HIPAA-compliant retrospective study and waived the requirement for patients' informed consent. Patients with resected HCC (n = 51), ICC (n = 40), and cHCC-CC (n = 11) and available pre-operative contrast-enhanced MRI were included from 2000 to 2015. Imaging features and final LI-RADS category were evaluated by four radiologists. Imaging features were compared by Fisher's exact test and interreader agreements were assessed by κ statistics.

RESULTS

None of the features were unique to either HCC or non-HCC. Imaging features that were significantly more common among HCC compared to ICC and cHCC-CC included washout (76%-78% vs. 10%-35%, p < 0.001), capsule (55%-71% vs. 16%-49%, p < 0.05), and intralesional fat (27%-52% vs. 2%-12%, p < 0.002). Features that were more common among ICC and cHCC-CC included peripheral arterial phase hyperenhancement (40%-64% vs. 10%-14%, p < 0.001) and progressive central enhancement (65%-82% vs. 14%-25%, p < 0.001). The interreader agreement was moderate for each of these imaging features (κ = 0.41-0.55). Moderate agreement was also achieved in the assignment of LR-M (κ = 0.53), with an overall sensitivity and specificity for non-HCC malignancy of 86.3% and 78.4%, respectively.

CONCLUSION

HCC and non-HCC show significant differences in the prevalence of imaging features defined by LI-RADS, and are identified by radiologists with moderate interreader agreement. Using LI-RADS, radiologists also achieved moderate interreader agreement in the assignment of the LR-M category.

LI-RADS® ancillary features on CT and MRI

The Liver Imaging Reporting and Data System (LI-RADS) uses an algorithm to assign categories that reflect the probability of hepatocellular carcinoma (HCC), non-HCC malignancy, or benignity. Unlike other imaging algorithms, LI-RADS utilizes ancillary features (AFs) to refine the final category. AFs in LI-RADS v2017 are divided into those favoring malignancy in general, those favoring HCC specifically, and those favoring benignity. Additionally, LI-RADS v2017 provides new rules regarding application of AFs. The purpose of this review is to discuss ancillary features included in LI-RADS v2017, the rationale for their use, potential pitfalls encountered in their interpretation, and tips on their application.

Predicting IDH mutation status of intrahepatic cholangiocarcinomas based on contrast-enhanced CT features

OBJECTIVES

To explore the difference in contrast-enhanced computed tomography (CT) features of intrahepatic cholangiocarcinomas (ICCs) with different isocitrate dehydrogenase (IDH) mutation status.

METHODS

Clinicopathological and contrast-enhanced CT features of 78 patients with 78 ICCs were retrospectively analysed and compared based on IDH mutation status.

RESULTS

There were 11 ICCs with IDH mutation (11/78, 14.1%) and 67 ICCs without IDH mutation (67/78, 85.9%). IDH-mutated ICCs showed intratumoral artery more often than IDH-wild ICCs (p = 0.023). Most ICCs with IDH mutation showed rim and internal enhancement (10/11, 90.9%), while ICCs without IDH mutation often appeared diffuse (26/67, 38.8%) or with no enhancement (4/67, 6.0%) in the arterial phase (p = 0.009). IDH-mutated ICCs showed significantly higher CT values, enhancement degrees and enhancement ratios in arterial and portal venous phases than IDH-wild ICCs (all p < 0.05). The CT value of tumours in the portal venous phase performed best in distinguishing ICCs with and without IDH mutation, with an area under the curve of 0.798 (p = 0.002).

CONCLUSIONS

ICCs with and without IDH mutation differed significantly in arterial enhancement mode, and the tumour enhancement degree on multiphase contrast-enhanced CT was helpful in predicting IDH mutation status.

KEY POINTS

• IDH mutation occurred frequently in ICCs. • ICCs with and without IDH mutation differed significantly in arterial enhancement mode. • ICCs with IDH mutation enhanced more than those without IDH mutation. • Enhancement ratio and tumour CT value can predict IDH mutation status.

Differentiation of Intrahepatic Cholangiocellular Carcinoma from Hepatocellular Carcinoma in the Cirrhotic Liver Using Contrast-enhanced MR Imaging

RATIONALE AND OBJECTIVES

This study aimed to investigate the potential of contrast-enhanced magnetic resonance imaging features to differentiate between mass-forming intrahepatic cholangiocellular carcinoma (ICC) and hepatocellular carcinoma (HCC) in cirrhotic livers.

MATERIALS AND METHODS

This study, performed between 2001 and 2013, included 64 baseline magnetic resonance imaging examinations with pathohistologically proven liver cirrhosis, presenting with either ICC (n = 32) or HCC (n = 32) tumors. To distinguish ICC form HCC tumors, 20 qualitative single-lesion descriptors were evaluated by two readers, in consensus, and statistically classified using the chi-square automatic interaction detection (CHAID) methodology. Diagnostic performance was assessed by a receiver operating characteristic analysis.

RESULTS

The CHAID algorithm identified three independent categorical lesion descriptors, including (1) liver capsular retraction; (2) progressive or persistent enhancement pattern or wash-out on the T1-weighted delayed phase; and (3) signal intensity appearance on T2-weighted images that could help to reliably differentiate ICC from HCC, which resulted in an AUC of 0.807, and a sensitivity and specificity of 68.8 and 90.6 (95% confidence interval 75.0-98.0), respectively.

CONCLUSIONS

The proposed CHAID algorithm provides a simple and robust step-by-step classification tool for a reliable and solid differentiation between ICC and HCC tumors in cirrhotic livers.

Extracellular contrast agent-enhanced MRI: 15-min delayed phase may improve the diagnostic performance for hepatocellular carcinoma in patients with chronic liver disease

OBJECTIVES

To determine the value of a 15-min delayed phase in extracellular contrast agent (ECA)-enhanced magnetic resonance imaging (MRI) for evaluation of hepatocellular carcinoma (HCC) in patients with chronic liver disease.

METHODS

Between 2014 and 2015, 103 patients with chronic liver disease underwent ECA-enhanced MRI; 133 lesions consisting of 107 HCCs, 23 benign lesions and three non-HCC malignancies were identified with pathological or clinical diagnosis. MRI images were reviewed by two abdominal radiologists independently using the European Association for the Study of the Liver (EASL) and Liver Imaging Reporting and Data System (LI-RADS) criteria. Imaging features observed in the 15-min delayed phase were recorded.

RESULTS

Of 107 HCCs, three or four additional HCCs were diagnosed according to the EASL criteria by adding the 15-min delayed phase, increasing sensitivity (Reviewer 1, from 69.2-72.0 % [P = 0.072]; Reviewer 2, from 75.7-79.4 % [P = 0.041]). Reviewers 1 and 2 upgraded one and four HCCs from LR-4 to LR-5 based on the LI-RADS, respectively. Among 23 benign lesions, no additional findings were observed in the 15-min delayed phase.

CONCLUSIONS

Including the 15-min delayed phase in ECA-enhanced MRI may improve the diagnostic performance for HCC in patients with chronic liver disease.

KEY POINTS

• Additional acquisition of 15-min delayed phase (FDP) requires approximately 20 s. • About 5 % of HCCs show washout or capsule appearance only in FDP. • Including FDP improves the sensitivity of extracellular contrast agent-enhanced MRI for HCC. • These results are applicable only to patients with chronic liver disease.

MRI features of primary rare malignancies of the liver: A report from four university centres

PURPOSE

To determine if rare primary malignancies of the liver may have consistent features on magnetic resonance imaging (MRI).

MATERIALS AND METHODS

This IRB-compliant retrospective study reviewed the records from the pathology departments of four university centres over an 11-year period from 2005-2016 to identify rare primary malignant tumours, which were cross-referenced with MRI records. MRI studies of these patients were reviewed to determine if these tumours exhibited consistent and distinctive features.

RESULTS

Sixty patients were identified with rare primary liver tumours. The following distinctive features and frequency of occurrence were observed: mixed hepatocellular carcinoma-cholangiocarcinoma showed regions of wash-out in 7/19 of patients; 6/6 of fibrolamellar carcinomas demonstrated large heterogeneous lesions with large heterogeneous central scars; epithelioid haemangioendothelioma larger than 2 cm showed target-like enhancement in late-phase enhancement in 9/13; sarcomas excluding angiosarcoma had central necrosis in 3/9 and haemorrhage in 5/9; angiosarcomas showed centripedal progressive nodular enhancement in 3/6 and showed regions of haemorrhage in 3/6; and 7/7 of primary hepatic lymphomas showed encasement of vessels.

CONCLUSION

Although helpful features for the differentiation of rare primary malignancies of the liver are identified, no MRI features appear to be specific and therefore histopathological confirmation is usually required for definitive diagnosis.

KEY POINTS

• No MRI features appear to be specific for rare primary liver malignancies. • Haemorrhage is a helpful sign in diagnosis of primary hepatic sarcomas. • Angiosarcomas may show progressive nodular enhancement towards the centre mimicking haemangioma. • Vessel encasement is a helpful sign in diagnosis of primary hepatic lymphoma.

Added value of smooth hypointense rim in the hepatobiliary phase of gadoxetic acid-enhanced MRI in identifying tumour capsule and diagnosing hepatocellular carcinoma

OBJECTIVES

To examine the added value of considering smooth hypointense rim in the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI as capsule appearance for diagnosing tumour capsules and hepatocellular carcinoma (HCC).

METHODS

A total of 377 hepatic lesions (330 HCCs, 35 non-HCC malignancies and 12 benign) were included from 345 patients who underwent resection after MRI between January 2008 and December 2011. Two radiologists assessed the presence or absence of conventional capsule appearance and smooth hypointense rim in the HBP, and categorized each hepatic lesion according to the Liver Imaging Reporting and Data System. Difference in diagnostic performance was evaluated using the generalized estimating equation method.

RESULTS

For identifying capsule, the sensitivity and accuracy of HBP hypointense rim were significantly higher than those of conventional capsule appearance (81.5 % vs. 57.8 % and 76.1 % vs. 59.4 %, respectively; P < 0.001). For diagnosing HCC, the sensitivity and accuracy of LR-5 or LR-5 V were significantly higher when the HBP hypointense rim was also considered capsule appearance (83 % vs. 72.7 % and 84.1 % vs. 75.1 %, respectively; P < 0.001), with the same specificity (91.5 %).

CONCLUSIONS

Regarding smooth hypointense rim in the HBP as capsule appearance could improve the detection of tumour capsule and the diagnosis of HCC.

KEY POINTS

• Identifying tumour capsule is important for diagnosis of hepatocellular carcinoma (HCC). • Gadoxetic acid-enhanced MRI provides hepatobiliary phase (HBP) images. • Smooth hypointense rim seen in HBP may represent tumour capsule. • Regarding smooth hypointense rim as capsule appearance may improve HCC diagnosis.