Predicting long-term survival among patients with HCC

BACKGROUND

Prognosticating survival among patients with HCC and cirrhosis must account for both the tumor burden/stage, as well as the severity of the underlying liver disease. Although there are many staging systems used to guide therapy, they have not been widely adopted to predict patient-level survival after the diagnosis of HCC. We sought to develop a score to predict long-term survival among patients with early- to intermediate-stage HCC using purely objective criteria.

METHODS

Retrospective cohort study among patients with HCC confined to the liver, without major medical comorbidities within the Veterans Health Administration from 2014 to 2023. Tumor data were manually abstracted and combined with clinical and laboratory data to predict 5-year survival from HCC diagnosis using accelerated failure time models. The data were randomly split using a 75:25 ratio for training and validation. Model discrimination and calibration were assessed and compared to other HCC staging systems.

RESULTS

The cohort included 1325 patients with confirmed HCC. A risk score using baseline clinical, laboratory, and HCC-related survival had excellent discrimination (integrated AUC: 0.71 in the validation set) and calibration (based on calibration plots and Brier scores). Models had superior performance to the BCLC and ALBI scores and similar performance to the combined BCLC-ALBI score.

CONCLUSIONS

We developed a risk score using purely objective data to accurately predict long-term survival for patients with HCC. This score, if validated, can be used to prognosticate survival for patients with HCC, and, in the setting of liver transplantation, can be incorporated to consider the net survival benefit of liver transplantation versus other curative options.

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.

Manifestations of Sickle Cell Disorder at Abdominal and Pelvic Imaging

Sickle cell disorder (SCD) refers to a spectrum of hematologic disorders that cause a characteristic clinical syndrome affecting the entire body. It is the most prevalent monogenetic hemoglobinopathy worldwide, with a wide range of focal and systemic expressions. Hemoglobin gene mutation leads to the formation of abnormal sickle-shaped red blood cells, which cause vascular occlusion and result in tissue and organ ischemia and infarction. Recurrent episodes of acute illness lead to progressive multisystem organ damage and dysfunction. Vaso-occlusion, hemolysis, and infection as a result of functional asplenia are at the core of the disease manifestations. Imaging plays an essential role in the diagnosis and management of SCD-related complications in the abdomen and pelvis. A thorough understanding of the key imaging findings of SCD complications involving hepatobiliary, gastrointestinal, genitourinary, and musculoskeletal systems is crucial to timely recognition and accurate diagnosis. The authors aim to familiarize the radiologist with the SCD spectrum, focusing on the detection and evaluation of manifestations that may appear at imaging of the abdomen and pelvis. The topics the authors address include (a) the pathophysiology of the disease, (b) the placement of SCD among hemoglobinopathies, (c) the clinical presentation of SCD, (d) the role of imaging in the evaluation and diagnosis of patients with SCD who present with abdominal and pelvic manifestations in addition to extraperitoneal manifestations detectable at abdominal or pelvic imaging, (e) imaging features associated with common and uncommon sequelae of SCD in abdominal and pelvic imaging studies, and (f) a brief overview of management and treatment of patients with SCD. Online supplemental material is available for this article. ^©RSNA, 2022.

Pre-Transplant Alpha-Fetoprotein > 25.5 and Its Dynamic on Waitlist Are Predictors of HCC Recurrence after Liver Transplantation for Patients Meeting Milan Criteria

BACKGROUND AND AIM

Hepatocellular carcinoma (HCC) recurrence rates after liver transplantation (LT) range between 8 and 20%. Alpha-fetoprotein (AFP) levels at transplant can predict HCC recurrence, however a defined cut-off value is needed to better stratify patients. The aim of this study was to evaluate the rate of HCC recurrence at our centre and to identify predictors, focusing on AFP.

METHODS

We retrospectively analysed 236 consecutive patients that were waitlisted for HCC who all met the Milan criteria from January 2001 to December 2017 at our liver transplant centre. A total of twenty-nine patients dropped out while they were waitlisted, and 207 patients were included in the final analysis. All survival analyses included the competing-risk model.

RESULTS

The mean age was 56.8 ± 6.8 years. A total of 14% were female (n = 29/207). The median MELD (model for end-stage liver disease) at LT was 12 (9-16). The median time on the waitlist was 92 (41-170) days. The HCC recurrence rate was 16.4% (n = 34/208). The mean time to recurrence was 3.3 ± 2.8 years. The median AFP levels at transplant were higher in patients with HCC recurrence (p < 0.001). At multivariate analysis, the AFP value at transplant that was greater than 25.5 ng/mL (AUC 0.69) was a strong predictor of HCC recurrence after LT [sHR 3.3 (1.6-6.81); p = 0.001]. The HCC cumulative incidence function (CIF) of recurrence at 10 years from LT was significantly higher in patients with AFP > 25.5 ng/mL [34.3% vs. 11.5% (p = 0.001)]. Moreover, an increase in AFP > 20.8%, was significantly associated with HCC recurrence (p = 0.034).

CONCLUSIONS

In conclusion, in our retrospective study, the AFP level at transplant > 25.5 ng/mL and its increase greater than 20.8% on the waitlist were strong predictors of HCC recurrence after LT in a cohort of patients that were waitlisted within the Milan criteria. However further studies are needed to validate these data.

The many faces of cirrhosis

There are many different imaging features of cirrhosis, some of which are less commonly recognized. It is important that the radiologist is familiar with these features as cirrhosis can be first discovered on imaging performed for other indications, thus alerting the clinician for the need to screen for complications of cirrhosis and referral for potential treatment. This article reviews the various imaging findings of cirrhosis seen on cross-sectional imaging of the abdomen and pelvis.

Benign Neoplasms, Mass-Like Infections, and Pseudotumors That Mimic Hepatic Malignancy at MRI

A variety of conditions may mimic hepatic malignancy at MRI. These include benign hepatic tumors and tumor-like entities such as focal nodular hyperplasia-like lesions, hepatocellular adenoma, hepatic infections, inflammatory pseudotumor, vascular entities, and in the cirrhotic liver, confluent fibrosis, and hypertrophic pseudomass. These conditions demonstrate MRI features that overlap with hepatic malignancy, and can be challenging for radiologists to diagnose accurately. In this review we discuss the MRI manifestations of various conditions that mimic hepatic malignancy, and highlight features that may allow distinction from malignancy. Level of Evidence 5 Technical Efficacy Stage 3.

LI-RADS to categorize liver nodules in patients at risk of HCC: tool or a gadget in daily practice?

PURPOSE

To determine the effectiveness of liver reporting and data system (LI-RADS) to diagnose hepatocellular carcinoma (HCC) and to retrospectively evaluate its impact on the adopted therapeutic strategy.

MATERIALS AND METHODS

Preoperative imaging of 40 of 350 patients (median age 66, 31 M/9 F) submitted to liver resection for suspected HCC, between January 2008 and August 2019, has been retrospectively analyzed by two radiologists with different expertise, according to CT/MRI LI-RADS^® v2018, both blinded to clinical and pathological results and untrained to using aforementioned scoring system.

RESULTS

The perfect agreement between the readers was about 62.5% (25/40) (Cohen k: 0.41), better for LR-5 category (16/25) and higher in magnetic resonance imaging (MRI) investigations (68%; 13/19), which has been demonstrated the modality of choice for diagnosis of high probable and certain HCC, with arterial phase hyperenhancement as the most sensitive and accurate major feature. Compared to final histology, LR4 and LR5 scores assigned by senior radiologist reached sensitivity, specificity, positive and negative predictive values (PPV, PNV) and diagnostic accuracy of 90,9%, 29,0%, 93,8%, 62,5% and 87,5%, respectively, slightly higher than junior's ones. Misdiagnosis of HCC was done by both radiologists in the same two patients: 1 primary hepatic lymphoma (PHL) and 1 regenerative liver nodule (RLN). If LI-RADS would have been applied at the time of pre-surgical imaging, treatment planning would be modified in 10% of patients (4/40); the patient scheduled as LR-3 and finally resulted a focal nodular hyperplasia would have avoided liver resection.

CONCLUSIONS

Application of LI-RADS, especially on MRI, may provide a more accurate evaluation of suspected HCC. PHL and RLN are the Achille's heels according to our experience.

Optimizing size thresholds for detection of clinically significant prostate cancer on MRI: Peripheral zone cancers are smaller and more predictable than transition zone tumors

PURPOSE

To evaluate if size-based cut-offs based on MR imaging can successfully assess clinically significant prostate cancer (csPCA). The goal was to improve the currently applied size-based differentiation criterion in PI-RADS.

METHODS AND MATERIALS

MRIs of 293 patients who had undergone 3 T MR imaging with subsequent confirmation of prostate cancer on systematic and targeted MRI/TRUS-fusion biopsy were re-read by three radiologists. All identifiable tumors were measured on T2WI for lesions originating in the transition zone (TZ) and on DWI for lesions from the peripheral zone (PZ) and tabulated against their Gleason grade.

RESULTS

309 lesions were analyzed, 213 (68.9 %) in the PZ and 96 (31.1 %) in the TZ. ROC-Analysis showed a stronger correlation between lesion size and clinically significant (defined as Gleason Grade Group ≥ 2) prostate cancer (PCa) for the PZ (AUC = 0.73) compared to the TZ (AUC = 0.63). The calculated Youden index resulted in size cut-offs of 14 mm for PZ and 21 mm for TZ tumors.

CONCLUSION

Size cut-offs can be used to stratify prostate cancer with different optimal size thresholds in the peripheral zone and transition zone. There was a clearer separation of clinically significant tumors in peripheral zone cancers compared to transition zone cancers. Future iterations of PI-RADS could therefore take different size-based cut-offs for peripheral zone and transition zone cancers into account.

Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study

PURPOSE

To develop a deep convolutional neural network (CNN) model to categorize multiphase CT and MRI liver observations using the liver imaging reporting and data system (LI-RADS) (version 2014).

METHODS

A pre-existing dataset comprising 314 hepatic observations (163 CT, 151 MRI) with corresponding diameters and LI-RADS categories (LR-1-5) assigned in consensus by two LI-RADS steering committee members was used to develop two CNNs: pre-trained network with an input of triple-phase images (training with transfer learning) and custom-made network with an input of quadruple-phase images (training from scratch). The dataset was randomly split into training, validation, and internal test sets (70:15:15 split). The overall accuracy and area under receiver operating characteristic curve (AUROC) were assessed for categorizing LR-1/2, LR-3, LR-4, and LR-5. External validation was performed for the model with the better performance on the internal test set using two external datasets (EXT-CT and EXT-MR: 68 and 44 observations, respectively).

RESULTS

The transfer learning model outperformed the custom-made model: overall accuracy of 60.4% and AUROCs of 0.85, 0.90, 0.63, 0.82 for LR-1/2, LR-3, LR-4, LR-5, respectively. On EXT-CT, the model had an overall accuracy of 41.2% and AUROCs of 0.70, 0.66, 0.60, 0.76 for LR-1/2, LR-3, LR-4, LR-5, respectively. On EXT-MR, the model had an overall accuracy of 47.7% and AUROCs of 0.88, 0.74, 0.69, 0.79 for LR-1/2, LR-3, LR-4, LR-5, respectively.

CONCLUSION

Our study shows the feasibility of CNN for assigning LI-RADS categories from a relatively small dataset but highlights the challenges of model development and validation.

Characterization of liver nodules in patients with chronic liver disease by MRI: performance of the Liver Imaging Reporting and Data System (LI-RADS v.2018) scale and its comparison with the Likert scale

OBJECTIVES

To evaluate the performance of the LI-RADS v.2018 scale by comparing it with the Likert scale, in the characterization of liver lesions.

METHODS

A total of 39 patients with chronic liver disease underwent MR examination for characterization of 44 liver lesions. Images were independently analyzed by two radiologists using the LI-RADS scale and by another two radiologists using the Likert scale. The reference standard used was either histopathological evaluation or a 4-year MRI follow-up. Receiver operating characteristic analysis was performed.

RESULTS

The LI-RADS scale obtained an accuracy of 80%, a sensitivity of 72%, a specificity of 93%, a positive predictive value (PPV) of 93% and a negative predictive value (NPV) of 70%, while the Likert scale achieved an accuracy of 79%, a sensitivity of 73%, a specificity of 87%, a PPV of 89% and a NPV of 70%. The area under the curve (AUC) was 85% for the LI-RADS scale and 83% for the Likert scale. The inter-observer agreement was strong (k = 0.89) between the LI-RADS evaluators and moderate (k = 0.69) between the Likert evaluators.

CONCLUSIONS

There was no statistically significant difference between the performances of the two scales; nevertheless, we suggest that the LI-RADS scale be used, as it appeared more objective and consistent.

Joint Consensus Statement of the Indian National Association for Study of the Liver and Indian Radiological and Imaging Association for the Diagnosis and Imaging of Hepatocellular Carcinoma Incorporating Liver Imaging Reporting and Data System

Hepatocellular carcinoma (HCC) is the 6th most common cancer and the second most common cause of cancer-related mortality worldwide. There are currently no universally accepted practice guidelines for the diagnosis of HCC on imaging owing to the regional differences in epidemiology, target population, diagnostic imaging modalities, and staging and transplant eligibility. Currently available regional and national guidelines include those from the American Association for the Study of Liver Disease (AASLD), the European Association for the Study of the Liver (EASL), the Asian Pacific Association for the Study of the Liver, the Japan Society of Hepatology, the Korean Liver Cancer Study Group, Hong Kong, and the National Comprehensive Cancer Network in the United States. India with its large population and a diverse health infrastructure faces challenges unique to its population in diagnosing HCC. Recently, American Association have introduced a Liver Imaging Reporting and Data System (LIRADS, version 2017, 2018) as an attempt to standardize the acquisition, interpretation, and reporting of liver lesions on imaging and hence improve the coherence between radiologists and clinicians and provide guidance for the management of HCC. The aim of the present consensus was to find a common ground in reporting and interpreting liver lesions pertaining to HCC on imaging keeping LIRADSv2018 in mind.

[Contrast-enhanced ultrasound: Liver Imaging Reporting and Data System (CEUS LI-RADS)]

The American College of Radiology (ACR) endorsed the Liver Imaging Reporting and Data System (LI-RADS) for standardized reporting and data collection of computed tomography (CT) and magnetic resonance (MR) imaging for hepatocellular carcinoma (HCC) in high-risk patients (liver cirrhosis). The LI-RADS imaging criteria are used to classify 'observations' from 'definitely benign' (LR-1) to 'definitely HCC' (LR-5) based on imaging criteria.Coincidently, the recent approval in the United States of a microbubble contrast agent for liver imaging (Lumason®, known as SonoVue® in Europe and elsewhere), LI-RADS. is being expanded to include contrast-enhanced ultrasound (CEUS). An international working group was initiated in 2014. Herewith, the most current version of CEUS-LI-RADS is presented.

Contributions of Magnetic Resonance Imaging to Gastroenterological Practice: MRIs for GIs

MRI has transformed from the theoretical, investigative realm to mainstream clinical medicine over the past four decades and has become a core component of the diagnostic toolbox in the practice of gastroenterology (GI). Its success is attributable to exquisite contrast and the ability to isolate specific proton species through the use of different pulse sequences (i.e., T1-weighted, T2-weighted, diffusion-weighted) and exploiting extracellular and hepatobiliary contrast agents. Consequently, MRI has gained preeminence in various GI clinical applications: liver and pancreatic lesion evaluation and detection, liver transplantation evaluation, pancreatitis evaluation, Crohn's disease evaluation (using MR enterography) rectal cancer staging and perianal fistula evaluation. MR elastography, in concert with technical innovations allowing for fat and iron quantification, provides a noninvasive approach, or "MRI virtual liver biopsy" for diagnosis and management of chronic liver diseases. In the future, the arrival of ultra-high-field MR systems (7 T) and the ability to perform magnetic resonance spectroscopy in the abdomen promise even greater diagnostic insight into chronic liver disease.

LI-RADS v2017 for liver nodules: how we read and report

The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation and reporting of imaging examinations in patients at risk for hepatocellular carcinoma (HCC). For focal liver observations it assigns categories (LR-1 to 5, LR-M, LR-TIV), which reflect the relative probability of benignity or malignancy of the respective observation. The categories assigned are based on major and ancillary image features, which have been developed by the American College of Radiology (ACR) and validated in many studies. This review summarizes the relevant CT and MRI features and presents an image-guided approach for readers not familiar with LI-RADS on how to use the system. The widespread adoption of LI-RADS for reporting would help reduce inter-reader variability and improve communication among radiologists, hepatologists, hepatic surgeons and oncologists, thus leading to improved patient management.

Contrast-enhanced ultrasound of the liver: technical and lexicon recommendations from the ACR CEUS LI-RADS working group

Contrast-enhanced ultrasound (CEUS) is a specific form of ultrasound imaging performed with intravenous administration of microbubble contrast agents. It has been extensively used for liver tumor characterization and was recently added to the American College of Radiology Liver Imaging Reporting and Data System (CEUS LI-RADS). This paper describes technical recommendations for successful liver CEUS lesion characterization, and provides imaging protocol and Lexicon of imaging findings.

Interreader Reliability of LI-RADS Version 2014 Algorithm and Imaging Features for Diagnosis of Hepatocellular Carcinoma: A Large International Multireader Study

Purpose To determine in a large multicenter multireader setting the interreader reliability of Liver Imaging Reporting and Data System (LI-RADS) version 2014 categories, the major imaging features seen with computed tomography (CT) and magnetic resonance (MR) imaging, and the potential effect of reader demographics on agreement with a preselected nonconsecutive image set. Materials and Methods Institutional review board approval was obtained, and patient consent was waived for this retrospective study. Ten image sets, comprising 38-40 unique studies (equal number of CT and MR imaging studies, uniformly distributed LI-RADS categories), were randomly allocated to readers. Images were acquired in unenhanced and standard contrast material-enhanced phases, with observation diameter and growth data provided. Readers completed a demographic survey, assigned LI-RADS version 2014 categories, and assessed major features. Intraclass correlation coefficient (ICC) assessed with mixed-model regression analyses was the metric for interreader reliability of assigning categories and major features. Results A total of 113 readers evaluated 380 image sets. ICC of final LI-RADS category assignment was 0.67 (95% confidence interval [CI]: 0.61, 0.71) for CT and 0.73 (95% CI: 0.68, 0.77) for MR imaging. ICC was 0.87 (95% CI: 0.84, 0.90) for arterial phase hyperenhancement, 0.85 (95% CI: 0.81, 0.88) for washout appearance, and 0.84 (95% CI: 0.80, 0.87) for capsule appearance. ICC was not significantly affected by liver expertise, LI-RADS familiarity, or years of postresidency practice (ICC range, 0.69-0.70; ICC difference, 0.003-0.01 [95% CI: -0.003 to -0.01, 0.004-0.02]. ICC was borderline higher for private practice readers than for academic readers (ICC difference, 0.009; 95% CI: 0.000, 0.021). Conclusion ICC is good for final LI-RADS categorization and high for major feature characterization, with minimal reader demographic effect. Of note, our results using selected image sets from nonconsecutive examinations are not necessarily comparable with those of prior studies that used consecutive examination series. ^© RSNA, 2017.

Management consensus guideline for hepatocellular carcinoma: 2016 updated by the Taiwan Liver Cancer Association and the Gastroenterological Society of Taiwan

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality in Taiwan. To help clinical physicians to manage patients with HCC, the Taiwan Liver Cancer Association and the Gastroenterological Society of Taiwan produced the management consensus guideline for HCC.

METHODS

The recommendations focus on nine important issues on management of HCC, including surveillance, diagnosis, staging, surgery, local ablation, transarterial chemoembolization/transarterial radioembolization/hepatic arterial infusion chemotherapy, systemic therapy, radiotherapy, and prevention.

RESULTS

The consensus statements were discussed, debated and got consensus in each expert team. And then the statements were sent to all of the experts for further discussion and refinement. Finally, all of the experts were invited to vote for the statements, including the level of evidence and recommendation.

CONCLUSION

With the development of the management consensus guideline, HCC patients could benefit from the optimal therapeutic modality.

Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study

Purpose To investigate diagnostic performance by using a deep learning method with a convolutional neural network (CNN) for the differentiation of liver masses at dynamic contrast agent-enhanced computed tomography (CT). Materials and Methods This clinical retrospective study used CT image sets of liver masses over three phases (noncontrast-agent enhanced, arterial, and delayed). Masses were diagnosed according to five categories (category A, classic hepatocellular carcinomas [HCCs]; category B, malignant liver tumors other than classic and early HCCs; category C, indeterminate masses or mass-like lesions [including early HCCs and dysplastic nodules] and rare benign liver masses other than hemangiomas and cysts; category D, hemangiomas; and category E, cysts). Supervised training was performed by using 55 536 image sets obtained in 2013 (from 460 patients, 1068 sets were obtained and they were augmented by a factor of 52 [rotated, parallel-shifted, strongly enlarged, and noise-added images were generated from the original images]). The CNN was composed of six convolutional, three maximum pooling, and three fully connected layers. The CNN was tested with 100 liver mass image sets obtained in 2016 (74 men and 26 women; mean age, 66.4 years ± 10.6 [standard deviation]; mean mass size, 26.9 mm ± 25.9; 21, nine, 35, 20, and 15 liver masses for categories A, B, C, D, and E, respectively). Training and testing were performed five times. Accuracy for categorizing liver masses with CNN model and the area under receiver operating characteristic curve for differentiating categories A-B versus categories C-E were calculated. Results Median accuracy of differential diagnosis of liver masses for test data were 0.84. Median area under the receiver operating characteristic curve for differentiating categories A-B from C-E was 0.92. Conclusion Deep learning with CNN showed high diagnostic performance in differentiation of liver masses at dynamic CT. ^© RSNA, 2017 Online supplemental material is available for this article.

Magnetic resonance imaging of the cirrhotic liver: diagnosis of hepatocellular carcinoma and evaluation of response to treatment - Part 2

In the second part of this review, we will describe the ancillary imaging features of hepatocellular carcinoma (HCC) that can be seen on standard magnetic resonance imaging (MRI) protocol, and on novel and emerging protocols such as diffusion weighted imaging and utilization of hepatocyte-specific/hepatobiliary contrast agent. We will also describe the morphologic sub-types of HCC, and give a simplified non-invasive diagnostic algorithm for HCC, followed by a brief description of the liver imaging reporting and data system (LI-RADS), and MRI assessment of tumor response following locoregional therapy.

Metabolomic applications in hepatocellular carcinoma: toward the exploration of therapeutics and diagnosis through small molecules

Hepatocellular carcinoma (HCC), a complex public health issue that is the most common primary hepatic malignancy, remains the highest incidence in developing countries and is showing sustained growth across the developed world.

Hepatitis B and Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide, and its incidence has been increasing in the last decade largely in parallel to the incidence and duration of exposure to hepatitis B and C. The widespread implementation of hepatitis B vaccine, hepatitis B antivirals, and the introduction of direct antiviral therapies for hepatitis C virus may have a substantial impact in reducing the incidence of HCC. This report reviews the risk factors and underlying mechanisms associated with the development of HCC in hepatitis B, along with advances in the diagnosis, imaging, and management of HCC.

Clinical Impact of Gadoxetic Acid-Enhanced Magnetic Resonance Imaging on Hepatoma Management: A Prospective Study

BACKGROUND

For patients with hepatocellular carcinoma (HCC), gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) improved the diagnosis, migrated Barcelona Clinic Liver Cancer (BCLC) stage, and changed therapeutic decision in retrospective analysis.

AIM

This prospective study was to evaluate the clinical impact of EOB-MRI on HCC management.

METHODS

From September 2012 to February 2014, consecutive patients with suspicion of HCC in BCLC early stage by multidetector computed tomography or dynamic MRI with non-specific gadolinium, well liver function reserve, and admitted for resection evaluation were enrolled prospectively. Additional EOB-MRI was performed. The HCC diagnosis, BCLC staging, and treatment decision were obtained in a liver cancer conference. EOB-MRI impact on HCC management was analyzed.

RESULTS

One hundred and three patients including 68 with typical and 35 with atypical HCC nodules in dynamic imaging studies were enrolled. EOB-MRI characterized 3 (4.4 %) benign and 33 (94.3 %) HCC for patients with typical and atypical HCC nodules, respectively. For 90 HCC patients, additional EOB-MRI changed BCLC stage in 25 (27.8 %) and treatment decision in 17 (18.9 %) patients. There were 66 patients with 78 resected nodules including 65 HCCs, 4 intrahepatic cholangiocarcinomas, and 9 benign nodules. Dynamic study and EOB-MRI detected and characterized 69 and 77 nodules, respectively. The sensitivity and accuracy in HCC diagnosis were 98.5 and 85.7 % for EOB-MRI, which were better than those of dynamic study (p < 0.001).

CONCLUSIONS

Additional EOB-MRI improved HCC diagnosis in sensitivity, accuracy but not specificity. It changed BCLC staging and treatment decision in 27.8 and 18.9 % of early-stage HCC patients.

New advances in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the leading cause of deaths in cirrhotic patients and the third cause of cancer related deaths. Most HCC are associated with well known underlying risk factors, in fact, HCC arise in cirrhotic patients in up to 90% of cases, mainly due to chronic viral hepatitis and alcohol abuse. The worldwide prevention strategies are conducted to avoid the infection of new subjects and to minimize the risk of liver disease progression in infected patients. HCC is a condition which lends itself to surveillance as at-risk individuals can readily be identified. The American and European guidelines recommended implementation of surveillance programs with ultrasound every six months in patient at-risk for developing HCC. The diagnosis of HCC can be based on non-invasive criteria (only in cirrhotic patient) or pathology. Accurately staging patients is essential to oncology practice. The ideal tumour staging system in HCC needs to account for both tumour characteristics and liver function. Treatment allocation is based on several factors: Liver function, size and number of tumours, macrovascular invasion or extrahepatic spread. The recommendations in terms of selection for different treatment strategies must be based on evidence-based data. Resection, liver transplant and interventional radiology treatment are mainstays of HCC therapy and achieve the best outcomes in well-selected candidates. Chemoembolization is the most widely used treatment for unresectable HCC or progression after curative treatment. Finally, in patients with advanced HCC with preserved liver function, sorafenib is the only approved systemic drug that has demonstrated a survival benefit and is the standard of care in this group of patients.

Imaging of HCC-Current State of the Art

Early diagnosis of hepatocellular carcinoma (HCC) is crucial for optimizing treatment outcome. Ongoing advances are being made in imaging of HCC regarding detection, grading, staging, and also treatment monitoring. This review gives an overview of the current international guidelines for diagnosing HCC and their discrepancies as well as critically summarizes the role of magnetic resonance imaging (MRI) and computed tomography (CT) techniques for imaging in HCC. The diagnostic performance of MRI with nonspecific and hepatobililiary contrast agents and the role of functional imaging with diffusion-weighted imaging will be discussed. On the other hand, CT as a fast, cheap and easily accessible imaging modality plays a major role in the clinical routine work-up of HCC. Technical advances in CT, such as dual energy CT and volume perfusion CT, are currently being explored for improving detection, characterization and staging of HCC with promising results. Cone beam CT can provide a three-dimensional analysis of the liver with tumor and vessel characterization comparable to cross-sectional imaging so that this technique is gaining an increasing role in the peri-procedural imaging of HCC treated with interventional techniques.

Classifying CT/MR findings in patients with suspicion of hepatocellular carcinoma: Comparison of liver imaging reporting and data system and criteria-free Likert scale reporting models

PURPOSE

To compare the Liver Imaging Reporting and Data System (LI-RADS) and a criteria-free Likert scale (LS) reporting models for classifying computed tomography/magnetic resonance imaging (CT/MR) findings of suspicious hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Imaging data of 281 hepatic nodules in 203 patients were retrospectively included. Imaging characteristics including diameter, arterial hyperenhancement, washout, and capsule were reviewed independently by two groups of readers using LI-RADS and LS (range, score 1-5). LS is primarily based on the overall impression of image findings without using fixed criteria. Interreader agreement (IRA), intraclass agreement (ICA), and diagnostic performance were determined by Fleiss, Cohen's kappa (κ), and logistic regression, respectively.

RESULTS

There were 167 contrast-enhanced CT (CECT) versus 114 MR data. Overall, IRA was moderate (κ = 0.47, 0.52); IRA was moderate-to-good for arterial hyperenhancement, washout, and capsule (κ = 0.56-0.69); excellent for diameter and tumor embolus (κ = 0.99). Overall, ICA between LI-RADS and LS was moderate (κ = 0.44-0.50); ICA was good for scores 1-2 (κ = 0.71-0.90), moderate for scores 3 and 5 (κ = 0.41-0.52), but very poor for score 4 (κ = 0.11-0.19). LI-RADS produced significantly lower accuracy (78.6% vs. 87.2%) and sensitivity (72.1% vs. 92.8%), higher specificity (97.3% vs. 71.2%) and positive likelihood ratio (+LR: 26.32 vs. 3.23) in diagnosis of HCC. CECT produced relatively low IRA, ICA, and diagnostic ability against MR.

CONCLUSION

There were substantial variations in liver observations between LI-RADS and LS. Further study is needed to investigate ICA between CECT and MR.

Magnetic resonance imaging of the cirrhotic liver: An update

Noninvasive imaging has become the standard for hepatocellular carcinoma (HCC) diagnosis in cirrhotic livers. In this review paper, we go over the basics of MR imaging in cirrhotic livers and describe the imaging appearance of a spectrum of hepatic nodules marking the progression from regenerative nodules to low- and high-grade dysplastic nodules, and ultimately to HCCs. We detail and illustrate the typical imaging appearances of different types of HCC including focal, multi-focal, massive, diffuse/infiltrative, and intra-hepatic metastases; with emphasis on the diagnostic value of MR in imaging these lesions. We also shed some light on liver imaging reporting and data system, and the role of different magnetic resonance imaging (MRI) contrast agents and future MRI techniques including the use of advanced MR pulse sequences and utilization of hepatocyte-specific MRI contrast agents, and how they might contribute to improving the diagnostic performance of MRI in early stage HCC diagnosis.

2014 KLCSG-NCC Korea Practice Guidelines for the management of hepatocellular carcinoma: HCC diagnostic algorithm

Hepatocellular carcinoma (HCC) is the fifth most commonly occurring cancer in Korea and typically has a poor prognosis with a 5-year survival rate of only 28.6%. Therefore, it is of paramount importance to achieve the earliest possible diagnosis of HCC and to recommend the most up-to-date optimal treatment strategy in order to increase the survival rate of patients who develop this disease. After the establishment of the Korean Liver Cancer Study Group (KLCSG) and the National Cancer Center (NCC), Korea jointly produced for the first time the Clinical Practice Guidelines for HCC in 2003, revised them in 2009, and published the newest revision of the guidelines in 2014, including changes in the diagnostic criteria of HCC and incorporating the most recent medical advances over the past 5 years. In this review, we will address the noninvasive diagnostic criteria and diagnostic algorithm of HCC included in the newly established KLCSG-NCC guidelines in 2014, and review the differences in the criteria for a diagnosis of HCC between the KLCSG-NCC guidelines and the most recent imaging guidelines endorsed by the European Organisation for Research and Treatment of Cancer (EORTC), the Liver Imaging Reporting and Data System (LI-RADS), the Organ Procurement and Transplantation Network (OPTN) system, the Asian Pacific Association for the Study of the Liver (APASL) and the Japan Society of Hepatology (JSH).