Evidence Supporting LI-RADS Major Features for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review

Preoperative prediction of early recurrence in hepatocellular carcinoma using simultaneous multislice diffusion kurtosis imaging

OBJECTIVE

This study aimed to evaluate the value of simultaneous multislice diffusion kurtosis imaging (SMS-DKI) for predicting early recurrence (within 2 years) in hepatocellular carcinoma (HCC) and to develop a predictive model.

MATERIALS AND METHODS

This prospective study included 67 HCC patients who underwent SMS-DKI on a 3-T MRI between June 2021 and January 2023. Diffusion parameters, including the apparent diffusion coefficient (ADC), SMS-mean kurtosis (SMS-MK), and SMS-mean diffusivity (SMS-MD), along with radiological features, were analyzed. Logistic regression models were used to predict early recurrence, internally validated using 10-fold cross-validation, and assessed using AUC, calibration curves, and decision curve analysis (DCA).

RESULTS

Among 67 patients (58 males; mean age, 53.5 ± 9.9 years), 30 (44.8%) experienced early recurrence. The early recurrence had significantly lower ADC (1.12 vs 1.22 × 10-3 mm2/s) and SMS-MD (1.45 vs 1.70 × 10-3 mm2/s), and higher SMS-MK (0.91 vs 0.75). SMS-MK showed the highest AUC (0.90, 95% CI: 0.80-0.96). Multivariate analysis identified SMS-MK (OR = 3.43 [1.31-8.89]), tumor size (OR = 4.22 [1.58-7.76]), non-smooth tumor margin (OR = 2.68 [1.58-7.96]), and complete capsule (OR = 0.22 [0.02-0.79]) as independent predictors of early recurrence. Based on these four parameters, the final model achieved an AUC of 0.94 (95% CI: 0.88-1.00). Calibration curves and DCA confirmed clinical utility.

CONCLUSION

SMS-DKI enhances early recurrence prediction in HCC. The predictive model, incorporating SMS-MK, tumor size, and key radiological features, demonstrated good prognostic value.

KEY POINTS

Question Can SMS-DKI predict HCC early recurrence within 2 years post-surgery? Findings Higher SMS-MK, larger tumor size, non-smooth margins, and incomplete capsule predict HCC early recurrence (model AUC = 0.94). Clinical relevance Integrating preoperative SMS-DKI biomarkers (SMS-MK) with tumor size and capsule status stratifies early HCC recurrence risk, guiding surgical planning and postoperative management.

Combining CEUS and CT/MRI LI-RADS major imaging features: diagnostic accuracy for classification of indeterminate liver observations in patients at risk for HCC

PURPOSE

To determine the diagnostic accuracy of combining CEUS and CT/MRI LI-RADS major imaging features for the improved categorization of liver observations indeterminate on both CT/MRI and CEUS.

MATERIALS AND METHODS

A retrospective analysis using a database from a prospective study conducted at 11 centers in North America and Europe from 2018 to 2022 included a total of 109 participants at risk for HCC who had liver observations with indeterminate characterization (LR3, LR-4, and LR-M) on both CEUS and CT/MRI. The individual CEUS and CT/MRI LI-RADS major features were extracted from the original study and analyzed in various combinations. Reference standards included biopsy, explant histology, and follow-up CT/MRI. The diagnostic performance of the combinations of LI-RADS major features for definitive diagnosis of HCC was calculated. A reverse, stepwise logistical regression sub-analysis was also performed.

RESULTS

This study included 114 observations indeterminate on both CT/MRI and CEUS. These observations were categorized as LR-3 (n = 37), LR-4 (n = 41), and LR-M (n = 36) on CT/MRI and LR-3 (n = 48), LR-4 (n = 36), LR-M (n = 29), and LR-TIV (n = 1) on CEUS. Of them, 43.0% (49/114) were confirmed as HCC, 37.3% (43/114) non-malignant, and 19.3% (22/114) non-hepatocellular malignancies. The highest diagnostic accuracy among the combinations of imaging features was achieved in CT/MRI LR-3 observations, where the combination of CEUS arterial phase hyper-enhancement (APHE) + CT/MRI APHE had 96.7% specificity, 75.0% positive predictive value (PPV), and 86.5% accuracy for HCC.

CONCLUSION

The combination of LI-RADS major features on CT/MRI and CEUS showed higher specificity, PPV, and accuracy compared to individual modalities' assessments, particularly for CT/MRI LR-3 observations.

Enhancing hepatocellular carcinoma diagnosis in non-high-risk patients: a customized ChatGPT model integrating contrast-enhanced ultrasound

PURPOSE

This study aims to improve hepatocellular carcinoma (HCC) diagnostic accuracy in non-high-risk populations by utilizing GPTs that incorporate integrated risk coefficients, and to explore its feasibility.

MATERIAL AND METHODS

Between August 2016 and June 2019, patients with focal liver lesions (FLLs) in non-high-risk populations, confirmed by histopathology or clinical/imaging evidence, were retrospectively included. A logistic regression model was developed using baseline characteristics and contrast-enhanced ultrasound (CEUS) features to identify independent HCC risk factors. Three ChatGPT-based models were evaluated: ChatGPT 4o (a general-purpose model developed by OpenAI), BaseGPT (a customized model with HCC diagnostic knowledge), and RiskGPT (a further customized model integrating HCC knowledge and identified risk factors). Their intra-agreement and diagnostic performance were compared.

RESULTS

Logistic regression identified male, obesity, HBcAb or HBeAb positivity, elevated alpha-fetoprotein, and mild washout on CEUS as associated with HCC. RiskGPT achieved the highest area under a receiver operating characteristic curve (AUC) (0.89) and demonstrated superior accuracy (90.3%) in HCC identification; significantly outperforming both ChatGPT 4o (AUC 0.79, P = 0.002; accuracy 83.1%, P = 0.02) and BaseGPT (AUC 0.81, P = 0.008; accuracy 80.6%, P = 0.002). RiskGPT demonstrated superior sensitivity compared to ChatGPT 4o (85.5% vs. 66.3%) and outperformed BaseGPT in specificity (92.7% vs. 80.6%) and positive predictive value (85.5% vs. 67.7%) (all P < 0.001). Additionally, RiskGPT showed substantial intra-consistency in diagnosing FLLs, with a κ value of 0.78.

CONCLUSION

RiskGPT improves HCC diagnostic accuracy in non-high-risk patients by integrating clinical, imaging features, and risk coefficients, demonstrating significant diagnostic potential.

Benign pericholecystic regeneration mimicking hepatocellular carcinoma: a potential pitfall in imaging of cirrhosis

Several pseudolesions mimicking malignancy have been reported in cirrhotic and non-cirrhotic livers. Pericholecystic regeneration is among those pseudolesions and can occasionally mimic malignancy. Herein we present a case series comprised of 10 cirrhotic patients (majority due to alcoholic liver disease [ALD] or metabolic dysfunction associated steatotic liver disease [MASLD]) with pericholecystic observations initially categorized as highly suspicious for HCC (LR-4 and LR-5) due to their suspicious enhancement pattern which were later proven to be benign based on biopsy, transplantation or imaging criteria (stability of size and morphology on serial CT or MRI for > 2 years). These observations ranged 2-6.3 cm in size, were multiple in most patients and universally resulted in indentations of gallbladder. Arterial phase hyperenhancement and washout, features usually attributed to hepatocellular carcinoma (HCC), were seen in most patients. However, ancillary features of malignancy such as diffusion restriction, increased T2 signal, and hypointensity on hepatobiliary phase MRI were not present. Pericholecystic observations, in particular in the setting of ALD and MASLD, should be carefully assessed and possibility of pseudolesion should be considered especially when the observations are multiple, cause indentation of gallbladder, and lack ancillary features of malignancy.

Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study

BACKGROUND

Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while preserving noise texture, which may potentially improve hypervascular focal liver lesions.

PURPOSE

To assess the impact of DLIR on image quality (IQ) and detectability of simulated hypervascular hepatocellular carcinoma (HCC) in fast kV-switching dual-energy CT (DECT).

METHODS

An anthropomorphic phantom of a standard patient morphology (body mass index of 23 kg m-2) with customized liver, including mimickers of hypervascular lesions in both late arterial phase (AP) and portal venous phase (PVP) enhancement, was scanned on a DECT. Virtual monoenergetic images were reconstructed from raw data at four energy levels (40/50/60/70 keV) using filtered back-projection (FBP), adaptive statistical iterative reconstruction-V 50% and 100% (ASIRV-50 and ASIRV-100), DLIR low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The contrast between the lesion and the liver parenchyma, the noise magnitude, the average and peak frequencies (favg and fpeak) of the noise power spectrum (NPS) reflecting noise texture, and the task-based measure of the modulation transfer function (MTFtask) were measured to evaluate spatial resolution. A detectability index (d') was computed to model the detection of hypervascular lesions in both AP and PVP. Metrics were compared between reconstructions and between energy levels using a Friedman test with follow-up post-hoc multiple comparison.

RESULTS

Lesion-to-liver contrast significantly increased with decreasing energy level in both AP and PVP (p ≤ 0.042) but was not affected by reconstruction algorithm (p ≥ 0.57). Overall, noise magnitude increased with decreasing energy levels and was the lowest with ASIRV-100 at all energy levels in both AP and PVP (p ≤ 0.01) and significantly lower with DLIR-M and DLIR-H reconstructions compared to ASIRV-50 and DLIR-L (p < 0.001). For all reconstructions, noise texture within the liver tended to get smoother with decreasing energy; favg significantly shifted towards lower frequencies from 70 to 40 keV (p ≤ 0.01). Noise texture was the smoothest with ASIRV-100 (p < 0.001) while DLIR-L had the noise texture closer to the one of FBP. The spatial resolution was not significantly affected by the energy level, but it was degraded when increasing the level of ASIRV and DLIR. For all reconstructions, the detectability indices increased with decreasing energy levels and peaked at 40 and 50 keV in AP and PVP, respectively. In both AP and PVP, the highest d' values were observed with ASIRV-100 and DLIR-H, whatever the energy level studied (p ≤ 0.01) without statistical significance between those two reconstructions.

CONCLUSIONS

Compared to the routinely used level of iterative reconstruction, DLIR reduces noise without consequential noise texture modification, and may improve the detectability of hypervascular liver lesions while enabling the use of lower energy virtual monoenergetic images. The optimal energy level and DLIR level may depend on the lesion enhancement.

Phase 3 Validation of PAaM for Hepatocellular Carcinoma Risk Stratification in Cirrhosis

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) risk stratification is an urgent unmet need for cost-effective HCC screening and early detection in patients with cirrhosis to improve poor HCC prognosis.

METHODS

Molecular (prognostic liver secretome signature with α-fetoprotein) and clinical (aMAP [age, male sex, albumin-bilirubin, and platelets] score) variable-based scores were integrated into PAaM (prognostic liver secretome signature with α-fetoprotein plus age, male sex, albumin-bilirubin, and platelets), which was subsequently validated in 2 phase 3 biomarker validation studies: the statewide Texas HCC Consortium and nationwide HCC Early Detection Strategy prospective cohorts, following the prospective specimen collection, retrospective blinded evaluation design. The associations between baseline PAaM and incident HCC were assessed using Fine-Gray regression, with overall death and liver transplantation as competing events.

RESULTS

Of 2156 patients with cirrhosis in the Texas HCC Consortium, PAaM identified 404 (19%) high-risk, 903 (42%) intermediate-risk, and 849 (39%) low-risk patients with annual HCC incidence rates of 5.3%, 2.7%, and 0.6%, respectively. Compared with low-risk patients, high- and intermediate-risk groups had sub-distribution hazard ratios for incident HCC of 7.51 (95% CI, 4.42-12.8) and 4.20 (95% CI, 2.52-7.01), respectively. Of 1328 patients with cirrhosis in the HCC early detection strategy, PAaM identified 201 high-risk (15%), 540 intermediate-risk (41%), and 587 low-risk (44%) patients, with annual HCC incidence rates of 6.2%, 1.8%, and 0.8%, respectively. High- and intermediate-risk groups were associated with sub-distribution hazard ratios for incident HCC of 6.54 (95% CI, 3.85-11.1) and 1.77 (95% CI, 1.02-3.08), respectively. Subgroup analysis showed robust risk stratification across HCC etiologies, including metabolic dysfunction-associated steatotic liver disease and cured hepatitis C infection.

CONCLUSIONS

PAaM enables accurate HCC risk stratification in patients with cirrhosis from contemporary etiologies.

Equilibrium phase images of the liver using a contrast-enhancement boost instead of the portal vein phase

BACKGROUND

Three-phase dynamic computed tomography imaging is particularly useful in the liver region. However, dynamic imaging with contrast media has the disadvantage of increased radiation exposure due to multiple imaging sessions. We hypothesized that the contrast enhancement boost (CE-boost) technique could be used to enhance the contrast in equilibrium phase (EP) images and produce enhancement similar to that of portal vein phase (PVP) images, and if this is possible, EP imaging could play the same role as PVP imaging. We also speculated that this might allow the conversion of three-phase dynamic imaging to biphasic dynamic imaging, reducing patients’ radiation exposure.

AIM

To determine if the CE-boost of EP, CE-boost (EP) is useful compared to a conventional image.

METHODS

We retrospectively analyzed the cases of 52 patients who were diagnosed with liver cancer between January 2016 and October 2022 at our institution. From these computed tomography images, CE-boost images were generated from the EP and plane images. We compared the PVP, EP, and CE-boost (EP) for blood vessels and hepatic parenchyma based on the contrast-to-noise ratio (CNR), signal-to-noise ratio, and figure-of-merit (FOM). Visual assessments were also performed for vessel visualization, lesion conspicuity, and image noise.

RESULTS

The CE-boost (EP) images showed significant superiority compared to the PVP images in the CNR, signal-to-noise ratio, and FOM except regarding the hepatic parenchyma. No significant differences were detected in CNR or FOM comparisons within the hepatic parenchyma (P = 0.62, 0.67). The comparison of the EP and CE-boost (EP) images consistently favored CE-boost (EP). Regarding the visual assessment, the CE-boost (EP) images were significantly superior to the PVP images in lesion conspicuity, and the PVP in image noise. The CE-boost (EP) images were significantly better than the EP images in the vessel visualization of segmental branches of the portal vein and lesion conspicuity, and the EP in image noise.

CONCLUSION

The image quality of CE-boost (EP) images was comparable or superior to that of conventional PVP and EP. CE-boost (EP) images might provide information comparable to the conventional PVP.

Current State of Evidence for Use of MRI in LI-RADS

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) is the preeminent framework for classification and risk stratification of liver observations on imaging in patients at high risk for hepatocellular carcinoma. In this review, the pathogenesis of hepatocellular carcinoma and the use of MRI in LI-RADS is discussed, including specifically the LI-RADS diagnostic algorithm, its components, and its reproducibility with reference to the latest supporting evidence. The LI-RADS treatment response algorithms are reviewed, including the more recent radiation treatment response algorithm. The application of artificial intelligence, points of controversy, LI-RADS relative to other liver imaging systems, and possible future directions are explored. After reading this article, the reader will have an understanding of the foundation and application of LI-RADS as well as possible future directions.

The value of contrast-enhanced ultrasound fusion imaging in percutaneous liver biopsy for liver lesions invisible on conventional B-mode ultrasound

Background

Ultrasound (US) is the most commonly used imaging method for guiding percutaneous liver biopsies. For lesions that are invisible on B-mode ultrasound (BMUS), contrast-enhanced ultrasound (CEUS) improves the contrast between neoplasms and liver parenchyma, while emerging fusion imaging techniques can enhance the localization of lesions. Therefore, our study aimed to assess the value of CEUS fusion imaging in detecting and guiding percutaneous liver biopsy for lesions invisible on BMUS.

Methods

Patients with focal liver lesions (FLLs) identified on computed tomography (CT) or magnetic resonance imaging (MRI) but not visible on BMUS were retrospectively included at Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University from September 2019 to December 2023. All patients underwent BMUS fusion imaging and CEUS fusion imaging. We evaluated the lesion visibility, detection rate of BMUS fusion, and CEUS fusion before liver biopsy and the rate of technical success and diagnostic success after CEUS fusion-guided biopsy.

Results

This study included 70 FLLs from 61 patients. The mean visibility score of the 70 lesions on BMUS was 1.51±0.52. BMUS fusion detected 31 (31/70, 44.3%) lesions, yielding a mean visibility score of 1.81±1.03. CEUS fusion detected 63 (63/70, 90%) lesions, and achieved a mean visibility score of 2.90±0.30, which was significantly higher than that of BMUS fusion. Subgroup analysis corroborated the superiority of CEUS fusion in detecting infiltrative lesions and lesions <2 cm. Finally, CEUS fusion-guided liver biopsy achieved a technical success rate of 98.4% and a diagnostic success rate of 82.0% (50/61).

Conclusions

Compared with conventional BMUS or BMUS fusion imaging, the combination of CEUS and fusion imaging enabled an increase in the detection rate and lesion visibility of BMUS-invisible lesions, particularly for infiltrative masses and small-sized lesions, thereby increasing operators' confidence and success rate during the percutaneous liver biopsy.

APT imaging of hepatocellular carcinoma signals an effective therapeutic response in advance of tumor shrinkage

Aim: The aim of this study was to assess the utility of weighted amide proton transfer (APTw) MRI in three different rodent models of hepatocellular carcinoma (HCC).Methods: APTw MRI was evaluated in models of diethylnitrosamine (DEN) induced HCC, N1S1 syngeneic orthotopic xenograft and human HepG2 ectopic xenograft.Results: All models of HCC showed a higher APTw signal over the surrounding normal tissues. In the DEN model, the APTw signal could differentiate HCC lesions from benign nodules. Intra-arterial administration of low-density lipoprotein docosahexaenoic acid (LDL-DHA) nanoparticles to N1S1 xenografts rapidly lowered the tumor APTw signal within 72 h. Direct injections of LDL-DHA nanoparticles into HepG2 xenografts also showed similar therapeutic responses.Conclusion: We have demonstrated the utility of APTw imaging in the diagnostic/therapeutic management of HCC.

Do Risk Factors for HCC Impact the Association of CT/MRI LIRADS Major Features With HCC? An Individual Participant Data Meta-Analysis

Background: Guidelines suggest the Liver Imaging Reporting and Data System (LI-RADS) may not be applicable for some populations at risk for hepatocellular carcinoma (HCC). However, data assessing the association of HCC risk factors with LI-RADS major features are lacking. Purpose: To evaluate whether the association between HCC risk factors and each CT/MRI LI-RADS major feature differs among individuals at-risk for HCC. Methods: Databases (MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Scopus) were searched from 2014 to 2022. Individual participant data (IPD) were extracted from studies evaluating HCC diagnosis using CT/MRI LI-RADS and reporting HCC risk factors. IPD from studies were pooled and modelled with one-stage meta-regressions. Interactions were assessed between major features and HCC risk factors, including age, sex, cirrhosis, chronic hepatitis B virus (HBV), and study location. A mixed effects model that included the major features, as well as separate models that included interactions between each risk factor and each major feature, were fit. Differences in interactions across levels of each risk factor were calculated using adjusted odds-ratios (ORs), 95% confidence-intervals (CI), and z-tests. Risk of bias was assessed using QUADAS-2. (Protocol: https://osf.io/tdv7j/). Results: Across 23 studies (2958 patients and 3553 observations), the associations between LI-RADS major features and HCC were consistent across several HCC risk factors (P-value range: .09-.99). A sensitivity analysis among the 4 studies with a low risk of bias did not differ from the primary analysis. Conclusion: The association between CT/MRI LI-RADS major features and HCC risk factors do not significantly differ in individuals at-risk for HCC. These findings suggest that CT/MR LI-RADS should be applied to all patients considered at risk by LI-RADS without modification or exclusions, regardless of the presence or absence of the risk factors evaluated in this study.

Imaging Prognostic Biomarkers in Hepatocellular Carcinoma: A Comprehensive Review

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide with its incidence on the rise globally. This paper provides a comprehensive review of prognostic imaging markers in HCC, emphasizing their role in risk stratification and clinical decision-making. We explore quantitative and qualitative criteria derived from imaging studies, such as computed tomography (CT) and magnetic resonance imaging (MRI), which can offer valuable insights into the biological behavior of the tumor. While many of these markers are not yet widely integrated into current clinical guidelines, they represent a promising future direction for approaching this highly heterogeneous cancer. However, standardization and validation of these markers remain important challenges. We conclude by emphasizing the importance of ongoing research to enhance clinical practices and improve outcomes for patients with HCC.

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.

SEOM-GEMCAD-TTD clinical guidelines for the management of hepatocarcinoma patients (2023)

Hepatocellular carcinoma (HCC) is the most common primary malignancy in the liver and is the third cause of cancer-related death worldwide. Surveillance with abdominal ultrasound should be offered to individuals at high risk for developing HCC. Accurate diagnosis, staging, and liver function are crucial when determining the optimal therapeutic approach. The BCLC staging system is widely endorsed in Western countries. Managing this pathology requires a multidisciplinary, personalized approach, generally with a multimodal strategy. Surgery remains the only curative option, albeit local and systemic therapy may also increase survival when surgery is not suitable. In advanced disease, systemic treatment should be offered to patients with ECOG/PS 0-1 and Child-Pugh class A.

Imaging of Cirrhosis and Hepatocellular Carcinoma: Current Evidence

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide. Early detection of HCC is a key factor in enabling curative therapies and improving overall survival. Worldwide, several guidelines are available for surveillance of at-risk populations and diagnosis of HCC. This article provides a current comprehensive update on screening and diagnosis of HCC.

Can Patient Characteristics and Pre-Treatment MRI Features Predict Survival After Stereotactic Ablative Radiotherapy (SABR) Treatment in Hepatocellular Carcinoma (HCC): Preliminary Assessment

BACKGROUND

The study purpose was to develop a machine learning (ML)-based predictive model for event-free survival (EFS) in patients with hepatocellular carcinoma (HCC) undergoing stereotactic ablative radiotherapy (SABR).

METHODS

Patients receiving SABR for HCC at a single institution, between 2017 and 2020, were included in the study. They were split into training and test (85%:15%) cohorts. Events of interest were HCC recurrence or death. Three ML models were trained, the features were selected, and the hyperparameters were tuned. The performance was measured using Harrell's C index with the best-performing model being tested on the unseen cohort.

RESULTS

Overall, 41 patients were included (training = 34, test = 7) and 64 lesions were analysed (training = 50, test = 14), resulting in 30 events (60% rate) in the training set (death = 6, recurrence = 24) and 8 events (57% rate) in the test set (death = 5, recurrence = 3). A Cox regression model, using age at treatment, albumin, and intra-lesional fat identified through MRI as variables, had the best performance with a mean training score of 0.78 (standard deviation (SD) 0.02), a mean validation of 0.78 (SD 0.18), and a test score of 0.94.

CONCLUSIONS

Predicting the outcomes in patients with HCC, following SABR, using a novel model is feasible and warrants further evaluation.

Advances in Understanding Hepatocellular Carcinoma Vasculature: Implications for Diagnosis, Prognostication, and Treatment

Hepatocellular carcinoma (HCC) progresses through multiple stages of hepatocarcinogenesis, with each stage characterized by specific changes in vascular supply, drainage, and microvascular structure. These vascular changes significantly influence the imaging findings of HCC, enabling non-invasive diagnosis. Vascular changes in HCC are closely related to aggressive histological characteristics and treatment responses. Venous drainage from the tumor toward the portal vein in the surrounding liver facilitates vascular invasion, and the unique microvascular pattern of vessels that encapsulate the tumor cluster (known as a VETC pattern) promotes vascular invasion and metastasis. Systemic treatments for HCC, which are increasingly being used, primarily target angiogenesis and immune checkpoint pathways, which are closely intertwined. By understanding the complex relationship between histopathological vascular changes in hepatocarcinogenesis and their implications for imaging findings, radiologists can enhance the accuracy of imaging diagnosis and improve the prediction of prognosis and treatment response. This, in turn, will ultimately lead to better patient care.

British Society of Gastroenterology guidelines for the management of hepatocellular carcinoma in adults

Deaths from the majority of cancers are falling globally, but the incidence and mortality from hepatocellular carcinoma (HCC) is increasing in the United Kingdom and in other Western countries. HCC is a highly fatal cancer, often diagnosed late, with an incidence to mortality ratio that approaches 1. Despite there being a number of treatment options, including those associated with good medium to long-term survival, 5-year survival from HCC in the UK remains below 20%. Sex, ethnicity and deprivation are important demographics for the incidence of, and/or survival from, HCC. These clinical practice guidelines will provide evidence-based advice for the assessment and management of patients with HCC. The clinical and scientific data underpinning the recommendations we make are summarised in detail. Much of the content will have broad relevance, but the treatment algorithms are based on therapies that are available in the UK and have regulatory approval for use in the National Health Service.

Apelin and the gut microbiome: Potential interaction in human MASLD

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading cause of chronic liver disease with increasing numbers worldwide. Adipokines like apelin (APLN) can act as key players in the complex pathophysiology of MASLD.

AIMS

Investigating the role of APLN in MASLD.

METHODS

Fecal and blood samples were collected in a MASLD cohort and healthy controls (HC). MASLD patients with liver fibrosis and MASLD-associated hepatocellular carcinoma (HCC) were included into the study. Systemic concentration of Apelin, Apelin receptor (APLNR) and circulating cytokines were measured in serum samples.

RESULTS

Apelin concentration correlated with the Fib-4 score and was elevated in MASLD patients (mild fibrosis, mF (Fib-4 <3.25) and severe fibrosis, sF (Fib-4 >3.25)) as well as in MASLD-associated HCC patients compared to HC. In accordance APLNR and circulating cytokines were also elevated in mF and sF. In contrast apelin levels were negatively associated with liver survival at three and five years. Changes in taxa composition at phylum level showed an increase of Enterobactericae, Prevotellaceae and Lactobacillaceae in patients with sF compared to mF. We could also observe an association between apelin concentrations and bacterial lineages (phyla).

CONCLUSIONS

Circulating apelin is associated with liver fibrosis and HCC. In addition, there might exist an interaction between systemic apelin and the gut microbiome.

Prediction of histologic grade of hepatocellular carcinoma using dual-layer spectral-detector computed tomography (CT): comparison of two region of interest plotting methods

Background

Multi-parameter imaging technology, which is based on substance separation, helps to predict the pathological grade of tumors. When using dual-layer spectral-detector computed tomography (DLCT) to quantify tumor properties, different methods of placing regions of interest (ROIs) directly impact the measurement of parameters, thus affecting the clinical diagnosis of lesions. Consequently, in this study, we aimed to compare the performance of 2 different ROI plotting methods on DLCT in differentiating the histologic grade of hepatocellular carcinoma (HCC).

Methods

This retrospective study included 48 consecutive patients with pathologically confirmed HCC, who underwent DLCT from May 2022 to March 2023. The attenuation value of conventional computed tomography (CT), electron density relative to water (EDW), normalized effective atomic number (NZeff), and normalized iodine density (NID) were measured by 2 radiologists using the conventional planar sketching (PS) method and the volumetric analysis method, respectively. The differences in parameters between the arterial phase (AP) and venous phase (VP) were calculated for each parameter (∆CT, ∆EDW, ∆NZeff, ∆NID). We used 2-sample t-test or Mann-Whitney U test was used to compare the differences in parameters between the 2 methods. Spearman correlation analysis was used to determine the correlation between each parameter and histologic grade. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance.

Results

The mean values for the spectral quantitative parameters (CTAP, NZeffAP, NIDAP) and the difference between the arterial phase and venous phase (AP-VP) of parameters (∆CT, ∆EDW, ∆NZeff) measured using the volumetric analysis method were significantly lower than those of the PS method (P<0.05). For the ∆NZeff, the volumetric analysis method achieved the highest area under the curve (AUC) with a value of 0.918 [95% confidence interval (CI): 0.847-0.988], followed by the PS method (AUC =0.853, 95% CI: 0.743-0.963).

Conclusions

The spectral parameters of DLCT provide a novel quantitative method for evaluating histological differentiation in patients with HCC, which is worthy of clinical recommendation. Different ROI plotting methods significantly impact the measurement of spectral parameters. Therefore, the whole tumor region should be covered in the parameter measurement of HCC lesions as much as feasible, which is more helpful in predicting the histological grading of tumors before treatment.

Advances in gross tumor target volume determination in radiotherapy for patients with hepatocellular carcinoma

Hepatocellular Carcinoma (HCC) is one of the most common malignant neoplasms. With the advancement of technology, the precision of radiotherapy (RT) for HCC has considerably increased, and it is an indispensable modality in the comprehensive management of HCC. Some RT techniques increase the radiation dose to HCC, which decreases the radiation dose delivered to the surrounding normal liver tissue. This approach significantly improves the efficacy of HCC treatment and reduces the incidence of Radiation-induced Liver Disease (RILD). Clear imaging and precise determination of the Gross Target Volume (GTV) are prerequisites of precise RT of HCC. The main hindrances in determining the HCC GTV include indistinct tumor boundaries on imaging and the impact on respiratory motion. The integration of multimodal imaging, four-dimensional imaging, and artificial intelligence (AI) techniques can help overcome challenges for HCC GTV. In this article, the advancements in medical imaging and precise determination for HCC GTV have been reviewed, providing a framework for the precise RT of HCC.

Response Evaluation Criteria in Gastrointestinal and Abdominal Cancers: Which to Use and How to Measure

As the management of gastrointestinal malignancy has evolved, tumor response assessment has expanded from size-based assessments to those that include tumor enhancement, in addition to functional data such as those derived from PET and diffusion-weighted imaging. Accurate interpretation of tumor response therefore requires knowledge of imaging modalities used in gastrointestinal malignancy, anticancer therapies, and tumor biology. Targeted therapies such as immunotherapy pose additional considerations due to unique imaging response patterns and drug toxicity; as a consequence, immunotherapy response criteria have been developed. Some gastrointestinal malignancies require assessment with tumor-specific criteria when assessing response, often to guide clinical management (such as watchful waiting in rectal cancer or suitability for surgery in pancreatic cancer). Moreover, anatomic measurements can underestimate therapeutic response when applied to molecular-targeted therapies or locoregional therapies in hypervascular malignancies such as hepatocellular carcinoma. In these cases, responding tumors may exhibit morphologic changes including cystic degeneration, necrosis, and hemorrhage, often without significant reduction in size. Awareness of pitfalls when interpreting gastrointestinal tumor response is required to correctly interpret response assessment imaging and guide appropriate oncologic management. Data-driven image analyses such as radiomics have been investigated in a variety of gastrointestinal tumors, such as identifying those more likely to respond to therapy or recur, with the aim of delivering precision medicine. Multimedia-enhanced radiology reports can facilitate communication of gastrointestinal tumor response by automatically embedding response categories, key data, and representative images. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

A Modified Targetoid Feature Emphasizing Thin-Rim APHE to Improve the Diagnostic Performance of LI-RADS for Malignant Hepatic Tumors

Objective

To identify imaging features that help distinguish between HCCs and non-HCC malignancies assigned to LI-RADS M (LR-M) and evaluate the diagnostic performance of a LI-RADS with targetoid criteria using thin-rim arterial phase hyperenhancement (APHE).

Materials and Methods

This retrospective study included 381 patients (387 observations) at high-risk for HCC who underwent enhanced-MRI before surgery. Three radiologists reviewed images for LI-RADS categorization of hepatic observations. Univariate and multivariate analysis was conducted to determine reliable features to differentiate between HCC and non-HCC malignancies among the LR-M lesions. The thin-rim (<30%) APHE was defined based on the thickest thickness of rim APHE compared with the tumor radius, and a modified LI-RADS emphasizing thin-rim APHE as a specific feature of LR-M was established. We compared the diagnostic performance of modified LR-M and LI-RADS 5 (LR-5) with the conventional one.

Results

Thin-rim APHE and targetoid diffusion-weighted imaging (DWI) were found as independent predictive factors of non-HCC malignancies, while enhancing capsule, thick-rim APHE and peripheral washout were noted as independent variables significantly associated with HCC of LR-M (P<0.05). The noticeable diagnostic performance of thin-rim APHE in distinguishing non-HCC malignancies from HCCs using the ROC curve. Emphasizing thin-rim APHE on targetoid features, the modified LR-M revealed significantly superior specificity and accuracy (89.4% vs 81.1%, P=0.004; and 87.9% vs 82.2%, P=0.027, respectively) while maintaining high sensitivity (82.2% vs 86.0%; P=0.529) compared with the LR-M. Meanwhile, the modified LR-5 achieved greater sensitivity and accuracy (88.6% vs 79.7%, P=0.004; and 85.8% vs 80.1%, P=0.036, respectively) for diagnosing HCC, without compromising specificity (78.3% vs.81.1%; P=0.608) compared with the LR-5.

Conclusion

Thin-rim APHE may be the specific imaging feature for differentiating non-HCC malignancies from HCCs within LR-M. The modified targetoid criteria emphasizing thin-rim APHE can improve the diagnostic performance of LI-RADS for hepatic malignancies.

ESR Essentials: diagnosis of hepatocellular carcinoma-practice recommendations by ESGAR

Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy and a leading cause of cancer related death worldwide. Current guidelines for the noninvasive diagnosis of HCC are provided by the European Association for the Study of the Liver (EASL), the American Association for the Study of Liver Diseases (AASLD) which endorsed the Liver Imaging Reporting and Data System (LI-RADS) algorithm, the Korean Liver Cancer Association-National Cancer Center (KLCA-NCC), and the Asian-Pacific Association for the Study of the Liver (APASL). These allow the diagnosis of HCC in high-risk patients in the presence of typical imaging features on contrast-enhanced CT, MRI, or contrast-enhanced ultrasound. Size, non-rim arterial phase hyperenhancement, non-peripheral washout, enhancing capsule, and growth are major imaging features and they should be combined for the diagnosis of HCC. This article provides concise and relevant practice recommendations aimed at general radiologist audience, summarizing the best practice and informing on the essential imaging criteria for the diagnosis of HCC, while also discussing the high-risk population criteria, imaging modalities, and imaging features according to the current guidelines. KEY POINTS: • Noninvasive diagnosis of hepatocellular carcinoma (HCC) can be provided only in patients at high risk. • Contrast-enhanced CT or MRI are the first-line imaging exams for the diagnosis of HCC. • Major imaging features should be combined to provide the diagnosis of definitive HCC.

Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning

Objective. Precise hepatocellular carcinoma (HCC) detection is crucial for clinical management. While studies focus on computed tomography-based automatic algorithms, there is a rareness of research on automatic detection based on dynamic contrast enhanced (DCE) magnetic resonance imaging. This study is to develop an automatic detection and segmentation deep learning model for HCC using DCE.Approach: DCE images acquired from 2016 to 2021 were retrospectively collected. Then, 382 patients (301 male; 81 female) with 466 lesions pathologically confirmed were included and divided into an 80% training-validation set and a 20% independent test set. For external validation, 51 patients (42 male; 9 female) in another hospital from 2018 to 2021 were included. The U-net architecture was modified to accommodate multi-phasic DCE input. The model was trained with the training-validation set using five-fold cross-validation, and furtherly evaluated with the independent test set using comprehensive metrics for segmentation and detection performance. The proposed automatic segmentation model consisted of five main steps: phase registration, automatic liver region extraction using a pre-trained model, automatic HCC lesion segmentation using the multi-phasic deep learning model, ensemble of five-fold predictions, and post-processing using connected component analysis to enhance the performance to refine predictions and eliminate false positives.Main results. The proposed model achieved a mean dice similarity coefficient (DSC) of 0.81 ± 0.11, a sensitivity of 94.41 ± 15.50%, a precision of 94.19 ± 17.32%, and 0.14 ± 0.48 false positive lesions per patient in the independent test set. The model detected 88% (80/91) HCC lesions in the condition of DSC > 0.5, and the DSC per tumor was 0.80 ± 0.13. In the external set, the model detected 92% (58/62) lesions with 0.12 ± 0.33 false positives per patient, and the DSC per tumor was 0.75 ± 0.10.Significance.This study developed an automatic detection and segmentation deep learning model for HCC using DCE, which yielded promising post-processed results in accurately identifying and delineating HCC lesions.

Management of hepatocellular carcinoma recurrence after liver surgery and thermal ablations: state of the art and future perspectives

Despite the improvements in surgical and medical therapy for hepatocellular carcinoma (HCC), recurrence still represents a major issue. Up to 70% of patients can experience HCC recurrence after liver resection (LR), as well as 20% of them even after liver transplantation (LT). The patterns of recurrence are different according to both the time and the location. Similarly, the risk factors and the management can change not only according to these patterns, but also according to the underlying liver condition and to the first treatment performed. Deep knowledge of such correlation is fundamental, since prevention and effective management of recurrence are undoubtedly the most important strategies to improve the outcomes of HCC treatment. Without adjuvant therapy, maintaining very close monitoring during the first 2 years in order to diagnose curable recurrence and continue this monitoring beyond 5 years because late recurrences exist, remains our only possibility today. Surgery represents the cornerstone treatment for HCC, including both LT and LR. However, new interesting therapeutic opportunities are coming from immunotherapy that has shown encouraging results also in the adjuvant setting. In such a complex and evolutionary scenario, the aim of this review is to summarize current strategies for the management of HCC recurrence, focusing on the different possible scenarios, as well as on future perspectives.

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

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

CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma

BACKGROUND & AIMS

The present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming to establish an automatic classification model.

METHODS

We recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study. GTV and normal liver tissue samples were manually segmented into regions of interest and randomly divided into five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models were constructed via logistic regression, support vector machine (SVM), random forest, Xgboost, and Adaboost algorithms. The diagnostic efficacy, discrimination, and calibration of algorithms were verified using area under the receiver operating characteristic curve (AUC) analyses and calibration plot comparison.

RESULTS

Seven screened radiomics features excelled at distinguishing the gross tumor area. The Xgboost machine learning algorithm had the best discrimination and comprehensive diagnostic performance with an AUC of 0.9975 [95% confidence interval (CI): 0.9973-0.9978] and mean MCC of 0.9369. SVM had the second best discrimination and diagnostic performance with an AUC of 0.9846 (95% CI: 0.9835- 0.9857), mean Matthews correlation coefficient (MCC)of 0.9105, and a better calibration. All other algorithms showed an excellent ability to distinguish between gross tumor area and normal liver tissue (mean AUC 0.9825, 0.9861,0.9727,0.9644 for Adaboost, random forest, logistic regression, naivem Bayes algorithm respectively).

CONCLUSION

CT radiomics based on machine learning algorithms can accurately classify GTV and normal liver tissue, while the Xgboost and SVM algorithms served as the best complementary algorithms.

Prediction of Non-Transplantable Recurrence After Liver Resection for Solitary Hepatocellular Carcinoma

Purpose

Using a combination model of preoperative imaging and clinical factors to predict non-transplantable recurrence (NTR) after liver resection and assist solitary hepatocellular carcinoma (HCC) patients in the selection of early treatment options.

Patients and Methods

A retrospective analysis was conducted on 253 solitary HCC patients who underwent radical resection and had preoperative MRI. NTR patients were defined as those exceeding the University of California, San Francisco (UCSF) criteria at the time of recurrence. Cox regression analysis was employed to identify preoperative factors associated with NTR based on clinical and tumor imaging characteristics. A risk scoring model (NTRScore) was developed and validated.

Results

Among the 253 patients, 86 (33.9%) experienced recurrence, and among those with recurrence, 34 patients (39.5%) developed NTR. In multivariate analysis, factors associated with NTR included alpha-fetoprotein (AFP) [>10 ng/mL] [HR: 3.42, 95% confidence interval (CI): 1.54-7.63, P: 0.003], arterial phase hyperenhancement (APHE) [HR: 2.23, 95% CI: 1.03-4.81, P: 0.041], washout[HR: 0.35, 95% CI: 0.15-0.84, P: 0.019], and capsule [HR: 0.44, 95% CI: 0.22-0.88, P: 0.021]. The β-coefficients of these variables were utilized to develop the weighted NTRScore(c-index 0.72, 95% CI: 0.65-0.79). The NTR occurrence increased across the three categories (low: 5.6%, medium: 13.6%, high: 35.1%, p < 0.001), and the Kaplan-Meier curves of recurrence-free survival(RFS) and overall survival(OS) show significant differences (p = 0.004 and p<0.001). Furthermore, the higher NTR categories may be associated with an increased risk of extrahepatic recurrence.

Conclusion

The NTRScore demonstrated strong discriminatory ability and may serve as a clinically useful tool to assist in risk stratification and potential to guide treatment and optimal surveillance for patients of solitary hepatocellular carcinoma within UCSF criteria.

A multi-center diagnostic system for intrahepatic mass-forming cholangiocarcinoma based on preoperative MRI and clinical features

OBJECTIVES

To establish a non-invasive diagnostic system for intrahepatic mass-forming cholangiocarcinoma (IMCC) via decision tree analysis.

METHODS

Totally 1008 patients with 504 pathologically confirmed IMCCs and proportional hepatocellular carcinomas (HCC) and combined hepatocellular cholangiocarcinomas (cHCC-CC) from multi-centers were retrospectively included (internal cohort n = 700, external cohort n = 308). Univariate and multivariate logistic regression analyses were applied to evaluate the independent clinical and MRI predictors for IMCC, and the selected features were used to develop a decision tree-based diagnostic system. Diagnostic efficacy of the established system was calculated by the receiver operating characteristic curve analysis in the internal training-testing and external validation cohorts, and also in small lesions  ≤ 3 cm.

RESULTS

Multivariate analysis revealed that female, no chronic liver disease or cirrhosis, elevated carbohydrate antigen 19-9 (CA19-9) level, normal alpha-fetoprotein (AFP) level, lobulated tumor shape, progressive or persistent enhancement pattern, no enhancing tumor capsule, targetoid appearance, and liver surface retraction were independent characteristics favoring the diagnosis of IMCC over HCC or cHCC-CC (odds ratio = 3.273-25.00, p < 0.001 to p = 0.021). Among which enhancement pattern had the highest weight of 0.816. The diagnostic system incorporating significant characteristics above showed excellent performance in the internal training (area under the curve (AUC) 0.971), internal testing (AUC 0.956), and external validation (AUC 0.945) cohorts, as well as in small lesions  ≤ 3 cm (AUC 0.956).

CONCLUSIONS

In consideration of the great generalizability and clinical efficacy in multi-centers, the proposed diagnostic system may serve as a non-invasive, reliable, and easy-to-operate tool in IMCC diagnosis, providing an efficient approach to discriminate IMCC from other HCC-containing primary liver cancers.

CLINICAL RELEVANCE STATEMENT

This study established a non-invasive, easy-to-operate, and explainable decision tree-based diagnostic system for intrahepatic mass-forming cholangiocarcinoma, which may provide essential information for clinical decision-making.

KEY POINTS

• Distinguishing intrahepatic mass-forming cholangiocarcinoma (IMCC) from other primary liver cancers is important for both treatment planning and outcome prediction. • The MRI-based diagnostic system showed great performance with satisfying generalization ability in the diagnosis and discrimination of IMCC. • The diagnostic system may serve as a non-invasive, easy-to-operate, and explainable tool in the diagnosis and risk stratification for IMCC.

Aborted living-donor liver transplantation in the real-world setting, lessons from 13 937 cases of Vanguard Multi-center Study of International Living Donor Liver Transplantation Group

There are exceedingly uncommon but clearly defined situations where intraoperative abortions are inevitable in living-donor liver transplantation (LDLT). This study aimed to summarize the cases of aborted LDLT and propose a strategy to prevent abortion or minimize donor damage from both recipient and donor sides. We collected data from a total of 43 cases of aborted LDLT out of 13 937 cases from 7 high-volume hospitals in the Vanguard Multi-center Study of the International Living Donor Liver Transplantation Group and reviewed it retrospectively. Of the 43 cases, there were 24 recipient-related abortion cases and 19 donor-related cases. Recipient-related abortions included pulmonary hypertension (n = 8), hemodynamic instability (n = 6), advanced hepatocellular carcinoma (n = 5), bowel necrosis (n = 4), and severe adhesion (n = 1). Donor-related abortions included graft steatosis (n = 7), graft fibrosis (n = 5), primary biliary cholangitis (n = 3), anaphylactic shock (n = 2), and hemodynamic instability (n = 2). Total incidence of aborted LDLT was 0.31%, and there was no remarkable difference between the centers. A strategy to minimize additional donor damage by delaying the donor's laparotomy or trying to open the recipient's abdomen with a small incision should be effective in preventing some causes of aborted LDLT, such as pulmonary hypertension, advanced cancer, and severe adhesions.

Establishment and Validation of the LI-RADS Morphologic Type II Hepatocellular Carcinoma Early Recurrence Risk Scoring System

BACKGROUND

Tumor morphology links to early recurrence of hepatocellular carcinoma. Controversy exists regarding the recurrence risk of Liver Imaging Reporting and Data System morphologic Type II hepatocellular carcinoma. This study aims to explore risk factors for early recurrence of Type II hepatocellular carcinoma.

METHODS

Retrospective analysis of hepatocellular carcinoma patients who underwent curative resection and preoperative contrast-enhanced MRI from June 2016 to June 2020. Our patients formed the development set, and hepatocellular carcinoma patients from the TCIA database served as validation. Univariable and multivariable Cox regression identified independent risk factors for early recurrence. A risk scoring system was established for risk stratification, and an early recurrence prediction model was developed and validated.

RESULTS

95 Type II hepatocellular carcinoma patients were in the development set, and 29 cases were in the validation set. Early recurrence rates were 33.7% and 37.9%, respectively. Multivariate analysis revealed age, histological grade, AFP, and intratumoral hemorrhage as independent risk factors for early recurrence. The model's diagnostic performance for early recurrence was AUC = 0.817 in the development set. A scoring system classified patients into low-risk (scores ≤ 3) and high-risk (scores > 3) groups. The high-risk group had significantly lower recurrence-free survival (40.0% vs 73.2%, P = 0.001), consistent with the validation set (25.0% vs 73.3%, P = 0.028).

CONCLUSIONS

The risk scoring system demonstrated excellent discrimination and predictive ability, aiding clinicians in assessing early recurrence risk and identifying high-risk individuals effectively.

Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study

Background & Aims

Assessment of computed tomography (CT)/magnetic resonance imaging Liver Imaging Reporting and Data System (LI-RADS) v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. We assessed the performance and added-value of a machine learning (ML)-based algorithm in assessing CT LI-RADS major features and categorisation of liver observations compared with qualitative assessment performed by a panel of radiologists.

Methods

High-risk patients as per LI-RADS v2018 with pathologically proven liver lesions who underwent multiphase contrast-enhanced CT at diagnosis between January 2015 and March 2019 in seven centres in five countries were retrospectively included and randomly divided into a training set (n = 84 lesions) and a test set (n = 345 lesions). An ML algorithm was trained to classify non-rim arterial phase hyperenhancement, washout, and enhancing capsule as present, absent, or of uncertain presence. LI-RADS major features and categories were compared with qualitative assessment of two independent readers. The performance of a sequential use of the ML algorithm and independent readers were also evaluated in a triage and an add-on scenario in LR-3/4 lesions. The combined evaluation of three other senior readers was used as reference standard.

Results

A total of 318 patients bearing 429 lesions were included. Sensitivity and specificity for LR-5 in the test set were 0.67 (95% CI, 0.62-0.72) and 0.91 (95% CI, 0.87-0.96) respectively, with 242 (70.1%) lesions accurately categorised. Using the ML algorithm in a triage scenario improved the overall performance for LR-5. (0.86 and 0.93 sensitivity, 0.82 and 0.76 specificity, 78% and 82.3% accuracy for the two independent readers).

Conclusions

Quantitative assessment of CT LI-RADS v2018 major features is feasible and diagnoses LR-5 observations with high performance especially in combination with the radiologist's visual analysis in patients at high-risk for HCC.

Impact and implications

Assessment of CT/MRI LI-RADS v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. Rather than replacing radiologists, our results highlight the potential benefit from the radiologist-artificial intelligence interaction in improving focal liver lesions characterisation by using the developed algorithm as a triage tool to the radiologist's visual analysis. Such an AI-enriched diagnostic pathway may help standardise and improve the quality of analysis of liver lesions in patients at high risk for HCC, especially in non-expert centres in liver imaging. It may also impact the clinical decision-making and guide the clinician in identifying the lesions to be biopsied, for instance in patients with multiple liver focal lesions.

Predicting the recurrence of hepatocellular carcinoma (≤ 5 cm) after resection surgery with promising risk factors: habitat fraction of tumor and its peritumoral micro-environment

PURPOSE

Characterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict the recurrence-free survival (RFS) of HCC (≤ 5 cm) with habitat imaging of HCC and its peritumoral micro-environment.

MATERIAL AND METHODS

A total of 264 patients with HCC were included. Taking advantage of the enhancement ratio at the arterial and hepatobiliary phase of contrast-enhanced MRI, all HCCs and their peritumoral tissue of 3 mm and 4 mm were encoded with different habitats. Besides, the quantitative fraction of each habitat of HCC and peritumoral tissue were calculated. Univariable and multivariable Cox regression analysis was performed to select the prognostic factors. The nomogram-based predictor was established. Kaplan-Meier analysis was conducted to stratify the recurrence risk. Fivefold cross-validation was performed to determine the predictive performance with the concordance index (C-Index). Decision curve analysis was used to evaluate the net benefit.

RESULTS

Qualitatively, the spatial distribution of the habitats varied for different survival outcomes. Quantitatively, the fraction of habitat 3 in peritumoral tissue of 4 mm (f3-P4) was selected as independent risk factors (OR = 89.2, 95% CI = 14.5-549.2, p < 0.001) together with other two clinical variables. Integrating both clinical variables and f3-P4, a nomogram was constructed and showed high predictive efficacy (C-Index: 0.735, 95% CI 0.617-0.854) and extra net benefit according to the decision curve. Furthermore, patients with low f3-P4 or risk score given by nomogram have far longer RFS than those with high f3-P4 or risk score (stratification by f3-P4: 131.9 vs 55.0 months and stratification by risk score:131.9 vs 34.1 months).

CONCLUSION

Habitat imaging of HCC and peritumoral microenvironment can be used for effectively and non-invasively estimating the RFS, which holds potential in guiding clinical management and decision making.

Diagnosis of hepatocellular carcinoma using Sonazoid: a comprehensive review

Sonazoid contrast-enhanced ultrasonography (CEUS) is a promising technique for the detection and diagnosis of focal liver lesions, particularly hepatocellular carcinoma (HCC). Recently, a collaborative effort between the Korean Society of Radiology and Korean Society of Abdominal Radiology resulted in the publication of guidelines for diagnosing HCC using Sonazoid CEUS. These guidelines propose specific criteria for identifying HCC based on the imaging characteristics observed during Sonazoid CEUS. The suggested diagnostic criteria include nonrim arterial phase hyperenhancement, and the presence of late and mild washout, or Kupffer phase washout under the premise that the early or marked washout should not occur during the portal venous phase. These criteria aim to improve the accuracy of HCC diagnosis using Sonazoid CEUS. This review offers a comprehensive overview of Sonazoid CEUS in the context of HCC diagnosis. It covers the fundamental principles of Sonazoid CEUS and its clinical applications, and introduces the recently published guidelines. By providing a summary of this emerging technique, this review contributes to a better understanding of the potential role of Sonazoid CEUS for diagnosing HCC.

Multimodality imaging of hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Hepatocellular carcinoma and intrahepatic cholangiocarcinoma are the two most common primary malignant tumors of the liver. The similarities and variations in imaging characteristics that may aid in distinguishing between these two primary tumors will be discussed and outlined in this review. Knowledge of imaging techniques that are currently available would assist in the differentiation between these primary malignancies.

Machine learning-based radiomics analysis of preoperative functional liver reserve with MRI and CT image

OBJECTIVE

The indocyanine green retention rate at 15 min (ICG-R15) is a useful tool to evaluate the functional liver reserve before hepatectomy for liver cancer. Taking ICG-R15 as criteria, we investigated the ability of a machine learning (ML)-based radiomics model produced by Gd-EOB-DTPA-enhanced hepatic magnetic resonance imaging (MRI) or contrast-enhanced computed tomography (CT) image in evaluating functional liver reserve of hepatocellular carcinoma (HCC) patients.

METHODS

A total of 190 HCC patients with CT, among whom 112 also with MR, were retrospectively enrolled and randomly classified into a training dataset (CT: n = 133, MR: n = 78) and a test dataset (CT: n = 57, MR: n = 34). Then, radiomics features from Gd-EOB-DTPA MRI and CT images were extracted. The features associated with the ICG-R15 classification were selected. Five ML classifiers were used for the ML-model investigation. The accuracy (ACC) and the area under curve (AUC) of receiver operating characteristic (ROC) with 95% confidence intervals (CI) were utilized for ML-model performance evaluation.

RESULTS

A total of 107 different radiomics features were extracted from MRI and CT, respectively. The features related to ICG-R15 which was classified into 10%, 20% and 30% were selected. In MRI groups, classifier XGBoost performed best with its AUC = 0.917 and ACC = 0.882 when the threshold was set as ICG-R15 = 10%. When ICG-R15 = 20%, classifier Random Forest performed best with AUC = 0.979 and ACC = 0.882. When ICG-R15 = 30%, classifier XGBoost performed best with AUC = 0.961 and ACC = 0.941. For CT groups, the classifier XGBoost performed best when ICG-R15 = 10% with AUC = 0.822 and ACC = 0.842. When ICG-R15 = 20%, classifier SVM performed best with AUC = 0.860 and ACC = 0.842. When ICG-R15 = 30%, classifier XGBoost performed best with AUC = 0.938 and ACC = 0.965.

CONCLUSIONS

Both the MRI- and CT-based machine learning models are proved to be valuable noninvasive methods for functional liver reserve evaluation.

Spatiotemporal Excitation Module-based CNN for Diagnosis of Hepatic Malignancy in Four-phase CT Images

Liver cancer is a part of the common causes of cancer death worldwide, and the accurate diagnosis of hepatic malignancy is important for effective next treatment. In this paper, we propose a convolutional neural network (CNN) based on a spatiotemporal excitation (STE) module for identification of hepatic malignancy in four-phase computed tomography (CT) images. To enhance the display detail of lesion, we expand single-channel CT images into three channels by using the channel expansion method. Our proposed STE module consists of a spatial excitation (SE) module and a temporal interaction (TI) module. The SE module calculates the temporal differences of CT slices at the feature level, which is used to excite shape-sensitive channels of the lesion features. The TI module shifts a portion of the channels in the temporal dimension to exchange information among the current CT slice and adjacent CT slices. Four-phase CT images of 398 patients diagnosed with hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are used for experiments and five cross-validations are performed. Our model achieved average accuracy of 85.00% and average AUC of 88.91% for classifying HCC and ICC.Clinical Relevance- The proposed deep learning-based model can perform HCC and ICC recognition tasks based on four-phase CT images, assisting doctors to obtain better diagnostic performance.

A gadoxetic-acid enhancement flux analysis of small liver nodules (≤2 cm) in patients at high risk of hepatocellular carcinoma

PURPOSE

To discriminate between benignities and hepatocellular carcinomas (HCCs) in patients at high risk of HCC using a novel enhancement flux analysis for gadoxetic-acid enhanced MRI.

METHOD

This study retrospectively collected 181 liver nodules in 156 patients at high risk of HCC who underwent gadoxetic acid-enhanced MRI examinations with following surgical resection from 1st August 2017 to 31st December 2021 as the training set; another 42 liver nodules in 36 patients were prospectively collected from 1st January 2022 to 1st October 2022 as the test set. The time-intensity curves (TICs) of liver nodules were formed with consecutive time points: 0 s, 20 s, 1 min, 2 min, 5 min, 10 min, 15 min, and 20 min since contrast injection. A novel enhancement flux analysis was applied by using a biexponential function fitting to distinguish benignities and HCC. Besides, previously published models including maximum enhancement ratio (ER_max), percentage signal ratio (PSR), and ER_max+PSR were compared. The areas under the receiver operating characteristic curves (AUCs) were compared among these methods.

RESULTS

The novel enhancement flux analysis showed the highest AUCs in the training set (0.897, 95%CI: 0.833-0.960) and the test set (0.859, 95%CI: 0.747-0.970) among all models. The AUCs of PSR, ER_max and ER_max+PSR were 0.801 (95%CI: 0.710-0.891), 0.620 (95%CI: 0.510-0.729), and 0.799 (95%CI: 0.709-0.889) in the training set, and were 0.701 (95%CI: 0.539-0.863), 0.529 (95%CI: 0.342-0.717), and 0.708 (95%CI: 0.549-0.867) in the test set.

CONCLUSIONS

The biexponential flux analysis for gadoxetic-acid enhanced MRI presents a better potential in accurate diagnosis of small HCC nodules.

Adherence to LI-RADS and EASL high-risk population criteria: A systematic review

BACKGROUND AND AIMS

The Liver Imaging Reporting and Data System (LI-RADS) and European Association for the Study of the Liver (EASL) diagnostic criteria for noninvasive diagnosis of HCC can only be applied to patients at a high risk of HCC. This systematic review assesses adherence to the LI-RADS and EASL high-risk population criteria in published studies.

APPROACH AND RESULTS

PubMed was searched for original research, published between January 2012 and December 2021, reporting LI-RADS and EASL diagnostic criteria on contrast-enhanced ultrasound, CT, or MRI. The algorithm version, publication year, risk status, and etiologies of chronic liver disease were recorded for each study. Adherence to high-risk population criteria was evaluated as optimal (unequivocal adherence), suboptimal (equivocal), or inadequate (clear violation). A total of 219 original studies were included, with 215 that used the LI-RADS criteria, 4 EASL only, and 15 that evaluated both LI-RADS and EASL criteria. Optimal, suboptimal, or inadequate adherence to high-risk population criteria was observed in 111/215 (51.6%), 86/215 (40.0%), and 18/215 (8.4%) LI-RADS and 6/19 (31.6%), 5/19 (26.3%), and 8/19 (42.1%) EASL studies ( p < 0.001) regardless of the imaging modality. Adherence to high-risk population criteria significantly improved according to the CT/MRI LI-RADS versions (optimal in v2018 in 64.5% of studies; v2017, 45.8%; v2014, 24.4%; v2013.1, 33.3%; p < 0.001) and the publication year (2020-2021, 62.5%; 2018-2019, 33.9%; 2014-2017, 39.3% of all LI-RADS studies; p = 0.002). No significant differences in adherence to high-risk population criteria were observed in the versions of contrast-enhanced ultrasound LI-RADS ( p = 0.388) or EASL ( p = 0.293).

CONCLUSION

Adherence to high-risk population criteria was optimal or suboptimal in about 90% and 60% of LI-RADS and EASL studies, respectively.

Synergistic Nanomedicine: Photodynamic, Photothermal and Photoimmune Therapy in Hepatocellular Carcinoma: Fulfilling the Myth of Prometheus?

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with high morbidity and mortality, which seriously threatens the health and life expectancy of patients. The traditional methods of treatment by surgical ablation, radiotherapy, chemotherapy, and more recently immunotherapy have not given the expected results in HCC. New integrative combined therapies, such as photothermal, photodynamic, photoimmune therapy (PTT, PDT, PIT), and smart multifunctional platforms loaded with nanodrugs were studied in this review as viable solutions in the synergistic nanomedicine of the future. The main aim was to reveal the latest findings and open additional avenues for accelerating the adoption of innovative approaches for the multi-target management of HCC. High-tech experimental medical applications in the molecular and cellular research of photosensitizers, novel light and laser energy delivery systems and the features of photomedicine integration via PDT, PTT and PIT in immuno-oncology, from bench to bedside, were introspected. Near-infrared PIT as a treatment of HCC has been developed over the past decade based on novel targeted molecules to selectively suppress cancer cells, overcome immune blocking barriers, initiate a cascade of helpful immune responses, and generate distant autoimmune responses that inhibit metastasis and recurrences, through high-tech and intelligent real-time monitoring. The process of putting into effect new targeted molecules and the intelligent, multifunctional solutions for therapy will bring patients new hope for a longer life or even a cure, and the fulfillment of the myth of Prometheus.

Incidental Findings in Pediatric Patients: How to Manage Liver Incidentaloma in Pediatric Patients

The World Federation for Ultrasound in Medicine and Biology (WFUMB) is addressing the issue of incidental findings (IFs) with a series of publications entitled "Incidental imaging findings-the role of ultrasound". IFs in the liver of newborns and children are rare and much less commonly encountered than in adults; as a result, they are relatively much more frequently malignant and life-threatening, even when they are of benign histology. Conventional B-mode ultrasound is the well-established first line imaging modality for the assessment of liver pathology in pediatric patients. US technological advances, resulting in image quality improvement, contrast-enhanced ultrasound (CEUS), liver elastography and quantification tools for steatosis have expanded the use of ultrasound technology in daily practice. The following overview is intended to illustrate incidentally detected liver pathology covering all pediatric ages. It aims to aid the examiner in establishing the final diagnosis. Management of incidentally detected focal liver lesions (FLL) needs to take into account the diagnostic accuracy of each imaging modality, the patient's safety issues (including ionizing radiation and nephrotoxic contrast agents), the delay in diagnosis, the psychological burden on the patient and the cost for the healthcare system. Moreover, this paper should help the pediatric clinician and ultrasound practitioner to decide which pathologies need no further investigation, which ones require interval imaging and which cases require further and immediate diagnostic procedures.

Improved diagnosis of histological capsule in hepatocallular carcinoma by using nonenhancing capsule appearance in addition to enhancing capsule appearance in gadoxetic acid-enhanced MRI

To assess the value of nonenhancing capsule by adding to enhancing capsule in gadoxetic acid-enhanced MRI (EOB-MRI) in comparison with contrast-enhanced CT (CE-CT) for diagnosing histological capsule in hepatocellular carcinoma (HCC). One-hundred fifty-one patients with HCC who underwent both CE-CT and EOB-MRI were retrospectively reviewed. Liver Imaging-Reporting and Data System (LI-RADS) v2018 imaging features, including enhancing and nonenhancing capsule were evaluated by two readers in CE-CT and EOB-MRI. Frequencies of each imaging feature were compared between CE-CT and EOB-MRI. The area under the receiver operating characteristic (AUC) curve for the diagnosis of histological capsule was compared across the following three imaging criteria: (1) enhancing capsule in CE-CT, (2) enhancing capsule in EOB-MRI, and (3) enhancing/nonenhancing capsule in EOB-MRI. Enhancing capsule in EOB-MRI was significantly less frequently depicted than that in CE-CT (p < 0.001 and = 0.016 for reader 1 and 2). Enhancing/nonenhancing capsule in EOB-MRI achieved a similar frequency of enhancing in CE-CT (p = 0.590 and 0.465 for reader 1 and 2). Adding nonenhancing capsule to enhancing capsule in EOB-MRI significantly increased AUCs (p < 0.001 for both readers) and achieved similar AUCs compared with enhancing capsule in CE-CT (p = 0.470 and 0.666 for reader 1 and 2). Adding nonenhancing capsule to the definition of capsule appearance can improve the diagnosis of capsule in EOB-MRI for the diagnosis of histological capsule in HCC and decrease discordance of capsule appearance between EOB-MRI and CE-CT.

A new classification and regression tree algorithm: Improved diagnostic sensitivity for HCC ≤ 3.0 cm using gadoxetate disodium-enhanced MRI

PURPOSE

To develop and validate an effective algorithm, based on classification and regression tree (CART) analysis and LI-RADS features, for diagnosing HCC ≤ 3.0 cm with gadoxetate disodium‑enhanced MRI (Gd-EOB-MRI).

METHOD

We retrospectively included 299 and 90 high-risk patients with hepatic lesions ≤ 3.0 cm that underwent Gd-EOB-MRI from January 2018 to February 2021 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Through binary and multivariate regression analyses of LI-RADS features in the development cohort, we developed an algorithm using CART analysis, which comprised the targeted appearance and independently significant imaging features. On per-lesion basis, we compared the diagnostic performances of our algorithm, two previously reported CART algorithms, and LI-RADS LR-5 in development and validation cohorts.

RESULTS

Our CART algorithm, presenting as a decision tree, included targetoid appearance, HBP hypointensity, nonrim arterial phase hyperenhancement (APHE), and transitional phase hypointensity plus mild-moderate T2 hyperintensity. For definite HCC diagnosis, the overall sensitivity of our algorithm (development cohort 93.2%, validation cohort 92.5%; P < 0.006) was significantly higher than those of Jiang's algorithm modified LR-5 (defined as targetoid appearance, nonperipheral washout, restricted diffusion, and nonrim APHE) and LI-RADS LR-5, with the comparable specificity (development cohort: 84.3%, validation cohort: 86.7%; P ≥ 0.006). Our algorithm, providing the highest balanced accuracy (development cohort: 91.2%, validation cohort: 91.6%), outperformed other criteria for identifying HCCs from non-HCC lesions.

CONCLUSIONS

In high-risk patients, our CART algorithm developed with LI-RADS features showed promise for the early diagnosis of HCC ≤ 3.0 cm with Gd-EOB-MRI.

Added value of contrast enhancement boost images in routine multiphasic contrast-enhanced CT for the diagnosis of small (<20 mm) hypervascular hepatocellular carcinoma

PURPOSE

To investigate the added value of contrast enhancement boost (CE-boost) images in multiphasic contrast-enhanced CT (CE-CT) for diagnosing small (<20 mm) hypervascular hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

This retrospective study included 69 patients (age, 74 ± 8 years; 52 men) with 70 hypervascular HCCs (<20 mm) who underwent multiphasic CE-CT (pre-contrast, late arterial phase [LAP], portal venous phase [PVP], and equilibrium phase). Two types of CE-boost images were generated by subtracting PVP from LAP (LA-PV) images and LAP from PVP (PV-LA) images to enhance the contrast effect of hepatic arterial and portal venous perfusion more selectively. Tumor-to-liver contrast-to-noise ratios (CNRs) in CE-boost images were compared with those in CE-CT images using the Wilcoxon signed-rank test. Two independent readers reviewed the imaging datasets: CE-CT alone and CE-CT with CE-boost images. The diagnostic performance of each dataset was compared using jackknife alternative free-response receiver operating characteristics (JAFROC-1).

RESULTS

The tumor-to-liver CNRs in the LA-PV (6.4 ± 3.0) and PV-LA (-3.3 ± 2.1) images were greater than those in the LAP (3.2 ± 1.7) and PVP images (-1.1 ± 1.4) (p <.001 for both). The reader-averaged figures of merit were 0.751 for CE-CT alone and 0.807 for CE-CT with CE-boost images (p <.001). Sensitivities increased by adding CE-boost images for both readers (p <.001 and = 0.03), while positive predictive values were equivalent (p >.99).

CONCLUSION

Adding CE-boost images to multiphasic CE-CT can improve the diagnostic accuracy and sensitivity for small hypervascular HCC by increasing the tumor-to-liver CNR.

Prospective evaluation of Gadoxetate-enhanced magnetic resonance imaging and computed tomography for hepatocellular carcinoma detection and transplant eligibility assessment with explant histopathology correlation

BACKGROUND

We aimed to prospectively compare the diagnostic performance of gadoxetic acid-enhanced MRI (EOB-MRI) and contrast-enhanced Computed Tomography (CECT) for hepatocellular carcinoma (HCC) detection and liver transplant (LT) eligibility assessment in cirrhotic patients with explant histopathology correlation.

METHODS

In this prospective, single-institution ethics-approved study, 101 cirrhotic patients were enrolled consecutively from the pre-LT clinic with written informed consent. Patients underwent CECT and EOB-MRI alternately every 3 months until LT or study exclusion. Two blinded radiologists independently scored hepatic lesions on CECT and EOB-MRI utilizing the liver imaging reporting and data system (LI-RADS) version 2018. Liver explant histopathology was the reference standard. Pre-LT eligibility accuracies with EOB-MRI and CECT as per Milan criteria (MC) were assessed in reference to post-LT explant histopathology. Lesion-level and patient-level statistical analyses were performed.

RESULTS

Sixty patients (49 men; age 33-72 years) underwent LT successfully. One hundred four non-treated HCC and 42 viable HCC in previously treated HCC were identified at explant histopathology. For LR-4/5 category lesions, EOB-MRI had a higher pooled sensitivity (86.7% versus 75.3%, p <  0.001) but lower specificity (84.6% versus 100%, p <  0.001) compared to CECT. EOB-MRI had a sensitivity twice that of CECT (65.9% versus 32.2%, p <  0.001) when all HCC identified at explant histopathology were included in the analysis instead of imaging visible lesions only. Disregarding the hepatobiliary phase resulted in a significant drop in EOB-MRI performance (86.7 to 72.8%, p <  0.001). EOB-MRI had significantly lower pooled sensitivity and specificity versus CECT in the LR5 category with lesion size < 2 cm (50% versus 79%, p = 0.002 and 88.9% versus 100%, p = 0.002). EOB-MRI had higher sensitivity (84.8% versus 75%, p <  0.037) compared to CECT for detecting < 2 cm viable HCC in treated lesions. Accuracies of LT eligibility assessment were comparable between EOB-MRI (90-91.7%, p = 0.156) and CECT (90-95%, p = 0.158).

CONCLUSION

EOB-MRI had superior sensitivity for HCC detection; however, with lower specificity compared to CECT in LR4/5 category lesions while it was inferior to CECT in the LR5 category under 2 cm. The accuracy for LT eligibility assessment based on MC was not significantly different between EOB-MRI and CECT.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03342677 , Registered: November 17, 2017.

Ultra-low-dose hepatic multiphase CT using deep learning-based image reconstruction algorithm focused on arterial phase in chronic liver disease: A non-inferiority study

PURPOSE

This study determined whether image quality and detectability of ultralow-dose hepatic multiphase CT (ULDCT, 33.3% dose) using a vendor-agnostic deep learning model(DLM) are noninferior to those of standard-dose CT (SDCT, 100% dose) using model-based iterative reconstruction(MBIR) in patients with chronic liver disease focusing on arterial phase.

METHODS

Sixty-seven patients underwent hepatic multiphase CT using a dual-source scanner to obtain two different radiation dose CT scans (100%, SDCT and 33.3%, ULDCT). ULDCT using DLM and SDCT using MBIR were compared. A margin of -0.5 for the difference between the two protocols was pre-defined as noninferiority of the overall image quality of the arterial phase image. Quantitative image analysis (signal to noise ratio[SNR] and contrast to noise ratio[CNR]) was also conducted. The detectability of hepatic arterial focal lesions was compared using the Jackknife free-response receiver operating characteristic analysis. Non-inferiority was satisfied if the margin of the lower limit of 95%CI of the difference in figure-of-merit was less than -0.1.

RESULTS

Mean overall arterial phase image quality scores with ULDCT using DLM and SDCT using MBIR were 4.35 ± 0.57 and 4.08 ± 0.58, showing noninferiority (difference: -0.269; 95 %CI, -0.374 to -0.164). ULDCT using DLM showed a significantly superior contrast-to-noise ratio of arterial enhancing lesion (p < 0.05). Figure-of-merit for detectability of arterial hepatic focal lesion was 0.986 for ULDCT using DLM and 0.963 for SDCT using MBIR, showing noninferiority (difference: -0.023, 95 %CI: -0.016 to 0.063).

CONCLUSION

ULDCT using DLM with 66.7% dose reduction showed non-inferior overall image quality and detectability of arterial focal hepatic lesion compared to SDCT using MBIR.