The incidence of hepatocellular carcinoma (HCC) is on the rise worldwide, due to the increasing prevalence of liver diseases associated with metabolic dysfunction and better management of cirrhosis and its complications. The diversification of HCC treatments has recently increased, with the choice of strategy based on HCC characteristics, liver function and comorbidities. The combination of new therapies has transformed the prognosis, with up to 70% survival at 5 years.

The aim of this review was to analyse the most recent data on preoperative evaluation, peri-operative anaesthetic management of liver resection, liver transplantation and other types of procedures, and to highlight the multidisciplinary aspect of such management.

The importance of preanaesthetic evaluation will depend largely on the procedure proposed, associated co-morbidities and the stage of liver disease. This assessment should verify stabilisation of all comorbidities, and evaluate the degree of portal hypertension, cirrhosis severity and sarcopenia. Liver resection and liver transplantation for HCC present specific surgical challenges, and minimally invasive techniques improve recovery. Nonsurgical procedures considered as therapeutic (ablation) or standby (regional embolisation) are diverse, and all expose patients to specific intra-anaesthetic complications, sometimes requiring intensive care management. Peri-operative anaesthetic strategies deployed in the management of liver resection or nonsurgical procedures involve specific management of fluids, coagulation, narcosis and analgesia, which can impact on patients' overall, and cancer prognosis. Lastly, new down-staging strategies combining several types of procedure and possibly immunotherapy, also call for collegial reflection on posthepatic transplant immunosuppression, which must remain tailored to each individual patient.

Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized reporting according to the Liver Imaging Reporting and Data System (LI-RADS) can improve Gd-MRI interpretation but is rather complex and time-consuming. These limitations could potentially be alleviated using recent deep learning-based segmentation and classification methods such as nnU-Net. The study aims to create and evaluate an automatic segmentation model for HCC risk assessment, according to LI-RADS v2018 using nnU-Net.

For this single-center retrospective study, 602 patients at risk for HCC were included, who had dynamic EOB-MRI examinations between 05/2005 and 09/2022, containing ≥ LR-3 lesion(s). Manual lesion segmentations in semantic segmentation masks as LR-3, LR-4, LR-5 or LR-M served as ground truth. A set of U-Net models with 14 input channels was trained using the nnU-Net framework for automatic segmentation. Lesion detection, LI-RADS classification, and instance segmentation metrics were calculated by post-processing the semantic segmentation outputs of the final model ensemble. For the external evaluation, a modified version of the LiverHccSeg dataset was used.

The final training/internal test/external test cohorts included 383/219/16 patients. In the three cohorts, LI-RADS lesions (≥ LR-3 and LR-M) ≥ 10 mm were detected with sensitivities of 0.41-0.85/0.40-0.90/0.83 (LR-5: 0.85/0.90/0.83) and positive predictive values of 0.70-0.94/0.67-0.88/0.90 (LR-5: 0.94/0.88/0.90). F1 scores for LI-RADS classification of detected lesions ranged between 0.48-0.69/0.47-0.74/0.84 (LR-5: 0.69/0.74/0.84). Median per lesion Sørensen-Dice coefficients were between 0.61-0.74/0.52-0.77/0.84 (LR-5: 0.74/0.77/0.84).

Deep learning-based HCC risk assessment according to LI-RADS can be implemented as automatically generated tumor risk maps using out-of-the-box image segmentation tools with high detection performance for LR-5 lesions. Before translation into clinical practice, further improvements in automatic LI-RADS classification, for example through large multi-center studies, would be desirable.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and early diagnosis via gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) significantly impacts patient outcomes. However, patient anxiety during MRI can affect image quality. This study investigates the impact of pre-examination video education on anxiety, satisfaction and image quality in Gd-EOB-DTPA-enhanced liver MRI.

We prospectively enrolled 480 patients who underwent Gd-EOB-DTPA-enhanced liver MRI from January 2022 to May 2023 at our hospital. Patients were divided into study and control groups in order of odd and even days, with 240 cases in each group. Before the examination, the radiology staff provided routine verbal guidance and breathing training to the patients in the control group, while the study group was given additional video education. The state anxiety scores, satisfaction scores of the provided information and motion artefact scores of the images before and after the examination were compared between the two groups.

The state anxiety scores of both groups of patients were lower than before the examination (all P < 0.05), but the change value of the study group was significantly greater than that of the control group (P = 0.004). The satisfaction rate of the information provided before the scan in the study group was significantly higher (P < 0.001). The image quality scores of the arterial phase were similar between the two groups (P = 0.403), but the image quality of the study group in the pre-contrast, portal phase, transitional phase and hepatobiliary phase was significantly better than that of the control group (all P < 0.05).

Supplementing routine pre-scan care with video guidance for Gd-EOB-DTPA-enhanced liver MRI offers several benefits, including reduced patient anxiety, increased satisfaction and improved image quality. These results suggest the potential for widespread application of video-based interventions to enhance the MRI experience for patients.

Hepatocellular carcinoma (HCC) is typically characterized by rich vascularity, with angiogenesis playing a crucial role in its growth and invasion. Molecular imaging of specific receptors in blood vessels is crucial in HCC diagnosis. In particular, in vivo imaging utilizing the second near-infrared (NIR-II) window offers improved tissue penetration, reduced light scattering, and lower autofluorescence. Despite the great potential of the NIR-II window, developing safe and effective probes to provide better imaging performance for HCC is urgently needed. In this study, NIR-II imaging integrated with a vascular endothelial growth factor receptor (VEGFR)-targeted probe generated by combining a VEGFR-targeted peptide with indocyanine green (ICG) is used to characterize HCC-related angiogenesis at a resolution of 56.0 µm. For the first time, liver metabolic curves and parameters of liver function reserve (LFR) are obtained by fitting NIR-II fluorescence signals with high spatiotemporal resolution, showing significant differences between HCC mice and controls. Moreover, unlike ICG, the targeting probe has a targeted effect on blood vessels in vivo. The tumor-to-normal (T/N) ratio in NIR-II imaging reaches up to 3.30 after post-injection of the targeting probe. The results indicate that the VEGFR-targeted probe is a powerful tool for NIR-II fluorescence imaging to enhance early diagnosis of HCC.

Primary liver cancer (PLC), notably hepatocellular carcinoma (HCC), stands as a formidable global health challenge, ranking as the sixth most prevalent malignant tumor and the third leading cause of cancer-related deaths. HCC presents a daunting clinical landscape characterized by nonspecific early symptoms and late-stage detection, contributing to its poor prognosis. Moreover, the limited efficacy of existing treatments and high recurrence rates post-surgery compound the challenges in managing this disease. While histopathologic examination remains the cornerstone for HCC diagnosis, its utility in guiding preoperative decisions is constrained. Radiomics, an emerging field, harnesses high-throughput imaging data, encompassing shape, texture, and intensity features, alongside clinical parameters, to elucidate disease characteristics through advanced computational techniques such as machine learning and statistical modeling. MRI radiomics specifically holds significant importance in the diagnosis and treatment of hepatocellular carcinoma (HCC).

This study aims to evaluate the methodology of radiomics and delineate the clinical advancements facilitated by MRI-based radiomics in the realm of hepatocellular carcinoma diagnosis and treatment.

A systematic review of the literature was conducted, encompassing peer-reviewed articles published between July 2018 and Jan 2025, sourced from PubMed and Google Scholar. Key search terms included Hepatocellular carcinoma, HCC, Liver cancer, Magnetic resonance imaging, MRI, radiomics, deep learning, machine learning, and artificial intelligence.

A comprehensive analysis of 93 articles underscores the efficacy of MRI radiomics, a noninvasive imaging analysis modality, across various facets of HCC management. These encompass tumor differentiation, subtype classification, histopathological grading, prediction of microvascular invasion (MVI), assessment of treatment response, early recurrence prognostication, and metastasis prediction.

MRI radiomics emerges as a promising adjunctive tool for early HCC detection and personalized preoperative decision-making, with the overarching goal of optimizing patient outcomes. Nevertheless, the current lack of interpretability within the field underscores the imperative for continued research and validation efforts.

We aimed to develop and validate a combined model integrating radiomic features derived from Contrast-Enhanced Ultrasound (CEUS) images and clinical parameters for preoperative prediction of CK19-positive status in hepatocellular carcinoma (HCC).

A total of 434 patients who underwent CEUS and surgical resection from January 2020 to December 2023 were included. Patients were randomly divided into a training cohort (n = 304) and a validation cohort (n = 130). Radiomic features were extracted from multiphase CEUS images, including two-dimensional ultrasound (US), arterial, portal venous, and delayed phases, and combined to derive a Radscore model. Subsequently, a Combined Model was constructed using the Radscore and clinical parameters. Model performance was assessed using calibration, discrimination, and clinical utility.

Multivariate logistic regression analysis identified Radscore (OR = 10.054, 95% CI: 5.931-19.120, p < 0.001) and AFP levels > 200 ng/mL (OR = 5.027, 95% CI: 2.089-12.784, p < 0.001) as significant predictors in the combined model. The AUC (Area Under the Curve) for the Combined Model was 0.954 in the training cohort and 0.927 in the validation cohort, compared to 0.939 and 0.917 for the Radscore Model alone. Calibration curves demonstrated strong concordance between predicted and actual outcomes. Decision curve analysis (DCA) showed that both the Radscore Model and the Combined Model exhibited good net benefits across a wide range of threshold values in both the training and validation cohorts.

The Radscore based on CEUS, combined with the serum markers AFP > 200 ng/L to construct a Combined Model, shows good predictive performance for CK19 + hepatocellular carcinoma (HCC).

BackgroundLiver dysfunction has been reported as a risk factor for predicting complications after hepatectomy. In patients with liver cirrhosis (LC) who underwent hepatectomy, a Functional Liver Imaging Score (FLIS), derived from gadoxetic acid-enhanced magnetic resonance imaging (MRI), has never been investigated as a predictor of clinically significant post-hepatectomy complications.PurposeTo evaluate whether FLIS can predict post-hepatectomy complications in patients with LC.Material and MethodsA retrospective review was conducted of patients with LC who underwent gadoxetic acid-enhanced MRI and hepatectomy. Univariable and multivariable logistic regression was used to identify clinicopathological and radiologic findings associated with the development of major complication (Clavien-Dindo classification [CDC] ≥ III). Receiver operating characteristic (ROC) curve analysis was performed to determine the cutoff value of FLIS for predicting CDC ≥ III.ResultsOf the finally included 106 patients (77 men; mean age = 62.5 ± 8.3 years), 12 patients had a CDC ≥ III. Multivariable analysis showed that only FLIS independently predicted post-hepatectomy complications (odds ratio = 0.02; P = 0.01). ROC analysis suggested the FLIS ≤ 4 was the optimal cutoff for predicting CDC ≥ III (AUC value = 0.94; sensitivity = 91.67%; specificity = 95.74%; positive likelihood ratio = 21.54; and negative likelihood ratio = 0.09).ConclusionIn patients with LC, FLIS was an independent predictor of post-hepatectomy complications. FLIS showed excellent diagnostic performance in predicting post-hepatectomy complications.

Liver cancer is the third leading cause of cancer-related deaths worldwide, with hepatocellular carcinoma (HCC) accounting for approximately 90% of primary liver cancers. Advances in diagnostic and therapeutic tools, along with improved understanding of their application, are transforming patient treatment. Integrating these innovations into clinical practice presents challenges and necessitates guidance. These clinical practice guidelines offer updated advice for managing patients with HCC and provide a comprehensive review of pertinent data. Key updates from the 2018 EASL guidelines include personalised surveillance based on individual risk assessment and the use of new tools, standardisation of liver imaging procedures and diagnostic criteria, use of minimally invasive surgery in complex cases together with updates on the integrated role of liver transplantation, transitions between surgical, locoregional, and systemic therapies, the role of radiation therapies, and the use of combination immunotherapies at various stages of disease. Above all, there is an absolute need for a multiparametric assessment of individual risks and benefits, considering the patient's perspective, by a multidisciplinary team encompassing various specialties.

This study compared the characteristics of lesions suspicious for hepatocellular carcinoma (HCC) and their LI-RADS classifications in adaptive statistical iterative reconstruction (ASIR) and deep learning reconstruction (DLR) to those of MR images, along with radiologist confidence.

This prospective single-center trial included patients who underwent four-phase liver CT and multiphasic contrast-enhanced MRI within 7 days from February to August 2023. The lesion characteristics, LI-RADS classifications and confidence scores according to two radiologists on the ASIR, DLR and MRI techniques were compared. If the patient had at least one lesion, he was included in the HCC group, otherwise in the non-HCC group. MRI being the technique with the best sensitivity, concordance of lesions characteristics and LI-RADS classifications were calculated by weighted kappa between the ASIR and MRI and between the DLR and MRI. The confidence scores are expressed as the means and standard deviations.

Eighty-nine patients were enrolled, 52 in the HCC group (67 years ± 9 [mean ± SD], 46 men) and 37 in the non-HCC group (68 years ± 9, 33 men). The concordance coefficient between the LI-RADS classification by ASIR and MRI was 0.64 [0.52; 0.76], showing good agreement, that by DLR and MRI was 0.83 [0.73; 0.92], showing excellent agreement. The diagnostic confidence in ASIR was 3.31 ± 0.95 (mean ± SD) and 3.0 ± 1.11, that in the DLR was 3.9 ± 0.88 and 4.11 ± 0.75, that in the MRI was 4.46 ± 0.80 and 4.57 ± 0.80.

DLR provided excellent LI-RADS classification concordance with MRI, whereas ASIR provided good concordance. The radiologists' confidence was greater in the DLR than in the ASIR but remained highest in the MR group.

Question Does the use of deep learning reconstructions (DLR) improve LI-RADS classification of suspicious hepatocellular carcinoma lesions compared to adaptive statistical iterative reconstructions (ASIR)? Findings DLR demonstrated superior concordance of LI-RADS classification with MRI compared to ASIR. It also provided greater diagnostic confidence than ASIR. Clinical relevance The use of DLR enhances radiologists' ability to visualize and characterize lesions suspected of being HCC, as well as their LI-RADS classification. Moreover, it also boosts their confidence in interpreting these images.

Hepatocellular carcinoma (HCC) and biliary tract cancers (BTC) pose significant diagnostic and therapeutic challenges. Magnetic resonance imaging (MRI) and multiphase computed tomography (CT) have been the preferred imaging modalities for diagnosis, staging, and surveillance of patients with these malignancies. The best clinical outcomes depend on the appropriate selection of treatment options from the tools available in neo-adjuvant therapy, surgical resection, locoregional therapy, liver transplantation, and adjuvant therapy. While not a part of the routine diagnostic work-up or follow-up, F-18 fluorodeoxyglucose positron emission tomography (FDG PET/CT) can inform therapeutic decision making and help avoid futile surgeries by detecting unsuspected distant metastases. Additionally, metabolic information obtained with FDG PET/CT has prognostic value, predicting treatment response and survival. In patients with HCC metabolic parameters obtained by FDG PET/CT have been shown to correlate with microvascular invasion and predict recurrence in orthotopic transplant recipients. This article will highlight studies that have evaluated the role of FDG PET/CT in diagnosis, staging and therapeutic response assessment in patients with hepatobiliary cancers.

This study aimed to develop nomograms for predicting post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC), using deep learning analysis of Gadoxetic acid-enhanced hepatobiliary (HBP) MRI.

This retrospective study analyzed patients who underwent gadoxetic acid-enhanced MRI and hepatectomy for HCC between 2016 and 2020 at two referral centers. Using a deep learning algorithm, volumes and signal intensities of whole non-tumor liver, expected remnant liver, and spleen were measured on HBP images. Two multivariable logistic regression models were formulated to predict PHLF, defined and graded by the International Study Group of Liver Surgery: one based on whole non-tumor liver measurements (whole liver model) and the other on expected remnant liver measurements (remnant liver model). The models were presented as nomograms and a web-based calculator. Discrimination performance was evaluated using the area under the receiver operating curve (AUC), with internal validation through 1000-fold bootstrapping.

The study included 1760 patients (1395 male; mean age ± standard deviation, 60 ± 10 years), with 137 (7.8%) developing PHLF. Nomogram predictors included sex, gamma-glutamyl transpeptidase, prothrombin time international normalized ratio, platelets, extent of liver resection, and MRI variables derived from the liver volume, liver-to-spleen signal intensity ratio, and spleen volume. The whole liver and the remnant liver nomograms demonstrated strong predictive performance for PHLF (optimism-corrected AUC of 0.78 and 0.81, respectively) and symptomatic (grades B and C) PHLF (optimism-corrected AUC of 0.81 and 0.84, respectively).

Nomograms based on deep learning analysis of gadoxetic acid-enhanced HBP images accurately stratify the risk of PHLF.

Question Can PHLF be predicted by integrating clinical and MRI-derived volume and functional variables through deep learning analysis of gadoxetic acid-enhanced MRI? Findings Whole liver and remnant liver nomograms demonstrated strong predictive performance for PHLF with the optimism-corrected area under the curve of 0.78 and 0.81, respectively. Clinical relevance These nomograms can effectively stratify the risk of PHLF, providing a valuable tool for treatment decisions regarding hepatectomy for HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide and is the fastest growing cause of cancer death in the United States (U.S.) In the U.S., current national clinical practice guidelines from the 2023 American Association for the Study of Liver Diseases (AASLD) Practice Guidance and the recently updated Liver Imaging Reporting & Data Systems (LI-RADS) Ultrasound (US) Surveillance v2024 core recommend semi-annual serum α-fetoprotein and US screening of patients deemed to be high risk for developing HCC. In this article, we will explore the transition to a patient-centered approach to HCC surveillance, including the role of the new LI-RADS US Surveillance v2024 core and the use of visualization score for determining ultrasound quality, the known risk factors for poor US image quality, and the potential options for alternative surveillance strategies when US may not be a viable option for certain patients, including multiphasic computed tomography (CT), magnetic resonance imaging (MRI), and several abbreviated MRI protocols.

Due to the major improvements in the hardware and image reconstruction algorithms, positron emission tomography/magnetic resonance imaging (PET/MR) is now a reliable state-of-the-art hybrid modality in medical practice. Currently, it can provide a broad range of advantages in preclinical and clinical imaging compared to single-modality imaging. In the second part of this review, we discussed the further clinical applications of PET/MR. In the chest, PET/MR has particular potential in the oncology setting, especially when utilizing ultrashort/zero echo time MR sequences. Furthermore, cardiac PET/MR can provide reliable information in evaluating myocardial inflammation, cardiac amyloidosis, myocardial perfusion, myocardial viability, atherosclerotic plaque, and cardiac masses. In gastrointestinal and hepato-pancreato-biliary malignancies, PET/MR is able to precisely detect metastases to the liver, being superior over the other imaging modalities. In genitourinary and gynaecology applications, PET/MR is a comprehensive diagnostic method, especially in prostate, endometrial, and cervical cancers. Its simultaneous acquisition has been shown to outperform other imaging techniques for the detection of pelvic nodal metastases and is also a reliable modality in radiation planning. Lastly, in haematologic malignancies, PET/MR can significantly enhance lymphoma diagnosis, particularly in detecting extra-nodal involvement. It can also comprehensively assess treatment-induced changes. Furthermore, PET/MR may soon become a routine in multiple myeloma management, being a one-stop shop for evaluating bone, bone marrow, and soft tissues.

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.

Hepatocellular carcinoma (HCC) is the third most fatal and fifth most common cancer worldwide, with rising incidence due to obesity and nonalcoholic fatty liver disease. Imaging modalities, including ultrasound (US), multidetector computed tomography (MDCT), and magnetic resonance imaging (MRI) play a vital role in detecting HCC characteristics, aiding in early detection, detailed visualization, and accurate differentiation of liver lesions. Liver-specific contrast agents, the Liver Imaging Reporting and Data System, and advanced techniques, including diffusion-weighted imaging and artificial intelligence, further enhance diagnostic accuracy. This review emphasizes the significant role of imaging in managing HCC, from diagnosis to treatment assessment, without the need for invasive biopsies.

The early diagnosis of hepatocellular carcinomas (HCCs) remains challenging in the clinic. Primovist-enhanced magnetic resonance imaging (MRI) aids HCC diagnosis but loses sensitivity for tumors <2 cm. Therefore, developing advanced MRI contrast agents is imperative for improving the diagnostic accuracy of HCCs in very-early-stage. To address this challenge, PEGylated ultra-small iron oxide nanoparticles (PUSIONPs) are synthesized and employed as liver-specific T1 MRI contrast agents. Intravenous delivery produces simultaneous hyperintense HCC and hypointense hepatic parenchyma signals on T1 imaging, creating an extraordinarily high tumor-to-liver contrast. Systematic studies uncover PUSIONP distribution in hepatic parenchyma, HCC lesions at the organ, tissue, cellular, and subcellular levels, revealing endosomal confinement of PUSIONP without aggregation. By mimicking such situations, the dependency of relaxometric properties on local PUSIONP concentration is investigated, emphasizing the key role of different endosomal concentrations in liver and tumor cells for high tumor-to-liver contrast and clear tumor boundaries. These findings offer exceptional imaging capabilities for early HCC diagnosis, potentially benefiting real HCC patients.

With ultrasound sensitivity limited in hepatocellular carcinoma (HCC) surveillance and few prospective studies on non-contrast abbreviated MRI (NC-AMRI), this study aimed to assess its diagnostic performance in detecting HCC.

This prospective study involved cirrhotic patients with contrast-enhanced MRI (CE-MRI) Liver Imaging Reporting and Data System (LI-RADS) LR-3 and LR-4 observations detected during HCC surveillance. Patients underwent average 3 complete CE-MRI rounds at 3-6 months interval, with approximately 12-month follow-up. NC-AMRI included diffusion-weighted (DWI), T2-weighted imaging (T2WI), and T1-weighted imaging (T1WI). NC-AMRI protocol images were analysed for diagnostic performance, with subgroup analyses. CE-MRI and NC-AMRI images were independently reviewed by 2 experienced radiologists, with inter-reader agreement assessed with Kappa coefficient. The reference standard was the American Association for the Study of Liver Diseases-defined presence of arterial hypervascularity and washout during the portal-venous or delayed phases on CE-MRI.

In 166 CE-MRI follow-ups of 63 patients (median age: 63 years; 60.3% male, 39.7% female), 12 patients developed HCC, with average size of 19.6 mm. The NC-AMRI (DWI + T2WI + T1WI) showed 91.7% sensitivity (95%CI, 61.5-99.8) and 91.6% specificity (95%CI, 86.0-95.4), area under receiver operating characteristic 0.92 (95%CI, 0.83-1.00). Across different Body Mass Index categories, lesion size, Child-Turcotte-Pugh classes, Albumin-Bilirubin (ALBI) grades, and Model for End-Stage Liver Disease classes, sensitivity remained consistent. However, specificity differed significantly between ALBI grade 1 and 2 (86.7% vs. 98.4%, P = .010), and between viral and non-viral cirrhosis (93.8% vs. 80.8%, P = .010).

NC-AMRI proved clinically feasible, and exhibits high diagnostic performance in HCC detection.

This study highlights efficacy of NC-AMRI in detecting HCC among cirrhotic patients with LR-3 and LR-4 observations, representing significant progress in HCC surveillance.

To compare the detection rates of hepatic artery digital subtraction angiography (HA-DSA) and magnetic resonance imaging (MRI) gadolinium diethylenetriaminepentaacetic acid (MRI-Gd-DTPA) and MRI gadolinium diethylenetriaminepentaacetic acid (MRI-Gd-EOB-DTPA) for small (diameter ≤2 cm) hepatocellular carcinoma (HCC) lesions.

A prospective analysis of patients admitted to the Tumor Hospital of Guangxi Medical University between January 1, 2015, and December 30, 2016, was conducted. The detection rates of the three methods were analyzed. The diameter of small HCC lesions detected using HA-DSA and MRI-Gd-EOB-DTPA were evaluated. The diagnostic value of HCC Barcelona staging for HA-DSA was analyzed.

For 107 small lesions detected in 57 patients, the detection rates of HA-DSA and MRI-Gd-DTPA were 86.0% (92/107) and 71.0% (76/107), respectively (p < .05). Of 77 small lesions detected in 42 patients using MRI-Gd-EOB-DTPA and HA-DSA, 67 were detected using HA-DSA, all of which had a rich blood supply, and 72 were detected using MRI-Gd-EOB-DTPA. The minimum diameter of lesions detected using MRI-Gd-EOB-DTPA was approximately 0.4 cm, whereas that of lesions detected using HA-DSA was approximately 0.5 cm. After HA-DSA, a change in the Barcelona staging occurred in 33.3% (62/186) of cases but not after MRI-Gd-DTPA; HA-DSA was significantly better than MRI-Gd-DTPA for staging (p = .03).

HA-DSA and MRI-Gd-EOB-DTPA have high diagnostic values for the detection of small HCC lesions, which is helpful for accurate staging of HCC and provides the most valuable information for patient treatment and prognosis.

Hepatocellular carcinoma (HCC) surveillance is currently performed using a one-size-fits-all strategy with ultrasound plus AFP (US + AFP). There is increasing interest in risk-stratified and precision surveillance strategies incorporating individual risk and variance in surveillance test performance; however, the cost-effectiveness of these approaches has not been evaluated.

We conducted a cost-effectiveness analysis to evaluate four surveillance strategies (no surveillance, universal US + AFP surveillance, risk-stratified surveillance, and precision surveillance) in a simulated cohort of 50-year-old patients with compensated cirrhosis. The most cost-effective strategy was that with the highest incremental cost-effectiveness ratio (ICER) and below the willingness-to-pay (WTP) threshold of $150,000/QALY gained. Model inputs were based on literature review, and costs were derived from the Medicare fee schedule.

The precision surveillance strategy demonstrated variation in recommended surveillance test based on HCC risk category and patient factors. US + AFP, risk-stratified, and precision surveillance detected more HCC cases per 100,000 population than no surveillance, with a higher proportion of early-stage cases for precision surveillance (67.6%) than risk-stratified (63.8%), universal ultrasound (63.2%), and no surveillance (38.0%). Compared to no surveillance, precision surveillance was most cost-effective, with an ICER of $104,614/QALY gained, whereas US + AFP and risk-stratified surveillance were both dominated. Compared to US + AFP, risk-stratified surveillance was cost saving and dominated US + AFP, whereas precision surveillance was cost-effective, with an ICER of $98,103/QALY gained. Results were sensitive to survival with early-stage HCC, cost of early-stage HCC treatment, and surveillance utilization. Precision surveillance remained the most cost-effective when WTP thresholds exceeded $110,000/QALY gained.

A precision surveillance strategy is the most cost-effective method for HCC surveillance. This approach could maximize surveillance benefits in high-risk patients, while minimizing surveillance harms in low-risk individuals.

National Cancer Institute (U01 CA230694, R01 CA222900, R01 CA212008, and U24ca086368) and Cancer Prevention Research Institute of Texas (CPRIT) (RP200554).

To explore the capability of diffusion-based virtual MR elastography (vMRE) in the preoperative prediction of recurrence in hepatocellular carcinoma (HCC) and to investigate the underlying relevant histopathological characteristics.

Between August 2015 and December 2016, patients underwent preoperative MRI examination with a dedicated DWI sequence (b-values: 200,1500 s/mm2) were recruited. The ADC values and diffusion-based virtual shear modulus (μdiff) of HCCs were calculated and MR morphological features were also analyzed. The Cox proportional hazards model was used to identify the risk factors associated with tumor recurrence. A preoperative radiologic model and postoperative model including pathological features were built to predict tumor recurrence after hepatectomy.

A total of 87 patients with solitary surgically confirmed HCCs were included in this study. Thirty-five patients (40.2%) were found to have tumor recurrence after hepatectomy. The preoperative model included higher μdiff and corona enhancement, while the postoperative model included higher μdiff, microvascular invasion, and histologic tumor grade. These factors were identified as significant prognostic factors for recurrence-free survival (RFS) (all p < 0.05). The HCC patients with μdiff values > 2.325 kPa showed poorer 5-year RFS after hepatectomy than patients with μdiff values ≤ 2.325 kPa (p < 0.001). Moreover, the higher μdiff values was correlated with the expression of CK19 (3.95 ± 2.37 vs. 3.15 ± 1.77, p = 0.017) and high Ki-67 labeling index (4.22 ± 1.63 vs. 2.72 ± 2.12, p = 0.001).

The μdiff values related to the expression of CK19 and Ki-67 labeling index potentially predict RFS after hepatectomy in HCC patients.

Although systemic therapies are recommended for hepatocellular carcinoma (HCC) patients with main portal vein (MPV) invasion and preserved liver function, the outcome is limited. In the real-world, chemoembolization is a commonly used local treatment for advanced HCC.

To evaluate whether the additional chemoembolization treatment yields survival benefits compared to systemic therapy for HCC patients with MPV invasion and preserved liver function (Child-Pugh score ≤ B7) in a real-world study from multiple centers.

Between January 2020 and December 2022, 91 consecutive HCC patients with MPV invasion who received either systemic medical therapy (i.e., tyrosine kinase inhibitors (TKIs) plus anti-PD-1 immunotherapy, S group, n = 43) or in combination with chemoembolization treatment (S-T group, n = 48) from five centers were enrolled in the study. The primary outcome was overall survival (OS), and the secondary outcomes were progression-free survival (PFS) and treatment response. Adverse events (AEs) related to treatment were also recorded. Survival curves were constructed with the Kaplan-Meier method and compared using the log-rank test.

The baseline characteristics were comparable between the two groups. The mean number of chemoembolization sessions per patient was 2.1 (range 1-3). The median OS was 10.0 months and 8.0 months in the S-T group and S group, respectively (P = 0.254). The median PFS between the two groups was similar (4.0 months vs. 4.0 months, P = 0.404). The disease control rate between the S-T and S groups were comparable (60.4% vs. 62.8%, P = 0.816). Although no chemoembolization-related deaths occurred, 13 grade 3-4 AEs occurred in the S-T group.

The results of the real-world study demonstrated that additional chemoembolization treatment did not yield survival benefits compared to TKIs plus anti-PD-1 immunotherapy for the overall patients with advanced HCC and MPV invasion.

Liver transplantation is an effective treatment for preventing hepatocellular carcinoma (HCC) recurrence. This retrospective study aimed to quantitatively evaluate the attenuation in Hounsfield units (HU) on contrast-enhanced computed tomography (CECT) as a prognostic factor for hepatocellular carcinoma (HCC) following liver transplantation as a treatment. Our goal is to optimize its predictive ability for early tumor recurrence and compare it with the other imaging modality-positron emission tomography (PET).

In 618 cases of LDLT for HCC, only 131 patients with measurable viable HCC on preoperative CECT and preoperative positron emission tomography (PET) evaluations were included, with a minimum follow-up period of 6 years. Cox regression models were developed to identify predictors of postoperative recurrence. Performance metrics for both CT and PET were assessed. The correlation between these two imaging modalities was also evaluated. Survival analyses were conducted using time-dependent receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) to assess accuracy and determine optimized cut-off points.

Univariate and multivariate analyses revealed that both arterial-phase preoperative tumor attenuation (HU) and PET were independent prognostic factors for recurrence-free survival. Both lower arterial tumor enhancement (Cut-off value = 59.2, AUC 0.88) on CT and PET positive (AUC 0.89) increased risk of early tumor recurrence 0.5-year time-dependent ROC. Composites with HU < 59.2 and a positive PET result exhibited significantly higher diagnostic accuracy in detecting early tumor recurrence (AUC = 0.96).

Relatively low arterial tumor enhancement values on CECT effectively predict early HCC recurrence after LDLT. The integration of CT and PET imaging may serve as imaging markers of early tumor recurrence in HCC patients after LDLT.

To evaluate whether sarcopenia, diagnosed by magnetic resonance imaging (MRI) protocol, constitutes a prognosis-associated risk factor in patients with hepatocellular carcinoma (HCC) after hepatectomy.

One hundred and ninety-three patients who underwent hepatectomy for HCC were retrospectively enrolled. The areas of the total skeletal muscle (SM) and psoas muscle (PM) were evaluated at the third lumbar vertebra in the preoperative MR images, and divided by the square of height in order to obtain the skeletal muscle index (SMI) and psoas muscle mass index (PMI). Sarcopenia was diagnosed respectively on the definitions based on the SMI or PMI. The potential of muscle-defined sarcopenia as a prognostic factor for overall survival (OS) and recurrence-free survival (RFS) was investigated in these patients.

The areas of SM and PM, and SMI and PMI were significantly higher in the men than in the women (all p < 0.05). Notably, SMI-defined sarcopenia displayed a significant sex difference (p = 0.003), while PMI-defined sarcopenia did not (p = 0.370). Through univariate and multivariate analyses, PMI-defined sarcopenia remained an independent predictor for OS and RFS (HR = 3.486, 95% CI: 1.700-7.145, p = 0.001 and HR = 1.993, 95% CI: 1.246-3.186, p = 0.004), even after adjusting for other clinical variables. Moreover, Kaplan-Meier analysis demonstrated significantly poorer OS and RFS for patients with sarcopenia defined by using PMI, but not SMI, compared to those without sarcopenia (p < 0.001 and p = 0.006, respectively).

MRI-derived, sarcopenia defined by using PMI, not SMI, may serve as a significant risk factor for RFS and OS in patients with HCC after hepatectomy.

Combined 18F-fluorodeoxyglucose (FDG) and 11C-acetate (dual-tracer) positron-emission tomography/computed tomography (PET/CT) is being increasingly performed for the management of hepatocellular carcinoma (HCC), although its role is not well defined. Therefore, we evaluated its effectiveness in (i) staging, (ii) characterization of indeterminate lesions on conventional imaging, and (iii) detection of HCC in patients with unexplained elevations in serum alpha-fetoprotein (AFP) levels.

We retrospectively assessed 525 consecutive patients from three tertiary centers between 2014 and 2020. For staging, we recorded new lesion detection rates, changes in the Barcelona Clinic Liver Cancer (BCLC) classification, and treatment allocation due to dual-tracer PET/CT. To characterize indeterminate lesions and unexplained elevation of serum AFP levels, the sensitivity and specificity of dual-tracer PET/CT in diagnosing HCC were evaluated. A multidisciplinary external review and a cost-benefit analysis of patients for metastatic screening were also performed.

Dual-tracer PET/CT identified new lesions in 14.3% of 273 staging patients, resulting in BCLC upstaging in 11.7% and treatment modifications in 7.7%. It upstaged 8.1% of 260 patients undergoing metastatic screening, with estimated savings of US$495 per patient. It had a sensitivity and specificity of 80.7% (95% CI 71.2-88.6%) and 94.8% (95% CI 90.4-98.6%), respectively, for diagnosing HCC in 201 indeterminate lesions. It detected HCC in 45.1% of 51 patients with unexplained elevations in serum AFP concentrations. External review revealed substantial agreement between local and external image interpretation and patient assessment (n = 273, κ = 0.822; 95% CI 0.803-0.864).

Dual-tracer PET/CT provides added value beyond conventional imaging in patients with HCC by improving staging, confirming HCC diagnosis with high accuracy in patients with indeterminate lesions, and detecting HCC in patients with unexplained elevation of serum AFP.

Compared to CT or MRI, dual-tracer positron-emission tomography/computed tomography (PET/CT) led to upstaging in 12% of patients with hepatocellular carcinoma (HCC) undergoing staging, resulting in treatment modification in 8% of cases and a cost saving of US$495 per patient. It also accurately detected HCC in high-risk cases where CT or MRI were equivocal or normal. Dual-tracer PET/CT provides added value beyond conventional imaging in patients with HCC by improving staging, confirming HCC diagnosis with high accuracy in patients with indeterminate lesions, and detecting HCC in patients with unexplained elevation of serum AFP.

To compare the ability to depict MRI features of hepatobiliary agents in microvascular infiltration (MVI) of hepatocellular carcinoma (HCC) during different stages of dynamic enhancement MRI.

A retrospective study included 111 HCC lesions scanned with either Gd-EOB-DTPA or Gd-BOPTA. All cases underwent multiphase dynamic contrast-enhanced scanning before surgery, including arterial phase (AP), portal venous phase (PVP), transitional phase (TP), delayed phase (DP), and hepatobiliary phase (HBP). Two abdominal radiologists independently evaluated MRI features of MVI in HCC, such as peritumoral hyperenhancement, incomplete capsule, non-smooth tumor margins, and peritumoral hypointensity. Finally, the results were reviewed by the third senior abdominal radiologist. Chi-square (χ2) Inspection for comparison between groups. P < 0.05 is considered statistically significant. Receiver operating characteristic (ROC) curve was used to evaluate correlation with pathology, and the area under the curve (AUC) and 95% confidence interval (95% CI) were calculated.

Among the four MVI evaluation signs, Gd-BOPTA showed significant differences in displaying two signs in the HBP (P < 0.05:0.000, 0.000), while Gd-EOB-DTPA exhibited significant differences in displaying all four signs (P < 0.05:0.005, 0.006, 0.000, 0.002). The results of the evaluations of the two contrast agents in the DP phase with incomplete capsulation showed the highest correlation with pathology (AUC: 0.843, 0.761). By combining the four MRI features, Gd-BOPTA and Gd-EOB-DTPA have correlated significantly with pathology, and Gd-BOPTA is better (AUC: 0.9312vs0.8712).

The four features of hepatobiliary agent dynamic enhancement MRI demonstrate a good correlation with histopathological findings in the evaluation of MVI in HCC, and have certain clinical significance.

Objective.In oncology, clinical decision-making relies on a multitude of data modalities, including histopathological, radiological, and clinical factors. Despite the emergence of computer-aided multimodal decision-making systems for predicting hepatocellular carcinoma (HCC) recurrence post-hepatectomy, existing models often employ simplistic feature-level concatenation, leading to redundancy and suboptimal performance. Moreover, these models frequently lack effective integration with clinically relevant data and encounter challenges in integrating diverse scales and dimensions, as well as incorporating the liver background, which holds clinical significance but has been previously overlooked.Approach.To address these limitations, we propose two approaches. Firstly, we introduce the tensor fusion method to our model, which offers distinct advantages in handling multi-scale and multi-dimensional data fusion, potentially enhancing overall performance. Secondly, we pioneer the consideration of the liver background's impact, integrating it into the feature extraction process using a deep learning segmentation-based algorithm. This innovative inclusion aligns the model more closely with real-world clinical scenarios, as the liver background may contain crucial information related to postoperative recurrence.Main results.We collected radiomics (MRI) and histopathological images from 176 cases diagnosed by experienced clinicians across two independent centers. Our proposed network underwent training and 5-fold cross-validation on this dataset before validation on an external test dataset comprising 40 cases. Ultimately, our model demonstrated outstanding performance in predicting early recurrence of HCC postoperatively, achieving an AUC of 0.883.Significance.These findings signify significant progress in addressing challenges related to multimodal data fusion and hold promise for more accurate clinical outcome predictions. In this study, we exploited global 3D liver background into modelling which is crucial to to the prognosis assessment and analyzed the whole liver background in addition to the tumor region. Both MRI images and histopathological images of HCC were fused at high-dimensional feature space using tensor techniques to solve cross-scale data integration issue.

To evaluated the impact of a deep learning (DL)-based image reconstruction on multi-arterial-phase magnetic resonance imaging (MA-MRI) for small hypervascular hepatic masses in patients who underwent gadoxetic acid-enhanced liver MRI.

We retrospectively enrolled 55 adult patients (aged ≥ 18 years) with small hepatic hypervascular mass (≤ 3 cm) between December 2022 and February 2023. All patients underwent MA-MRI, subsequently reconstructed with a DL-based application. Qualitative assessment with Linkert scale including motion artifact (MA), liver edge (LE), hepatic vessel clarity (HVC) and image quality (IQ) was performed. Quantitative image analysis including signal to noise ratio (SNR), contrast to noise ratio (CNR) and noise was performed.

On both arterial phases (APs), all qualitative parameters were significantly improved after DL-based image reconstruction. (LE on 1st AP, 1.22 vs 1.61; LE on 2nd AP, 1.21 vs 1.65; HVC on 1st AP, 1.24 vs 1.39; HVC on 2nd AP, 1.24 vs 1.44; IQ on 1st AP, 1.17 vs 1.45; IQ on 2nd AP, 1.17 vs 1.47, all p values < 0.05). The SNR, CNR and noise were significantly improved after DL-based image reconstruction. (SNR on AP1, 279.08 vs 176.14; SNR on AP2, 334.34 vs 199.24; CNR on AP1, 106.09 vs 64.14; CNR on AP2, 129.66 vs 73.73; noise on AP1, 1.51 vs 2.33; noise on AP2, 1.45 vs 2.28, all p values < 0.05).

Gadoxetic acid-enhanced MA-MRI with DL-based image reconstruction improved the qualitative and quantitative parameters. Despite the short acquisition time, high-quality MA-MRI is now achievable.

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer globally, poses a substantial health burden. Influenced by risk factors such as hepatitis B or C virus infections, chronic consumption of alcohol, and metabolic dysfunction, its exact etiology likely involves a complex interplay between viral infection, hepatocyte mutations, and chronic liver diseases like cirrhosis and metabolic dysfunction-associated steatohepatitis, and demographic variables like sex, race, and age. Disease stage significantly impacts the prognosis of HCC. There is significant potential for life-saving and socioeconomic benefits through the implementation of surveillance programs and the introduction of low-cost screening measures for high-risk groups; these screening measures include ultrasound imaging and blood tests. Treatment options for HCC encompass liver resection, transplantation, transarterial chemoembolization, radiation therapy, chemotherapy, targeted therapy, and immunotherapy. Despite therapeutic advances, treating advanced HCC remains challenging, emphasizing the need for continued efforts in prevention, early detection, and development of treatments to improve prognosis and long-term survival.

We conducted a comprehensive review of the current literature of published data and clinical trials (MEDLINE), as well as published congress contributions and active recruiting clinical trials on targeted therapies in hepatocellular carcinoma. Combinations of different agents and medical therapy along with radiological interventions were analyzed for the setting of advanced HCC. Those settings were also analyzed in combination with adjuvant situations after resection or radiological treatments. We summarized the current knowledge for each therapeutic setting and combination that currently is or has been under clinical evaluation. We further discuss the results in the background of current treatment guidelines. In addition, we review the pathophysiological mechanisms and pathways for each of these investigated targets and drugs to further elucidate the molecular background and underlying mechanisms of action. Established and recommended targeted treatment options that already exist for patients are considered for systemic treatment: atezolizumab/bevacizumab, durvalumab/tremelimumab, sorafenib, lenvatinib, cabozantinib, regorafenib, and ramucirumab. Combination treatment for systemic treatment and local ablative treatment or transarterial chemoembolization and adjuvant and neoadjuvant treatment strategies are under clinical investigation.

To investigate the consistency of LI-RADS of CEUS and EOB-MRI in the categorization of liver nodules ≤2cm in patients at high risk for HCC.

Patients at high risk for HCC with nodules ≤2cm who underwent CEUS and EOB-MRI in our hospital were prospectively enrolled. The CEUS images and EOB-MRI imaging of each liver nodule were observed to evaluate inter-observer consistency and category according to CEUS LI-RADS V2017 and CT/MRI LI-RADS V2017 criteria double blinded. Pathology and/or follow-up were used as reference standard.

A total of 127 nodules in 119 patients met the inclusion criteria. The inter-observer agreement was good on CEUS and EOB-MRI LI-RADS (kappa = 0.76, 0.76 p < 0.001). The inter-modality agreement was fair (kappa=0.21, p < 0.001). There was no statistical difference in PPV and specificity between CEUS and EOB-MRI LR-5 for HCC, while the difference in AUC was statistically significant. We used new criteria (CEUS LR-5 and EOB-MRI LR-4/5 or CEUS LR-4/5 and EOB-MRI LR-5) to diagnose HCC. The sensitivity, specificity, and AUC of this criteria was 63.4%, 95.6%, and 0.80.

CEUS and EOB-MRI showed fair inter-modality agreement in LI-RADS categorization of nodules ≤2 cm. The inter-observer agreement of CEUS and EOB-MRI LI-RADS were substantial. CEUS and EOB-MRI LR-5 have equally good positive predictive value and specificity for HCC ≤ 2cm, and combining these two modalities may better diagnose HCC ≤ 2 cm.

https://clinicaltrials.gov/, identifier NCT04212286.

Indentifying predictive factors for postoperative recurrence of hepatocellular carcinoma (HCC) has great significance for patient prognosis.

To explore the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) combined with clinical features in predicting early recurrence of HCC after resection.

A total of 161 patients with pathologically confirmed HCC were enrolled. The patients were divided into early recurrence and non-early recurrence group based on the follow-up results. The clinical, laboratory, pathological results and Gd-EOB-DTPA enhanced MRI imaging features were analyzed.

Of 161 patients, 73 had early recurrence and 88 were had non-early recurrence. Univariate analysis showed that patient age, gender, serum alpha-fetoprotein level, the Barcelona Clinic Liver Cancer stage, China liver cancer (CNLC) stage, microvascular invasion (MVI), pathological satellite focus, tumor size, tumor number, tumor boundary, tumor capsule, intratumoral necrosis, portal vein tumor thrombus, large vessel invasion, nonperipheral washout, peritumoral enhancement, hepatobiliary phase (HBP)/tumor signal intensity (SI)/peritumoral SI, HBP peritumoral low signal and peritumoral delay enhancement were significantly associated with early recurrence of HCC after operation. Multivariate logistic regression analysis showed that patient age, MVI, CNLC stage, tumor boundary and large vessel invasion were independent predictive factors. External data validation indicated that the area under the curve of the combined predictors was 0.861, suggesting that multivariate logistic regression was a reasonable predictive model for early recurrence of HCC.

Gd-EOB-DTPA enhanced MRI combined with clinical features would help predicting the early recurrence of HCC after operation.

[18F] Fluorodeoxyglucose (18F-FDG) PET/CT can improve the staging accuracy and clinical management of patients with hepatobiliary and pancreatic cancers, by detection of unsuspected metastases. 18F-FDG PET/CT metabolic parameters are valuable in predicting treatment response and survival. Metabolic response on 18F-FDG PET/CT can predict preoperative pathologic response to neoadjuvant therapy in patients with pancreatic cancer and determine prognosis. Several novel non-FDG tracers, such as 68Ga prostate-specific membrane antigen (PSMA) and 68Ga-fibroblast activation protein inhibitor (FAPI) PET/CT, show promise for imaging hepatobiliary and pancreatic cancers with potential for radioligand therapy.

To evaluate intra-patient and interobserver agreement in patients who underwent liver MRI with gadoxetic acid using two different multi-arterial phase (AP) techniques.

A total of 154 prospectively enrolled patients underwent clinical gadoxetic acid-enhanced liver MRI twice within 12 months, using two different multi-arterial algorithms: CAIPIRINHA-VIBE and TWIST-VIBE. For every patient, breath-holding time, body mass index, sex, age were recorded. The phase without contrast media and the APs were independently evaluated by two radiologists who quantified Gibbs artefacts, noise, respiratory motion artefacts, and general image quality. Presence or absence of Gibbs artefacts and noise was compared by the McNemar's test. Respiratory motion artefacts and image quality scores were compared using Wilcoxon signed rank test. Interobserver agreement was assessed by Cohen kappa statistics.

Compared with TWIST-VIBE, CAIPIRINHA-VIBE images had better scores for every parameter except higher noise score. Triple APs were always acquired with TWIST-VIBE but failed in 37% using CAIPIRINHA-VIBE: 11% have only one AP, 26% have two. Breath-holding time was the only parameter that influenced the success of multi-arterial techniques. TWIST-VIBE images had worst score for Gibbs and respiratory motion artefacts but lower noise score.

CAIPIRINHA-VIBE images were always diagnostic, but with a failure of triple-AP in 37%. TWIST-VIBE was successful in obtaining three APs in all patients. Breath-holding time is the only parameter which can influence the preliminary choice between CAIPIRINHA-VIBE and TWIST-VIBE algorithm.

If the patient is expected to perform good breath-holds, TWIST-VIBE is preferable; otherwise, CAIPIRINHA-VIBE is more appropriate.

Large animal models are still used in many studies because of their likeness to humans. It has not been documented that regular-sized conventional farm-breed pigs, generally bred for meat production, can be used to generate hepatocellular carcinoma (HCC) animal models. The goal of this study was to investigate how N-diethylnitrosamine (DENA) and phenobarbital (PB) together can generate HCC in ordinary farmed pigs.

Conventional domestic swine (Sus scrofa domesticus) were used. DENA 15 mg/kg was intraperitoneally injected weekly for 12 weeks, while PB tablets (4 mg/kg) were also administered through food for 16 weeks. Blood testing and ultrasonography evaluation were performed to monitor the progress. Subsequently, computed tomography was conducted in cases with suspected nodules, followed by histopathological examination to confirm the diagnosis.

Ten swine (seven males, three females; age: 2 months; weight: 9-15 kg) were included in the study and followed up for 25 months; nine were experimental, and one was control for ethical considerations. The maximum weight of animals during this study reached 162-228 kg. The weight gain seen in the intervention swine was predominantly lower than that documented in the control. The laboratory analysis revealed no notable abnormalities in liver function markers but did demonstrate statistically significant changes in urea (p = 0.028) and creatinine (p = 0.003) levels. Ultrasonography and computed tomography showed multiple liver nodules with characteristics resembling HCC. Serial imaging screening and more extended observations revealed that all animals eventually developed tumors. Histopathological confirmation at 15-22 weeks post-induction revealed that all intervened swine developed multiple nodules of well-differentiated HCC and some with hepatic angiosarcoma.

This study successfully generated HCC in conventional domestic swine with a DENA and PB combination. This investigation required at least 15 months to develop tumors. This model will be beneficial for future investigations of HCC in large animals.

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.