Tumor response assessment in hepatocellular carcinoma treated with immunotherapy: imaging biomarkers for clinical decision-making.

OBJECTIVE: To compare the performance of 1D and 3D tumor response assessment for predicting median overall survival (mOS) in patients who underwent immunotherapy for hepatocellular carcinoma (HCC). METHODS: Patients with HCC who underwent immunotherapy between 2017 and 2023 and received multi-phasic contrast-enhanced MRIs pre- and post-treatment were included in this retrospective study. Tumor response was measured using 1D, RECIST 1.1, and mRECIST, and 3D, volumetric, and percentage quantitative EASL (vqEASL and %qEASL). Patients were grouped into disease control vs progression and responders vs non-responders. Kaplan-Meier curves analyzed with log-rank tests assessed the predictive value for mOS. Cox regression modeling evaluated the association of clinical baseline parameters with mOS. RESULTS: This study included 37 patients (mean age, 69.1 years [SD, 8.0]; 33 men). The mOS was 16.9 months. 3D vqEASL and %qEASL successfully stratified patients into disease control and progression (vqEASL: HR 0.21, CI: 0.55-0.08, p < 0.001; %qEASL: HR 0.18, CI: 0.83-0.04, p = 0.013), as well as responder and nonresponder (vqEASL: HR 0.25, CI: 0.08-0.74, p = 0.007; %qEASL: HR 0.17, CI: 0.04-0.72, p = 0.007) for predicting mOS. The 1D criteria, mRECIST stratified into disease control and progression only (HR 0.24, CI: 0.65-0.09, p = 0.002), and RECIST 1.1 showed no predictive value in either stratification. Multivariate Cox regression identified alpha-fetoprotein > 500 ng/mL as a predictor for poor mOS (p = 0.04). CONCLUSION: The 3D quantitative enhancement-based response assessment tool qEASL can predict overall survival in patients undergoing immunotherapy for HCC and could identify non-responders. CLINICAL RELEVANCE STATEMENT: Using 3D quantitative enhancement-based tumor response criteria (qEASL), radiologists' predictions of tumor response in patients undergoing immunotherapy for HCC can be further improved. KEY POINTS: MRI-based tumor response criteria predict immunotherapy survival benefits in HCC patients. 3D tumor response assessment methods surpass current evaluation criteria in predicting overall survival during HCC immunotherapy. Enhancement-based 3D tumor response criteria are robust prognosticators of survival for HCC patients on immunotherapy.

Outcomes of Repeat Conventional Transarterial Chemoembolization in Patients with Liver Metastases.

INTRODUCTION AND OBJECTIVES: Although unlimited sessions of conventional transarterial chemoembolization (cTACE) may be performed for liver metastases, there is no data indicating when treatment becomes ineffective. This study aimed to determine the optimal number of repeat cTACE sessions for nonresponding patients before abandoning cTACE in patients with liver metastases. MATERIALS AND METHODS: In this retrospective, single-institutional analysis, patients with liver metastases from neuroendocrine tumors (NET), colorectal carcinoma (CRC), and lung cancer who underwent consecutive cTACE sessions from 2001 to 2015 were studied. Quantitative European Association for Study of the Liver (qEASL) criteria were utilized for response assessment. The association between the number of cTACE and 2-year, 5-year, and overall survival was evaluated to estimate the optimal number of cTACE for each survival outcome. RESULTS: Eighty-five patients underwent a total of 186 cTACE sessions for 117 liver metastases, of which 30.7% responded to the first cTACE. For the target lesions that did not respond to the first, second, and third cTACE sessions, response rates after the second, third, and fourth cTACE sessions were 33.3%, 23%, and 25%, respectively. The fourth cTACE session was the optimal number for 2-year survival (HR 0.40; 95%CI: 0.16-0.97; p=0.04), 5-year survival (HR 0.31; 95%CI: 0.11-0.87; p=0.02), and overall survival (HR 0.35; 95%CI: 0.13-0.89; p=0.02). CONCLUSIONS: Repeat cTACE in the management of liver metastases from NET, CRC, and lung cancer was associated with improved patient survival. We recommend at least four cTACE sessions before switching to another treatment for nonresponding metastatic liver lesions.

Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning.

BACKGROUND: Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective. PURPOSE: To develop and independently validate a machine learning mortality risk quantification method for HCC patients using standard-of-care clinical data and liver radiomics on baseline magnetic resonance imaging (MRI). METHODS: This retrospective study included all patients with multiphasic contrast-enhanced MRI at the time of diagnosis treated at our institution. Patients were censored at their last date of follow-up, end-of-observation, or liver transplantation date. The data were randomly sampled into independent cohorts, with 85% for development and 15% for independent validation. An automated liver segmentation framework was adopted for radiomic feature extraction. A random survival forest combined clinical and radiomic variables to predict overall survival (OS), and performance was evaluated using Harrell's C-index. RESULTS: A total of 555 treatment-naïve HCC patients (mean age, 63.8 years ± 8.9 [standard deviation]; 118 females) with MRI at the time of diagnosis were included, of which 287 (51.7%) died after a median time of 14.40 (interquartile range, 22.23) months, and had median followed up of 32.47 (interquartile range, 61.5) months. The developed risk prediction framework required 1.11 min on average and yielded C-indices of 0.8503 and 0.8234 in the development and independent validation cohorts, respectively, outperforming conventional clinical staging systems. Predicted risk scores were significantly associated with OS (p < .00001 in both cohorts). CONCLUSIONS: Machine learning reliably, rapidly, and reproducibly predicts mortality risk in patients with hepatocellular carcinoma from data routinely acquired in clinical practice. CLINICAL RELEVANCE STATEMENT: Precision mortality risk prediction using routinely available standard-of-care clinical data and automated MRI radiomic features could enable personalized follow-up strategies, guide management decisions, and improve clinical workflow efficiency in tumor boards. KEY POINTS: • Machine learning enables hepatocellular carcinoma mortality risk prediction using standard-of-care clinical data and automated radiomic features from multiphasic contrast-enhanced MRI. • Automated mortality risk prediction achieved state-of-the-art performances for mortality risk quantification and outperformed conventional clinical staging systems. • Patients were stratified into low, intermediate, and high-risk groups with significantly different survival times, generalizable to an independent evaluation cohort.

High-resolution extracellular pH imaging of liver cancer with multiparametric MR using Deep Image Prior.

Noninvasive extracellular pH (pHe) mapping with Biosensor Imaging of Redundant Deviation in Shifts (BIRDS) using MR spectroscopic imaging (MRSI) has been demonstrated on 3T clinical MR scanners at 8 × 8 × 10 mm3 spatial resolution and applied to study various liver cancer treatments. Although pHe imaging at higher resolution can be achieved by extending the acquisition time, a postprocessing method to increase the resolution is preferable, to minimize the duration spent by the subject in the MR scanner. In this work, we propose to improve the spatial resolution of pHe mapping with BIRDS by incorporating anatomical information in the form of multiparametric MRI and using an unsupervised deep-learning technique, Deep Image Prior (DIP). Specifically, we used high-resolution T 1 , T 2 , and diffusion-weighted imaging (DWI) MR images of rabbits with VX2 liver tumors as inputs to a U-Net architecture to provide anatomical information. U-Net parameters were optimized to minimize the difference between the output super-resolution image and the experimentally acquired low-resolution pHe image using the mean-absolute error. In this way, the super-resolution pHe image would be consistent with both anatomical MR images and the low-resolution pHe measurement from the scanner. The method was developed based on data from 49 rabbits implanted with VX2 liver tumors. For evaluation, we also acquired high-resolution pHe images from two rabbits, which were used as ground truth. The results indicate a good match between the spatial characteristics of the super-resolution images and the high-resolution ground truth, supported by the low pixelwise absolute error.

Effect of Incomplete Cryoablation and Matrix Metalloproteinase Inhibition on Intratumoral CD8+ T-Cell Infiltration in Murine Hepatocellular Carcinoma.

Background Image-guided tumor ablation is the first-line therapy for early-stage hepatocellular carcinoma (HCC), with ongoing investigations into its combination with immunotherapies. Matrix metalloproteinase (MMP) inhibition demonstrates immunomodulatory potential and reduces HCC tumor growth when combined with ablative treatment. Purpose To evaluate the effect of incomplete cryoablation with or without MMP inhibition on the local immune response in residual tumors in a murine HCC model. Materials and Methods Sixty 8- to 10-week-old female BALB/c mice underwent HCC induction with use of orthotopic implantation of syngeneic Tib-75 cells. After 7 days, mice with a single lesion were randomized into treatment groups: (a) no treatment, (b) MMP inhibitor, (c) incomplete cryoablation, and (d) incomplete cryoablation and MMP inhibitor. Macrophage and T-cell subsets were assessed in tissue samples with use of immunohistochemistry and immunofluorescence (cell averages calculated using five 1-µm2 fields of view [FOVs]). C-X-C motif chemokine receptor type 3 (CXCR3)- and interferon γ (IFNγ)-positive T cells were assessed using flow cytometry. Groups were compared using unpaired Student t tests, one-way analysis of variance with Tukey correction, and the Kruskal-Wallis test with Dunn correction. Results Mice treated with incomplete cryoablation (n = 6) showed greater infiltration of CD206+ tumor-associated macrophages (mean, 1.52 cells per FOV vs 0.64 cells per FOV; P = .03) and MMP9-expressing cells (mean, 0.89 cells per FOV vs 0.11 cells per FOV; P = .03) compared with untreated controls (n = 6). Incomplete cryoablation with MMP inhibition (n = 6) versus without (n = 6) led to greater CD8+ T-cell (mean, 15.8% vs 8.29%; P = .04), CXCR3+CD8+ T-cell (mean, 11.64% vs 8.47%; P = .004), and IFNγ+CD8+ T-cell infiltration (mean, 11.58% vs 5.18%; P = .02). Conclusion In a mouse model of HCC, incomplete cryoablation and systemic MMP inhibition showed increased cytotoxic CD8+ T-cell infiltration into the residual tumor compared with either treatment alone. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Gemmete in this issue.

Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics.

OBJECTIVES: To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on contrast-enhanced portal venous phase magnetic resonance imaging (MRI). MATERIALS AND METHODS: This retrospective study included hepatocellular carcinoma patients from an institutional database with portal venous MRI. After manual segmentation, the data was randomly split into independent training, validation, and internal testing sets. From a collaborating institution, de-identified scans were used for external testing. The public LiverHccSeg dataset was used for further external validation. A 3D DCNN was trained to automatically segment the liver. Segmentation accuracy was quantified by the Dice similarity coefficient (DSC) with respect to manual segmentation. A Mann-Whitney U test was used to compare the internal and external test sets. Agreement of volumetry and radiomic features was assessed using the intraclass correlation coefficient (ICC). RESULTS: In total, 470 patients met the inclusion criteria (63.9±8.2 years; 376 males) and 20 patients were used for external validation (41±12 years; 13 males). DSC segmentation accuracy of the DCNN was similarly high between the internal (0.97±0.01) and external (0.96±0.03) test sets (p=0.28) and demonstrated robust segmentation performance on public testing (0.93±0.03). Agreement of liver volumetry was satisfactory in the internal (ICC, 0.99), external (ICC, 0.97), and public (ICC, 0.85) test sets. Radiomic features demonstrated excellent agreement in the internal (mean ICC, 0.98±0.04), external (mean ICC, 0.94±0.10), and public (mean ICC, 0.91±0.09) datasets. CONCLUSION: Automated liver segmentation yields robust and generalizable segmentation performance on MRI data and can be used for volumetry and radiomic feature extraction. CLINICAL RELEVANCE STATEMENT: Liver volumetry, anatomic localization, and extraction of quantitative imaging biomarkers require accurate segmentation, but manual segmentation is time-consuming. A deep convolutional neural network demonstrates fast and accurate segmentation performance on T1-weighted portal venous MRI. KEY POINTS: • This deep convolutional neural network yields robust and generalizable liver segmentation performance on internal, external, and public testing data. • Automated liver volumetry demonstrated excellent agreement with manual volumetry. • Automated liver segmentations can be used for robust and reproducible radiomic feature extraction.

An Interventional Radiologist's Primer of Critical Appraisal of Artificial Intelligence Research.

Recent advances in artificial intelligence (AI) are expected to cause a significant paradigm shift in all digital data-driven aspects of information gain, processing, and decision making in both clinical healthcare and medical research. The field of interventional radiology (IR) will be enmeshed in this innovation, yet the collective IR expertise in the field of AI remains rudimentary because of lack of training. This primer provides the clinical interventional radiologist with a simple guide for critically appraising AI research and products by identifying 12 fundamental items that should be considered: (a) need for AI technology to address the clinical problem, (b) type of applied Al algorithm, (c) data quality and degree of annotation, (d) reporting of accuracy, (e) applicability of standardized reporting, (f) reproducibility of methodology and data transparency, (g) algorithm validation, (h) interpretability, (i) concrete impact on IR, (j) pathway toward translation to clinical practice, (k) clinical benefit and cost-effectiveness, and (l) regulatory framework.

Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma.

Interventional oncology is a rapidly growing field with advances in minimally invasive image-guided local-regional treatments for hepatocellular carcinoma (HCC), including transarterial chemoembolization, transarterial radioembolization, and thermal ablation. However, current standardized clinical staging systems for HCC are limited in their ability to optimize patient selection for treatment as they rely primarily on serum markers and radiologist-defined imaging features. Given the variation in treatment responses, an updated scoring system that includes multidimensional aspects of the disease, including quantitative imaging features, serum markers, and functional biomarkers, is needed to optimally triage patients. With the vast amounts of numerical medical record data and imaging features, researchers have turned to image-based methods, such as radiomics and artificial intelligence (AI), to automatically extract and process multidimensional data from images. The synthesis of these data can provide clinically relevant results to guide personalized treatment plans and optimize resource utilization. Machine learning (ML) is a branch of AI in which a model learns from training data and makes effective predictions by teaching itself. This review article outlines the basics of ML and provides a comprehensive overview of its potential value in the prediction of treatment response in patients with HCC after minimally invasive image-guided therapy.

Ethiodized oil as an imaging biomarker after conventional transarterial chemoembolization.

Conventional transarterial chemoembolization (cTACE) utilizing ethiodized oil as a chemotherapy carrier has become a standard treatment for intermediate-stage hepatocellular carcinoma (HCC) and has been adopted as a bridging and downstaging therapy for liver transplantation. Water-in-oil emulsion made up of ethiodized oil and chemotherapy solution is retained in tumor vasculature resulting in high tissue drug concentration and low systemic chemotherapy doses. The density and distribution pattern of ethiodized oil within the tumor on post-treatment imaging are predictive of the extent of tumor necrosis and duration of response to treatment. This review describes the multiple roles of ethiodized oil, particularly in its role as a biomarker of tumor response to cTACE. CLINICAL RELEVANCE: With the increasing complexity of locoregional therapy options, including the use of combination therapies, treatment response assessment has become challenging; Ethiodized oil deposition patterns can serve as an imaging biomarker for the prediction of treatment response, and perhaps predict post-treatment prognosis. KEY POINTS: • Treatment response assessment after locoregional therapy to hepatocellular carcinoma is fraught with multiple challenges given the varied post-treatment imaging appearance. • Ethiodized oil is unique in that its' radiopacity can serve as an imaging biomarker to help predict treatment response. • The pattern of deposition of ethiodozed oil has served as a mechanism to detect portions of tumor that are undertreated and can serve as an adjunct to enhancement in order to improve management in patients treated with intraarterial embolization with ethiodized oil.

ChatGPT: The transformative influence of generative AI on science and healthcare.

In an age where technology is evolving at a sometimes incomprehensibly rapid pace, the liver community must adjust and learn to embrace breakthroughs with an open mind in order to benefit from potentially transformative influences on our science and practice. The Journal of Hepatology has responded to novel developments in artificial intelligence (AI) by recruiting experts in the field to serve on the Editorial Board. Publications introducing novel AI technology are no longer uncommon in our journal and are among the most highly debated and possibly practice-changing papers across a broad range of scientific disciplines, united by their focus on liver disease. As AI is rapidly evolving, this expert paper will focus on educating our readership on large language models and their possible impact on our research practice and clinical outlook, outlining both challenges and opportunities in the field. "To improve is to change; to be perfect is to change often." - Winston S. Churchill.

Hepatic Hypertrophy in Normal and Cirrhotic Livers Following Portal Vein Embolization: Comparative Assessment of 2 Different Embolic Regimens in a Large Animal Model.

PURPOSE: To compare the mechanistic effects and hypertrophy outcomes using 2 different portal vein embolization (PVE) regimens in normal and cirrhotic livers in a large animal model. METHODS AND MATERIALS: The Institutional Animal Care and Use Committee approved all experiments conducted in this study. Fourteen female Yorkshire pigs were separated into a cirrhotic group (CG, n = 7) and non-cirrhotic group (NCG, n = 7) and further subgrouped into those using microspheres and coils (MC, n = 3) or n-butyl cyanoacrylate (nBCA, n = 3) and their corresponding controls (each n = 1). A 3:1 ethiodized oil and ethanol mixture was administered intra-arterially in the CG to induce cirrhosis 4 weeks before PVE. Animals underwent baseline computed tomography (CT), PVE including pre-PVE and post-PVE pressure measurements, and CT imaging at 2 and 4 weeks after PVE. Immunofluorescence stainings for CD3, CD16, Ki-67, and caspase 3 were performed to assess immune cell infiltration, hepatocyte proliferation, and apoptosis. Statistical significance was tested using the Student's t test. RESULTS: Four weeks after PVE, the percentage of future liver remnant (FLR%) increased by 18.8% (standard deviation [SD], 3.6%) vs 10.9% (SD, 0.95%; P < .01) in the NCG vs CG. The baseline percentage of standardized future liver remnant (sFLR%) for the controls were 41.6% for CG vs 43.6% for NCG. Based on the embolic agents used, the sFLR% two weeks after PVE was 58.4% (SD, 3.7%) and 52.2% (SD, 0.9%) (P < .01) for MC and 46.0% (SD, 2.2%) and 47.2% (SD, 0.4%) for nBCA in the NCG and CG, respectively. Meanwhile, the sFLR% 4 weeks after PVE was 60.5% (SD, 3.9%) and 54.9% (SD, 0.8%) (P < .01) and 60.4% (SD, 3.5%) and 54.2% (SD, 0.95%) (P < .01), respectively. Ki-67 signal intensity increased in the embolized lobe in both CG and NCG (P < .01). CONCLUSIONS: This preclinical study demonstrated that MC could be the preferred embolic of choice compared to nBCA when a substantial and rapid FLR increase is needed for resection, in both cirrhotic and non-cirrhotic livers.

Bone Mass Changes Following Percutaneous Radiofrequency Ablation, Osteoplasty, Reinforcement, and Internal Fixation of Periacetabular Osteolytic Metastases.

BACKGROUND: The success of orthopedic interventions for periacetabular osteolytic metastases depends on the progression or regression of cancer-induced bone loss. PURPOSE: To characterize relative bone mass changes following percutaneous radiofrequency ablation, osteoplasty, cement reinforcement, and internal screw fixation (AORIF). METHODS: Of 70 patients who underwent AORIF at a single institution, 21 patients (22 periacetabular sites; average follow-up of 18.5 ± 12.3 months) had high-resolution pelvic bone CT scans, with at least one scan within 3 months following their operation (baseline) and a comparative scan at least 6 months post-operatively. In total, 73 CT scans were measured for bone mass changes using Hounsfield Units (HU). A region of interest was defined for the periacetabular area in the coronal, axial, and sagittal reformation planes for all CT scans. For 6-month and 1-year scans, the coronal and sagittal HU were combined to create a weight-bearing HU (wbHU). Three-dimensional volumetric analysis was performed on the baseline and longest available CT scans. Cohort survival was compared to predicted PathFx 3.0 survival. RESULTS: HU increased from baseline post-operative (1.2 ± 1.1 months) to most recent follow-up (20.2 ± 12.1 months) on coronal (124.0 ± 112.3), axial (140.3 ± 153.0), and sagittal (151.9 ± 162.4), p < 0.05. Grayscale volumetric measurements increased by 173.4 ± 166.4 (p < 0.05). AORIF median survival was 27.7 months (6.0 months PathFx3.0 predicted; p < 0.05). At 12 months, patients with >10% increase in wbHU demonstrated superior median survival of 36.5 months (vs. 26.4 months, p < 0.05). CONCLUSION: Percutaneous stabilization leads to improvements in bone mass and may allow for delays in extensive open reconstruction procedures.

Consensus report from the 10th Global Forum for Liver Magnetic Resonance Imaging: developments in HCC management.

The 10th Global Forum for Liver Magnetic Resonance Imaging (MRI) was held as a virtual 2-day meeting in October 2021, attended by delegates from North and South America, Asia, Australia, and Europe. Most delegates were radiologists with experience in liver MRI, with representation also from specialists in liver surgery, oncology, and hepatology. Presentations, discussions, and working groups at the Forum focused on the following themes: • Gadoxetic acid in clinical practice: Eastern and Western perspectives on current uses and challenges in hepatocellular carcinoma (HCC) screening/surveillance, diagnosis, and management • Economics and outcomes of HCC imaging • Radiomics, artificial intelligence (AI) and deep learning (DL) applications of MRI in HCC. These themes are the subject of the current manuscript. A second manuscript discusses multidisciplinary tumor board perspectives: how to approach early-, mid-, and late-stage HCC management from the perspectives of a liver surgeon, interventional radiologist, and oncologist (Taouli et al, 2023). Delegates voted on consensus statements that were developed by working groups on these meeting themes. A consensus was considered to be reached if at least 80% of the voting delegates agreed on the statements. CLINICAL RELEVANCE STATEMENT: This review highlights the clinical applications of gadoxetic acid-enhanced MRI for liver cancer screening and diagnosis, as well as its cost-effectiveness and the applications of radiomics and AI in patients with liver cancer. KEY POINTS: • Interpretation of gadoxetic acid-enhanced MRI differs slightly between Eastern and Western guidelines, reflecting different regional requirements for sensitivity vs specificity. • Emerging data are encouraging for the cost-effectiveness of gadoxetic acid-enhanced MRI in HCC screening and diagnosis, but more studies are required. • Radiomics and artificial intelligence are likely, in the future, to contribute to the detection, staging, assessment of treatment response and prediction of prognosis of HCC-reducing the burden on radiologists and other specialists and supporting timely and targeted treatment for patients.

Consensus report from the 10th global forum for liver magnetic resonance imaging: multidisciplinary team discussion.

The 10th Global Forum for Liver Magnetic Resonance Imaging was held in October 2021. The themes of the presentations and discussions at this Forum are described in detail in the review by Taouli et al (2023). The focus of this second manuscript developed from the Forum is on multidisciplinary tumor board perspectives in hepatocellular carcinoma (HCC) management: how to approach early-, mid-, and late-stage management from the perspectives of a liver surgeon, an interventional radiologist, and an oncologist. The manuscript also includes a panel discussion by multidisciplinary experts on three selected cases that explore challenging aspects of HCC management. CLINICAL RELEVANCE STATEMENT: This review highlights the importance of a multidisciplinary team approach in liver cancer patients and includes the perspectives of a liver surgeon, an interventional radiologist, and an oncologist, including illustrative case studies. KEY POINTS: • A liver surgeon, interventional radiologist, and oncologist presented their perspectives on the treatment of early-, mid-, and late-stage HCC. • Different perspectives on HCC management between specialties emphasize the importance of multidisciplinary tumor boards. • A multidisciplinary faculty discussed challenging aspects of HCC management, as highlighted by three case studies.

Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning.

Tumor recurrence affects up to 70% of early-stage hepatocellular carcinoma (HCC) patients, depending on treatment option. Deep learning algorithms allow in-depth exploration of imaging data to discover imaging features that may be predictive of recurrence. This study explored the use of convolutional neural networks (CNN) to predict HCC recurrence in patients with early-stage HCC from pre-treatment magnetic resonance (MR) images. This retrospective study included 120 patients with early-stage HCC. Pre-treatment MR images were fed into a machine learning pipeline (VGG16 and XGBoost) to predict recurrence within six different time frames (range 1-6 years). Model performance was evaluated with the area under the receiver operating characteristic curves (AUC-ROC). After prediction, the model's clinical relevance was evaluated using Kaplan-Meier analysis with recurrence-free survival (RFS) as the endpoint. Of 120 patients, 44 had disease recurrence after therapy. Six different models performed with AUC values between 0.71 to 0.85. In Kaplan-Meier analysis, five of six models obtained statistical significance when predicting RFS (log-rank p < 0.05). Our proof-of-concept study indicates that deep learning algorithms can be utilized to predict early-stage HCC recurrence. Successful identification of high-risk recurrence candidates may help optimize follow-up imaging and improve long-term outcomes post-treatment.

Experimental VX2 Rabbit Liver Tumor Model in Carbon Tetrachloride-Induced Cirrhosis of the Liver.

Liver cirrhosis is a major underlying factor in the development of hepatocellular carcinoma. Currently, there is an unmet need for midsize experimental vertebrate models that would offer reproducible implantable liver tumors in a cirrhotic liver background. This study establishes a protocol for a syngeneic rabbit model of VX2 liver cancer with underlying liver cirrhosis induced using carbon tetrachloride (CCl4). Male New Zealand white rabbits (n = 3) received CCl4 by intragastric administration once weekly. Concentrations started at 5% v/v CCl4 dissolved in olive oil. CCl4 dosing was progressively increased every week by 2.5% v/v increments for the duration of treatment (16 weeks total). VX2 tumors were then orthotopically implanted into the left hepatic lobe and allowed to grow for 3 weeks. Cross-sectional imaging confirmed the presence of hepatic tumors. Gross and histopathological evaluations showed reproducible tumor growth in the presence of liver cirrhosis in all animals.