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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.
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Carcinoma Hepatocelular , Criocirurgia , Neoplasias Hepáticas , Feminino , Animais , Camundongos , Carcinoma Hepatocelular/cirurgia , Inibidores de Metaloproteinases de Matriz , Neoplasias Hepáticas/cirurgia , Linfócitos T CD8-Positivos , Metaloproteinases da MatrizRESUMO
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.
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Neoplasias Hepáticas , Imageamento por Ressonância Magnética Multiparamétrica , Animais , Concentração de Íons de Hidrogênio , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/patologia , Coelhos , Aprendizado Profundo , Espaço Extracelular/diagnóstico por imagem , Espaço Extracelular/metabolismo , Imagem de Difusão por Ressonância MagnéticaRESUMO
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.
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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.
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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.
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Carcinoma Hepatocelular , Neoplasias Hepáticas , Imageamento por Ressonância Magnética , Humanos , Masculino , Imageamento por Ressonância Magnética/métodos , Feminino , Pessoa de Meia-Idade , Neoplasias Hepáticas/diagnóstico por imagem , Estudos Retrospectivos , Carcinoma Hepatocelular/diagnóstico por imagem , Adulto , Redes Neurais de Computação , Fígado/diagnóstico por imagem , Meios de Contraste , Idoso , RadiômicaRESUMO
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.
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Inteligência Artificial , Pesquisa Biomédica , Humanos , Reprodutibilidade dos Testes , Algoritmos , RadiologistasRESUMO
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 30.7â¯% 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 33.3â¯%, 23â¯%, and 25â¯%, respectively. The fourth cTACE session was the optimal number for 2-year survival (HR 0.40; 95â¯%CI: 0.16-0.97; pâ¯=â¯0.04), 5-year survival (HR 0.31; 95â¯%CI: 0.11-0.87; pâ¯=â¯0.02), and overall survival (HR 0.35; 95â¯%CI: 0.13-0.89; pâ¯=â¯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.
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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.
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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.
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Carcinoma Hepatocelular , Quimioembolização Terapêutica , Neoplasias Hepáticas , Humanos , Inteligência Artificial , Aprendizado de Máquina , BiomarcadoresRESUMO
BACKGROUND: A textbook outcome (TO) is a composite indicator covering the entire intervention process in order to reflect the "ideal" intervention and be a surrogate for patient important outcomes. Selective internal radiation therapy (SIRT) is a complex multidisciplinary and multistep intervention facing the challenge of standardization. This expert opinion-based study aimed to define a TO for SIRT of hepatocellular carcinoma. METHODS: This study involved two steps: (1) the steering committee (4 interventional radiologists) first developed an extensive list of possible relevant items reflecting an optimal SIRT intervention based on a literature review and (2) then conducted an international and multidisciplinary survey which resulted in the final TO. This survey was online, from February to July 2021, and consisted three consecutive rounds with predefined settings. Experts were identified by contacting senior authors of randomized trials, large observational studies, or studies on quality improvement in SIRT. This study was strictly academic. RESULTS: A total of 50 items were included in the first round of the survey. A total of 29/40 experts (73%) responded, including 23 interventional radiologists (79%), three nuclear medicine physicians (10%), two hepatologists, and one oncologist, from 11 countries spanning three continents. The final TO consisted 11 parameters across six domains ("pre-intervention workup," "tumor targeting and dosimetry," "intervention," "post-90Y imaging," "length of hospital stay," and "complications"). Of these, all but one were applied in the institutions of > 80% of experts. CONCLUSIONS: This multidimensional indicator is a comprehensive standardization tool, suitable for routine care, clinical round, and research.
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Carcinoma Hepatocelular , Neoplasias Hepáticas , Humanos , Carcinoma Hepatocelular/diagnóstico por imagem , Carcinoma Hepatocelular/radioterapia , Carcinoma Hepatocelular/patologia , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/radioterapia , Neoplasias Hepáticas/tratamento farmacológico , Radiometria , Radioisótopos de Ítrio/uso terapêuticoRESUMO
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.
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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.
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Carcinoma Hepatocelular , Neoplasias Hepáticas , Humanos , Carcinoma Hepatocelular/diagnóstico por imagem , Carcinoma Hepatocelular/terapia , Carcinoma Hepatocelular/patologia , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/terapia , Neoplasias Hepáticas/patologia , Consenso , Meios de Contraste , Gadolínio DTPA , Imageamento por Ressonância Magnética/métodos , Espectroscopia de Ressonância Magnética , Equipe de Assistência ao PacienteRESUMO
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.
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Carcinoma Hepatocelular , Neoplasias Hepáticas , Humanos , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/terapia , Carcinoma Hepatocelular/diagnóstico por imagem , Carcinoma Hepatocelular/terapia , Inteligência Artificial , Meios de Contraste , Gadolínio DTPA , Imageamento por Ressonância Magnética/métodos , Espectroscopia de Ressonância Magnética , Sensibilidade e Especificidade , Estudos RetrospectivosRESUMO
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.
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Carcinoma Hepatocelular , Cirrose Hepática Experimental , Neoplasias Hepáticas Experimentais , Neoplasias Hepáticas , Coelhos , Masculino , Animais , Tetracloreto de Carbono/efeitos adversos , Fígado/patologia , Cirrose Hepática , Neoplasias Hepáticas/patologia , Carcinoma Hepatocelular/patologia , Neoplasias Hepáticas Experimentais/patologia , Cirrose Hepática Experimental/induzido quimicamente , Cirrose Hepática Experimental/patologiaRESUMO
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.
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Embolia , Embolização Terapêutica , Neoplasias Hepáticas , Animais , Feminino , Suínos , Veia Porta/diagnóstico por imagem , Veia Porta/patologia , Antígeno Ki-67 , Fígado/patologia , Hepatectomia/métodos , Embolização Terapêutica/métodos , Neoplasias Hepáticas/terapia , Hipertrofia/patologia , Hipertrofia/cirurgia , Embolia/cirurgia , Cirrose Hepática/complicações , Cirrose Hepática/diagnóstico por imagem , Modelos Animais , Resultado do TratamentoRESUMO
PURPOSE: To establish molecular magnetic resonance (MR) imaging instruments for in vivo characterization of the immune response to hepatic radiofrequency (RF) ablation using cell-specific immunoprobes. MATERIALS AND METHODS: Seventy-two C57BL/6 wild-type mice underwent standardized hepatic RF ablation (70 °C for 5 minutes) to generate a coagulation area measuring 6-7 mm in diameter. CD68+ macrophage periablational infiltration was characterized with immunohistochemistry 24 hours, 72 hours, 7 days, and 14 days after ablation (n = 24). Twenty-one mice were subjected to a dose-escalation study with either 10, 15, 30, or 60 mg/kg of rhodamine-labeled superparamagnetic iron oxide nanoparticles (SPIONs) or 2.4, 1.2, or 0.6 mg/kg of gadolinium-160 (160Gd)-labeled CD68 antibody for assessment of the optimal in vivo dose of contrast agent. MR imaging experiments included 9 mice, each receiving 10-mg/kg SPIONs to visualize phagocytes using T2∗-weighted imaging in a horizontal-bore 9.4-T MR imaging scanner, 160Gd-CD68 for T1-weighted MR imaging of macrophages, or 0.1-mmol/kg intravenous gadoterate (control group). Radiological-pathological correlation included Prussian blue staining, rhodamine immunofluorescence, imaging mass cytometry, and immunohistochemistry. RESULTS: RF ablation-induced periablational infiltration (206.92 µm ± 12.2) of CD68+ macrophages peaked at 7 days after ablation (P < .01) compared with the untreated lobe. T2∗-weighted MR imaging with SPION contrast demonstrated curvilinear T2∗ signal in the transitional zone (TZ) (186 µm ± 16.9), corresponsing to Iron Prussian blue staining. T1-weighted MR imaging with 160Gd-CD68 antibody showed curvilinear signal in the TZ (164 µm ± 3.6) corresponding to imaging mass cytometry. CONCLUSIONS: Both SPION-enhanced T2∗-weighted and 160Gd-enhanced T1-weighted MR imaging allow for in vivo monitoring of macrophages after RF ablation, demonstrating the feasibility of this model to investigate local immune responses.
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Fígado , Ablação por Radiofrequência , Animais , Camundongos , Camundongos Endogâmicos C57BL , Fígado/patologia , Imageamento por Ressonância Magnética/métodos , Macrófagos , Imunidade , Meios de ContrasteRESUMO
BACKGROUND. Posttreatment recurrence is an unpredictable complication after liver transplant for hepatocellular carcinoma (HCC) that is associated with poor survival. Biomarkers are needed to estimate recurrence risk before organ allocation. OBJECTIVE. This proof-of-concept study evaluated the use of machine learning (ML) to predict recurrence from pretreatment laboratory, clinical, and MRI data in patients with early-stage HCC initially eligible for liver transplant. METHODS. This retrospective study included 120 patients (88 men, 32 women; median age, 60.0 years) with early-stage HCC diagnosed who were initially eligible for liver transplant and underwent treatment by transplant, resection, or thermal ablation between June 2005 and March 2018. Patients underwent pretreatment MRI and posttreatment imaging surveillance. Imaging features were extracted from postcontrast phases of pretreatment MRI examinations using a pretrained convolutional neural network. Pretreatment clinical characteristics (including laboratory data) and extracted imaging features were integrated to develop three ML models (clinical model, imaging model, combined model) for predicting recurrence within six time frames ranging from 1 through 6 years after treatment. Kaplan-Meier analysis with time to recurrence as the endpoint was used to assess the clinical relevance of model predictions. RESULTS. Tumor recurred in 44 of 120 (36.7%) patients during follow-up. The three models predicted recurrence with AUCs across the six time frames of 0.60-0.78 (clinical model), 0.71-0.85 (imaging model), and 0.62-0.86 (combined model). The mean AUC was higher for the imaging model than the clinical model (0.76 vs 0.68, respectively; p = .03), but the mean AUC was not significantly different between the clinical and combined models or between the imaging and combined models (p > .05). Kaplan-Meier curves were significantly different between patients predicted to be at low risk and those predicted to be at high risk by all three models for the 2-, 3-, 4-, 5-, and 6-year time frames (p < .05). CONCLUSION. The findings suggest that ML-based models can predict recurrence before therapy allocation in patients with early-stage HCC initially eligible for liver transplant. Adding MRI data as model input improved predictive performance over clinical parameters alone. The combined model did not surpass the imaging model's performance. CLINICAL IMPACT. ML-based models applied to currently underutilized imaging features may help design more reliable criteria for organ allocation and liver transplant eligibility.
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Carcinoma Hepatocelular , Neoplasias Hepáticas , Masculino , Humanos , Feminino , Pessoa de Meia-Idade , Carcinoma Hepatocelular/diagnóstico por imagem , Carcinoma Hepatocelular/cirurgia , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/cirurgia , Estudos Retrospectivos , Fatores de Risco , Imageamento por Ressonância Magnética/métodos , Recidiva Local de Neoplasia/epidemiologiaRESUMO
Background Patients with intermediate- and advanced-stage hepatocellular carcinoma (HCC) represent a highly heterogeneous patient collective with substantial differences in overall survival. Purpose To evaluate enhancing tumor volume (ETV) and enhancing tumor burden (ETB) as new criteria within the Barcelona Clinic Liver Cancer (BCLC) staging system for optimized allocation of patients with intermediate- and advanced-stage HCC to undergo transarterial chemoembolization (TACE). Materials and Methods In this retrospective study, 682 patients with HCC who underwent conventional TACE or TACE with drug-eluting beads from January 2000 to December 2014 were evaluated. Quantitative three-dimensional analysis of contrast-enhanced MRI was performed to determine thresholds of ETV and ETB (ratio of ETV to normal liver volume). Patients with ETV below 65 cm3 or ETB below 4% were reassigned to BCLC Bn, whereas patients with ETV or ETB above the determined cutoffs were restratified or remained in BCLC Cn by means of stepwise verification of the median overall survival (mOS). Results This study included 494 patients (median age, 62 years [IQR, 56-71 years]; 401 men). Originally, 123 patients were classified as BCLC B with mOS of 24.3 months (95% CI: 21.4, 32.9) and 371 patients as BCLC C with mOS of 11.9 months (95% CI: 10.5, 14.8). The mOS of all included patients (including the BCLC B and C groups) was 15 months (95% CI: 12.3, 17.2). A total of 152 patients with BCLC C tumors were restratified into a new BCLC Bn class, in which the mOS was then 25.1 months (95% CI: 21.8, 29.7; P < .001). The mOS of the remaining patients (ie, BCLC Cn group) (n = 222; ETV ≥65 cm3 or ETB ≥4%) was 8.4 months (95% CI: 6.1, 11.2). Conclusion Substratification of patients with intermediate- and advanced-stage hepatocellular carcinoma according to three-dimensional quantitative tumor burden identified patients with a survival benefit from transarterial chemoembolization before therapy. © RSNA, 2022 Online supplemental material is available for this article.
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Carcinoma Hepatocelular , Quimioembolização Terapêutica , Neoplasias Hepáticas , Biomarcadores Tumorais , Carcinoma Hepatocelular/diagnóstico por imagem , Carcinoma Hepatocelular/terapia , Humanos , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/terapia , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Estudos Retrospectivos , Resultado do Tratamento , Carga TumoralRESUMO
OBJECTIVES: The goal of this study was to investigate the effects of TACE using Lipiodol, Oncozene™ drug-eluting embolics (DEEs), or LUMI™-DEEs alone, or combined with bicarbonate on the metabolic and immunological tumor microenvironment in a rabbit VX2 tumor model. METHODS: VX2 liver tumor-bearing rabbits were assigned to five groups. MRI and extracellular pH (pHe) mapping using Biosensor Imaging of Redundant Deviation in Shifts (BIRDS) were performed before and after intra-arterial therapy with conventional TACE (cTACE), DEE-TACE with Idarubicin-eluting Oncozene™-DEEs, or Doxorubicin-eluting LUMI™-DEEs, each with or without prior bicarbonate infusion, and in untreated rabbits or treated with intra-arterial bicarbonate only. Imaging results were validated with immunohistochemistry (IHC) staining of cell viability (PCNA, TUNEL) and immune response (HLA-DR, CD3). Statistical analysis was performed using Mann-Whitney U test. RESULTS: pHe mapping revealed that combining cTACE with prior bicarbonate infusion significantly increased tumor pHe compared to control (p = 0.0175) and cTACE alone (p = 0.0025). IHC staining revealed peritumoral accumulation of HLA-DR+ antigen-presenting cells and CD3 + T-lymphocytes in controls. cTACE-treated tumors showed reduced immune infiltration, which was restored through combination with bicarbonate. DEE-TACE with Oncozene™-DEEs induced moderate intratumoral and marked peritumoral infiltration, which was slightly reduced with bicarbonate. Addition of bicarbonate prior to LUMI™-beads enhanced peritumoral immune cell infiltration compared to LUMI™-beads alone and resulted in the strongest intratumoral immune cell infiltration across all treated groups. CONCLUSIONS: The choice of chemoembolic regimen for TACE strongly affects post-treatment TME pHe and the ability of immune cells to accumulate and infiltrate the tumor tissue. KEY POINTS: ⢠Combining conventional transarterial chemotherapy with prior bicarbonate infusion increases the pHe towards a more physiological value (p = 0.0025). ⢠Peritumoral infiltration and intratumoral accumulation patterns of antigen-presenting cells and T-lymphocytes after transarterial chemotherapy were dependent on the choice of the chemoembolic regimen. ⢠Combination of intra-arterial treatment with Doxorubicin-eluting LUMI™-beads and bicarbonate infusion resulted in the strongest intratumoral presence of immune cells (positivity index of 0.47 for HLADR+-cells and 0.62 for CD3+-cells).
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Carcinoma Hepatocelular , Quimioembolização Terapêutica , Neoplasias Hepáticas , Animais , Carcinoma Hepatocelular/patologia , Quimioembolização Terapêutica/métodos , Doxorrubicina , Óleo Etiodado , Neoplasias Hepáticas/patologia , Coelhos , Microambiente TumoralRESUMO
PURPOSE: To show that a deep learning (DL)-based, automated model for Lipiodol (Guerbet Pharmaceuticals, Paris, France) segmentation on cone-beam computed tomography (CT) after conventional transarterial chemoembolization performs closer to the "ground truth segmentation" than a conventional thresholding-based model. MATERIALS AND METHODS: This post hoc analysis included 36 patients with a diagnosis of hepatocellular carcinoma or other solid liver tumors who underwent conventional transarterial chemoembolization with an intraprocedural cone-beam CT. Semiautomatic segmentation of Lipiodol was obtained. Subsequently, a convolutional U-net model was used to output a binary mask that predicted Lipiodol deposition. A threshold value of signal intensity on cone-beam CT was used to obtain a Lipiodol mask for comparison. The dice similarity coefficient (DSC), mean squared error (MSE), center of mass (CM), and fractional volume ratios for both masks were obtained by comparing them to the ground truth (radiologist-segmented Lipiodol deposits) to obtain accuracy metrics for the 2 masks. These results were used to compare the model versus the threshold technique. RESULTS: For all metrics, the U-net outperformed the threshold technique: DSC (0.65 ± 0.17 vs 0.45 ± 0.22, P < .001) and MSE (125.53 ± 107.36 vs 185.98 ± 93.82, P = .005). The difference between the CM predicted and the actual CM was 15.31 mm ± 14.63 versus 31.34 mm ± 30.24 (P < .001), with lesser distance indicating higher accuracy. The fraction of volume present ([predicted Lipiodol volume]/[ground truth Lipiodol volume]) was 1.22 ± 0.84 versus 2.58 ± 3.52 (P = .048) for the current model's prediction and threshold technique, respectively. CONCLUSIONS: This study showed that a DL framework could detect Lipiodol in cone-beam CT imaging and was capable of outperforming the conventionally used thresholding technique over several metrics. Further optimization will allow for more accurate, quantitative predictions of Lipiodol depositions intraprocedurally.