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.

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.

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.

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.

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.

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.

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.

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.

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.

MR Imaging-Based In Vivo Macrophage Imaging to Monitor Immune Response after Radiofrequency Ablation of the Liver

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.

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.

Defining textbook outcome for selective internal radiation therapy of hepatocellular carcinoma: an international expert study

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.

[Acquired alpha-thalassemia in an 86-year-old patient with myelodysplastic syndrome]

BACKGROUND

Alpha thalassemia-myelodysplastic syndrome (ATMDS) is one of the possible complications related to the genetic instability typical of clonal hemopoietic disorders such as myelodysplastic syndromes (MDS). Hemoglobin H acquisition, which is hemoglobin without alpha chains and with 4 beta chains is the hallmark of this disease.

OBSERVATION

An 86-year-old male with chronic, microcytic anemia was referred due to a fall in his hemoglobin level. The blood smear was remarkable for intense anisocytoses and poikilocytosis. Bone marrow analysis was followed by a diagnosis of MDS with a good prognostic score. Peripheral blood coloration with brilliant cresyl blue showed "golf ball-like" erythrocytes. Hemoglobin electrophoresis is notable for the presence of H hemoglobin. The new generation sequencing confirmed the diagnosis of ATMDS showing a non-sense mutation in the gene ATRX.

CONCLUSION

The diagnosis of ATMDS should be considered in the presence of the association of MDS, microcytic anemia and marked blood smear abnormalities such as anisocytosis and poikilocytosis. A little less than 10% of all MDS are complicated by ATMDS.

Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study

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.

Conventional vs. Drug-Eluting Beads Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma-A Propensity Score Weighted Comparison of Efficacy and Safety

This study compared the efficacy and safety of conventional transarterial chemoembolization (cTACE) with drug-eluting beads (DEB)-TACE in patients with unresectable hepatocellular carcinoma (HCC). This retrospective analysis included 370 patients with HCC treated with cTACE (n = 248) or DEB-TACE (n = 122) (January 2000-July 2014). Overall survival (OS) was assessed using uni- and multivariate Cox proportional hazards models and Kaplan-Meier analysis. Additionally, baseline imaging was assessed, and clinical and laboratory toxicities were recorded. Propensity score weighting via a generalized boosted model was applied to account for group heterogeneity. There was no significant difference in OS between cTACE (20 months) and DEB-TACE patients (24.3 months, ratio 1.271, 95% confidence interval 0.876-1.69; p = 0.392). However, in patients with infiltrative disease, cTACE achieved longer OS (25.1 months) compared to DEB-TACE (9.2 months, ratio 0.366, 0.191-0.702; p = 0.003), whereas DEB-TACE proved more effective in nodular disease (39.4 months) than cTACE (18 months, ratio 0.458, 0.308-0681; p = 0.007). Adverse events occurred with similar frequency, except for abdominal pain, which was observed more frequently after DEB-TACE (101/116; 87.1%) than cTACE (119/157; 75.8%; p = 0.02). In conclusion, these findings suggest that tumor morphology and distribution should be used as parameters to inform decisions on the selection of embolic materials for TACE for a more personalized treatment planning in patients with unresectable HCC.

Analysis of Tumor Burden as a Biomarker for Patient Survival with Neuroendocrine Tumor Liver Metastases Undergoing Intra-Arterial Therapies: A Single-Center Retrospective Analysis

PURPOSE

To assess the value of quantitative analysis of tumor burden on baseline MRI for prediction of survival in patients with neuroendocrine tumor liver metastases (NELM) undergoing intra-arterial therapies.

MATERIALS AND METHODS

This retrospective single-center analysis included 122 patients with NELM who received conventional (n = 74) or drug-eluting beads, (n = 20) chemoembolization and radioembolization (n = 28) from 2000 to 2014. Overall tumor diameter (1D) and area (2D) of up to 3 largest liver lesions were measured on baseline arterially contrast enhanced MR images. Three-dimensional quantitative analysis was performed using the qEASL tool (IntelliSpace Portal Version 8, Philips) to calculate enhancing tumor burden (the ratio between enhancing tumor volume and total liver volume). Based on Q-statistics, patients were stratified into low tumor burden (TB) or high TB.

RESULTS

The survival curves were significantly separated between low TB and high TB groups for 1D (p < 0.001), 2D (p < 0.001) and enhancing TB (p = 0.008) measurements, with, respectively, 2.7, 2.6 and 2.2 times longer median overall survival (MOS) in the low TB group (p < 0.001, p < 0.001 and p = 0.008). Multivariate analysis showed that 1D, 2D, and enhancing TB were independent prognostic factors for MOS, with respective hazard ratios of 0.4 (95%CI: 0.2-0.6, p < 0.001), 0.4 (95%CI: 0.3-0.7, p < 0.001) and 0.5 (95%CI: 0.3-0.8, p = 0.003).

CONCLUSION

The overall tumor diameter, overall tumor area, and enhancing tumor burden are strong prognostic factors of overall survival in patients with neuroendocrine tumor liver metastases undergoing intra-arterial therapies.

Response assessment methods for patients with hepatic metastasis from rare tumor primaries undergoing transarterial chemoembolization

PURPOSE

This study assessed the response to conventional transarterial chemoembolization (cTACE) in patients with liver metastases from rare tumor primaries using one-dimensional (1D) and three-dimensional (3D) quantitative response assessment methods, and investigate the relationship of lipiodol deposition in predicting response.

MATERIALS AND METHODS

This retrospective bicentric study included 16 patients with hepatic metastases from rare tumors treated with cTACE between 2002 and 2017. Multi-phasic MR imaging obtained before and after cTACE was used for assessment of response. Response evaluation criteria in solid tumors (RECIST) and modified-RECIST (mRECIST) were utilized for 1D response assessment, and volumetric RECIST (vRECIST) and enhancement-based quantitative European Association for Study of the Liver EASL (qEASL) were used for 3D response assessment. The same day post-cTACE CT scan was analyzed to quantify intratumoral lipiodol deposition (%).

RESULTS

The mean and standard deviation (SD) of diameter of treated lesions per targeted area was 7.5 ± 5.4 cm, and the mean and SD of number of metastases in each targeted area was 4.2 ± 4.6. cTACE was technically successful in all patients, without major complications. While RECIST and vRECIST methods did not allocate patients with partial response, mRECIST and qEASL identified patients with partial response. Intratumoral lipiodol deposition significantly predicted treatment response according qEASL (R² = 0.470, p < 0.01), while no association was shown between lipiodol deposition within treated tumor area and RECIST or mRECIST (p > 0.212).

CONCLUSION

3D quantitative volumetric response analysis can be used for stratification of response to cTACE in patients with hepatic metastases originating from rare primary tumors. Lipiodol deposition could potentially be used as an early surrogate to predict response to cTACE.

Intraarterial Therapies for the Management of Hepatocellular Carcinoma

Image-guided locoregional therapies play a crucial role in the management of patients with hepatocellular carcinoma (HCC). Transarterial therapies consist of a group of catheter-based treatments where embolic agents are delivered directly into the tumor via their supplying arteries. Some of the transarterial therapies available include bland embolization (TAE), transarterial chemoembolization (TACE), drug-eluting beads-transarterial chemoembolization (DEB-TACE), selective internal radioembolization therapy (SIRT), and hepatic artery infusion (HAI). This article provides a review of pre-procedural, intra-procedural, and post-procedural aspects of each therapy, along with a review of the literature. Newer embolotherapy options and future directions are also briefly discussed.

Optimization of the BCLC Staging System for Locoregional Therapy for Hepatocellular Carcinoma by Using Quantitative Tumor Burden Imaging Biomarkers at MRI

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 B_n, whereas patients with ETV or ETB above the determined cutoffs were restratified or remained in BCLC C_n 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 B_n 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 C_n 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.

Impact of Chemoembolic Regimen on Immune Cell Recruitment and Immune Checkpoint Marker Expression following Transcatheter Arterial Chemoembolization in a VX2 Rabbit Liver Tumor Model

PURPOSE

To characterize the effects of commonly used transcatheter arterial chemoembolization (TACE) regimens on the immune response and immune checkpoint marker expression using a VX2 rabbit liver tumor model.

MATERIALS AND METHODS

Twenty-four VX2 liver tumor-bearing New Zealand white rabbits were assigned to 7 groups (n = 3 per group) undergoing locoregional therapy as follows: (a) bicarbonate infusion without embolization, (b) conventional TACE (cTACE) using a water-in-oil emulsion containing doxorubicin mixed 1:2 with Lipiodol, drug-eluting embolic-TACE with either (c) idarubicin-eluting Oncozene microspheres (40 μm) or (d) doxorubicin-eluting Lumi beads (40-90 μm). For each therapy arm (b-d), a tandem set of 3 animals with additional bicarbonate infusion before TACE was added, to evaluate the effect of pH modification on the immune response. Three untreated rabbits served as controls. Tissue was harvested 24 hours after treatment, followed by digital immunohistochemistry quantification (counts/μm² ± SEM) of tumor-infiltrating cluster of differentiation 3⁺ T-lymphocytes, human leukocyte antigen DR type antigen-presenting cells (APCs), cytotoxic T-lymphocyte-associated protein-4 (CTLA-4), and programmed cell death protein-1 (PD-1)/PD-1 ligand (PD-L1) pathway axis expression.

RESULTS

Lumi-bead TACE induced significantly more intratumoral T-cell and APC infiltration than cTACE and Oncozene-microsphere TACE. Additionally, tumors treated with Lumi-bead TACE expressed significantly higher intratumoral immune checkpoint markers compared with cTACE and Oncozene-microsphere TACE. Neoadjuvant bicarbonate demonstrated the most pronounced effect on cTACE and resulted in a significant increase in intratumoral cluster of differentiation 3⁺ T-cell infiltration compared with cTACE alone.

CONCLUSIONS

This preclinical study revealed significant differences in evoked tumor immunogenicity depending on the choice of chemoembolic regimen for TACE.

MR Imaging Biomarkers for the Prediction of Outcome after Radiofrequency Ablation of Hepatocellular Carcinoma: Qualitative and Quantitative Assessments of the Liver Imaging Reporting and Data System and Radiomic Features

PURPOSE

To assess the Liver Imaging Reporting and Data System (LI-RADS) and radiomic features in pretreatment magnetic resonance (MR) imaging for predicting progression-free survival (PFS) in patients with nodular hepatocellular carcinoma (HCC) treated with radiofrequency (RF) ablation.

MATERIAL AND METHODS

Sixty-five therapy-naïve patients with 85 nodular HCC tumors <5 cm in size were included in this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved, retrospective study. All patients underwent RF ablation as first-line treatment and demonstrated complete response on the first follow-up imaging. Gadolinium-enhanced MR imaging biomarkers were analyzed for LI-RADS features by 2 board-certified radiologists or by analysis of nodular and perinodular radiomic features from 3-dimensional segmentations. A radiomic signature was calculated with the most informative features of a least absolute shrinkage and selection operator Cox regression model using leave-one-out cross-validation. The association between both LI-RADS features and radiomic signatures with PFS was assessed via the Kaplan-Meier analysis and a weighted log-rank test.

RESULTS

The median PFS was 19 months (95% confidence interval, 16.1-19.4) for a follow-up period of 24 months. Multifocality (P = .033); the appearance of capsular continuity, compared with an absent or discontinuous capsule (P = .012); and a higher radiomic signature based on nodular and perinodular features (P = .030) were associated with poorer PFS in early-stage HCC. The observation size, presence of arterial hyperenhancement, nonperipheral washout, and appearance of an enhancing "capsule" were not associated with PFS (P > .05).

CONCLUSIONS

Although multifocal HCC clearly indicates a more aggressive phenotype even in early-stage disease, the continuity of an enhancing capsule and a higher radiomic signature may add value as MR imaging biomarkers for poor PFS in HCC treated with RF ablation.

Comparison of metabolic and immunologic responses to transarterial chemoembolization with different chemoembolic regimens in a rabbit VX2 liver tumor model

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).

Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction

Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.

Quantitative Automated Segmentation of Lipiodol Deposits on Cone Beam CT Imaging acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach

PURPOSE

The purpose of this study was to show that a deep learning-based, automated model for Lipiodol segmentation on CBCT after cTACE performs closer to the "ground truth segmentation" than a conventional thresholding-based model.

MATERIALS & METHODS

This post-hoc analysis included 36 patients with a diagnosis of HCC or other solid liver tumor who underwent cTACE with an intra-procedural CBCT. Semi-automatic segmentation of Lipiodol were obtained. Then, a convolutional U-net model was used to output a binary mask that predicts Lipiodol deposition. A threshold value of signal intensity on CBCT was used to obtain a Lipiodol mask for comparison. Dice similarity coefficient (DSC), Mean-squared error (MSE), and 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 two masks. These results were used to compare the model vs. 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<0.001) and MSE (125.53±107.36 vs. 185.98±93.82,p=0.005). Difference between the CM predicted, and the actual CM was (15.31±14.63mm vs. 31.34±30.24mm,p<0.001), with lesser distance indicating higher accuracy. The fraction of volume present ([predicted Lipiodol volume]/[ground truth Lipiodol volume]) was 1.22±0.84vs.2.58±3.52,p=0.048 for our model's prediction and threshold technique, respectively.

CONCLUSION

This study showed that a deep learning framework could detect Lipiodol in CBCT 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 intra-procedurally.

Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging

PURPOSE

Accurate liver segmentation is key for volumetry assessment to guide treatment decisions. Moreover, it is an important pre-processing step for cancer detection algorithms. Liver segmentation can be especially challenging in patients with cancer-related tissue changes and shape deformation. The aim of this study was to assess the ability of state-of-the-art deep learning 3D liver segmentation algorithms to generalize across all different Barcelona Clinic Liver Cancer (BCLC) liver cancer stages.

METHODS

This retrospective study, included patients from an institutional database that had arterial-phase T1-weighted magnetic resonance images with corresponding manual liver segmentations. The data was split into 70/15/15% for training/validation/testing each proportionally equal across BCLC stages. Two 3D convolutional neural networks were trained using identical U-net-derived architectures with equal sized training datasets: one spanning all BCLC stages ("All-Stage-Net": AS-Net), and one limited to early and intermediate BCLC stages ("Early-Intermediate-Stage-Net": EIS-Net). Segmentation accuracy was evaluated by the Dice Similarity Coefficient (DSC) on a dataset spanning all BCLC stages and a Wilcoxon signed-rank test was used for pairwise comparisons.

RESULTS

219 subjects met the inclusion criteria (170 males, 49 females, 62.8±9.1 years) from all BCLC stages. Both networks were trained using 129 subjects: AS-Net training comprised 19, 74, 18, 8, and 10 BCLC 0, A, B, C, and D patients, respectively; EIS-Net training comprised 21, 86, and 22 BCLC 0, A, and B patients, respectively. DSCs (mean±SD) were 0.954±0.018 and 0.946±0.032 for AS-Net and EIS-Net (p<0.001), respectively. The AS-Net 0.956±0.014 significantly outperformed the EIS-Net 0.941±0.038 on advanced BCLC stages (p<0.001) and yielded similarly good segmentation performance on early and intermediate stages (AS-Net: 0.952±0.021; EIS-Net: 0.949±0.027; p = 0.107).

CONCLUSION

To ensure robust segmentation performance across cancer stages that is independent of liver shape deformation and tumor burden, it is critical to train deep learning models on heterogeneous imaging data spanning all BCLC stages.

Consensus Guidelines for the Definition of Time-to-Event End Points in Image-guided Tumor Ablation: Results of the SIO and DATECAN Initiative

There is currently no consensus regarding preferred clinical outcome measures following image-guided tumor ablation or clear definitions of oncologic end points. This consensus document proposes standardized definitions for a broad range of oncologic outcome measures with recommendations on how to uniformly document, analyze, and report outcomes. The initiative was coordinated by the Society of Interventional Oncology in collaboration with the Definition for the Assessment of Time-to-Event End Points in Cancer Trials, or DATECAN, group. According to predefined criteria, based on experience with clinical trials, an international panel of 62 experts convened. Recommendations were developed using the validated three-step modified Delphi consensus method. Consensus was reached on when to assess outcomes per patient, per session, or per tumor; on starting and ending time and survival time definitions; and on time-to-event end points. Although no consensus was reached on the preferred classification system to report complications, quality of life, and health economics issues, the panel did agree on using the most recent version of a validated patient-reported outcome questionnaire. This article provides a framework of key opinion leader recommendations with the intent to facilitate a clear interpretation of results and standardize worldwide communication. Widespread adoption will improve reproducibility, allow for accurate comparisons, and avoid misinterpretations in the field of interventional oncology research. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Liddell in this issue.

Longitudinal Analysis of the Effect of Repeated Transarterial Chemoembolization for Liver Cancer on Portal Venous Pressure

Objectives

Investigate long-term effects of repeated transarterial chemoembolization (TACE) on portal venous pressure (PVP) using non-invasive surrogate markers of portal hypertension.

Methods

Retrospective, Institutional Review Board-approved study. 99 patients [hepatocellular carcinoma (HCC) group (n=57); liver metastasis group (n=42)] who underwent 279TACEs and had longitudinal pre-/post-therapy contrast-enhanced-MRI (n=388) and complete blood work were included. Outcomes of interest were platelet count (PC), spleen volume, ascites and portosystemic collaterals. Variables included TACE type/number, tumor type, microcatheter location, Child-Pugh, baseline tumor burden (tumor number/total/largest size), vessel invasion, alpha-fetoprotein, Eastern Cooperative Oncology Group (ECOG) performance status, and Model for End-Stage Liver Disease (MELD) score. Generalized Estimating Equations assessed the associations between TACE and outcomes. Power analysis determined the sample size was sufficient.

Results

No significant change in PC over time was observed in either groups, regardless of liver function (P>0.05). Baseline spleen volume was 226 cm³ for metastatic group, and was larger by 204 cm³ for HCC group (P<0.001). Spleen volume increased by 20 cm³ (95%CI: 8-32; P=0.001) for both groups after 1^stTACE and by 16cm³/TACE (P=0.099) over the full follow-up (up to 9TACEs). Spleen volume also tended to increase by 23cm³ (95%CI: -1–48; P=0.064) with higher tumor burden. Odds of developing moderate/severe ascites for metastatic patients was decreased by 0.5 (95%CI: 0.3–0.9; P=0.014), regardless of the Child-Pugh, and increased by 1.5 (95%CI: 1.2–1.9; P<0.001) among HCC patients with unstable Child-Pugh, whereas no change was noted with stable Child-Pugh. HCC patients with unstable Child-Pugh demonstrated a significant increase in portosystemic collaterals number over time (P=0.008). PVP-related complications such as variceal bleeding post-TACE were low (0.4%).

Conclusion

Repeated TACEs did seem to have an impact on PVP. However, the increase in PVP had marginal effects with low portal hypertension-related complications.

Lipiodol Deposition and Washout in Primary and Metastatic Liver Tumors After Chemoembolization

BACKGROUND/AIM

Lipiodol is the key component of conventional trans-arterial chemoembolization. Our aim was to evaluate lipiodol deposition and washout rate after conventional trans-arterial chemoembolization in intrahepatic cholangiocarcinoma and hepatic metastases originating from neuroendocrine tumors and colorectal carcinoma.

PATIENTS AND METHODS

This was a retrospective analysis of 44 patients with intrahepatic cholangiocarcinoma and liver metastasis from neuroendocrine tumors or colorectal carcinoma who underwent conventional trans-arterial chemoembolization. Lipiodol volume (cm³) was analyzed on non-contrast computed tomography imaging obtained within 24 h post conventional trans-arterial chemoembolization, and 40-220 days after conventional trans-arterial chemoembolization using volumetric image analysis software. Tumor response was assessed on contrast-enhanced magnetic resonance imaging 1 month after conventional trans-arterial chemoembolization.

RESULTS

The washout rate was longer for neuroendocrine tumors compared to colorectal carcinoma, with half-lives of 54.61 days (p<0.00001) and 19.39 days (p<0.001), respectively, with no exponential washout among intrahepatic cholangiocarcinomas (p=0.83). The half-life for lipiodol washout was longer in tumors larger than 300 cm³ compared to smaller tumors (25.43 vs. 22.71 days). Lipiodol wash out half-life was 54.76 days (p<0.01) and 29.45 days (p<0.00001) for tumors with a contrast enhancement burden of 60% or more and less than 60%, respectively. A negative exponential relationship for lipiodol washout was observed in non-responders (p<0.00001).

CONCLUSION

Lipiodol washout is a time-dependent process, and occurs faster in colorectal carcinoma tumors, tumors smaller than 300 cm³, tumors with baseline contrast enhancement burden of less than 60%, and non-responding target lesions.

How COVID-19 kick-started online learning in medical education-The DigiMed study

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic led to far-reaching restrictions of social and professional life, affecting societies all over the world. To contain the virus, medical schools had to restructure their curriculum by switching to online learning. However, only few medical schools had implemented such novel learning concepts. We aimed to evaluate students' attitudes to online learning to provide a broad scientific basis to guide future development of medical education.

METHODS

Overall, 3286 medical students from 12 different countries participated in this cross-sectional, web-based study investigating various aspects of online learning in medical education. On a 7-point Likert scale, participants rated the online learning situation during the pandemic at their medical schools, technical and social aspects, and the current and future role of online learning in medical education.

RESULTS

The majority of medical schools managed the rapid switch to online learning (78%) and most students were satisfied with the quantity (67%) and quality (62%) of the courses. Online learning provided greater flexibility (84%) and led to unchanged or even higher attendance of courses (70%). Possible downsides included motivational problems (42%), insufficient possibilities for interaction with fellow students (67%) and thus the risk of social isolation (64%). The vast majority felt comfortable using the software solutions (80%). Most were convinced that medical education lags behind current capabilities regarding online learning (78%) and estimated the proportion of online learning before the pandemic at only 14%. In order to improve the current curriculum, they wish for a more balanced ratio with at least 40% of online teaching compared to on-site teaching.

CONCLUSION

This study demonstrates the positive attitude of medical students towards online learning. Furthermore, it reveals a considerable discrepancy between what students demand and what the curriculum offers. Thus, the COVID-19 pandemic might be the long-awaited catalyst for a new "online era" in medical education.

Hepatic Radiofrequency Ablation: Monitoring of Ablation-Induced Macrophage Recruitment in the Periablational Rim Using SPION-Enhanced Macrophage-Specific Magnetic Resonance Imaging

OBJECTIVES

Macrophages accumulating in the periablational rim play a pivotal role in initiating and sustaining the perifocal inflammatory reaction, which has been shown to be at least 1 of the mechanisms responsible for the systemic pro-oncogenic effects of focal hepatic radiofrequency ablation (RFA). Herein, we tested the hypothesis to use superparamagnetic iron oxide nanoparticle (SPION)-enhanced magnetic resonance imaging (MRI) for noninvasive quantification of iron-loaded macrophages in the periablational rim of VX2 tumor-bearing rabbits.

MATERIALS AND METHODS

Twelve VX2 tumor-bearing rabbits underwent MRI immediately after and up to 3 weeks after focal hepatic RFA. For noninvasive quantification of macrophage accumulation in the periablational rim, animals were scanned before and 24 hours after SPION injection. T2*-weighted images were analyzed and correlated with histopathological and immunohistochemical findings. Furthermore, correlations with quantitative measurements (ICP-MS [inductively coupled plasma-mass spectrometry] and LA-ICP-MS [laser ablation-ICP-MS]) were performed.

RESULTS

SPION-enhanced T2*-weighted MRI scans displayed a progressive increase in the areas of signal intensity (SI) loss within the periablational rim peaking 3 weeks after RFA. Accordingly, quantitative analysis of SI changes demonstrated a significant decline in the relative SI ratio reflecting a growing accumulation of iron-loaded macrophages in the rim. Histological analyses confirmed a progressive accumulation of iron-loaded macrophages in the periablational rim. The ICP-MS and LA-ICP-MS confirmed a progressive increase of iron concentration in the periablational rim.

CONCLUSIONS

SPION-enhanced MRI enables noninvasive monitoring and quantification of ablation-induced macrophage recruitment in the periablational rim. Given the close interplay between ablation-induced perifocal inflammation and potential unwanted tumorigenic effects of RFA, SPION-enhanced MRI may serve as a valuable tool to guide and modulate adjuvant therapies after hepatic RFA.

Quantitative volumetric assessment of baseline enhancing tumor volume as an imaging biomarker predicts overall survival in patients with glioblastoma

BACKGROUND

Glioblastoma multiforme (GBM) is the commonest malignant primary brain tumor and still has one of the worst prognoses among cancers in general. There is a need for non-invasive methods to predict individual prognosis in patients with GBM.

PURPOSE

To evaluate quantitative volumetric tissue assessment of enhancing tumor volume on cranial magnetic resonance imaging (MRI) as an imaging biomarker for predicting overall survival (OS) in patients with GBM.

MATERIAL AND METHODS

MRI scans of 49 patients with histopathologically confirmed GBM were analyzed retrospectively. Baseline contrast-enhanced (CE) MRI sequences were transferred to a segmentation-based three-dimensional quantification tool, and the enhancing tumor component was analyzed. Based on a cut-off percentage of the enhancing tumor volume (PoETV) of >84.78%, samples were dichotomized, and the OS and intracranial progression-free survival (PFS) were evaluated. Univariable and multivariable analyses, including variables such as sex, Karnofsky Performance Status score, O⁶-methylguanine-DNA-methyltransferase status, age, and resection status, were performed using the Cox regression model.

RESULTS

The median OS and PFS were 16.9 and 7 months in the entire cohort, respectively. Patients with a CE tumor volume of >84.78% showed a significantly shortened OS (12.9 months) compared to those with a CE tumor volume of ≤84.78% (17.7 months) (hazard ratio [HR] 2.72; 95% confidence interval [CI] 1.22-6.03; P = 0.01). Multivariable analysis confirmed that PoETV had a significant prognostic role (HR 2.47; 95% CI 1.08-5.65; P = 0.03).

CONCLUSION

We observed a correlation between PoETV and OS. This imaging biomarker may help predict the OS of patients with GBM.

Use of Artificial Intelligence in Non-Oncologic Interventional Radiology: Current State and Future Directions

The future of radiology is disproportionately linked to the applications of artificial intelligence (AI). Recent exponential advancements in AI are already beginning to augment the clinical practice of radiology. Driven by a paucity of review articles in the area, this article aims to discuss applications of AI in non-oncologic IR across procedural planning, execution, and follow-up along with a discussion on the future directions of the field. Applications in vascular imaging, radiomics, touchless software interactions, robotics, natural language processing, post-procedural outcome prediction, device navigation, and image acquisition are included. Familiarity with AI study analysis will help open the current 'black box' of AI research and help bridge the gap between the research laboratory and clinical practice.

Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration

Multimodal image registration has many applications in diagnostic medical imaging and image-guided interventions, such as Transcatheter Arterial Chemoembolization (TACE) of liver cancer guided by intraprocedural CBCT and pre-operative MR. The ability to register peri-procedurally acquired diagnostic images into the intraprocedural environment can potentially improve the intra-procedural tumor targeting, which will significantly improve therapeutic outcomes. However, the intra-procedural CBCT often suffers from suboptimal image quality due to lack of signal calibration for Hounsfield unit, limited FOV, and motion/metal artifacts. These non-ideal conditions make standard intensity-based multimodal registration methods infeasible to generate correct transformation across modalities. While registration based on anatomic structures, such as segmentation or landmarks, provides an efficient alternative, such anatomic structure information is not always available. One can train a deep learning-based anatomy extractor, but it requires large-scale manual annotations on specific modalities, which are often extremely time-consuming to obtain and require expert radiological readers. To tackle these issues, we leverage annotated datasets already existing in a source modality and propose an anatomy-preserving domain adaptation to segmentation network (APA2Seg-Net) for learning segmentation without target modality ground truth. The segmenters are then integrated into our anatomy-guided multimodal registration based on the robust point matching machine. Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations.

Identifying enhancement-based staging markers on baseline MRI in patients with colorectal cancer liver metastases undergoing intra-arterial tumor therapy

OBJECTIVES

To determine if three-dimensional whole liver and baseline tumor enhancement features on MRI can serve as staging biomarkers and help predict survival of patients with colorectal cancer liver metastases (CRCLM) more accurately than one-dimensional and non-enhancement-based features.

METHODS

This retrospective study included 88 patients with CRCLM, treated with transarterial chemoembolization or Y90 transarterial radioembolization between 2001 and 2014. Semi-automated segmentations of up to three dominant lesions were performed on pre-treatment MRI to calculate total tumor volume (TTV) and total liver volumes (TLV). Quantitative 3D analysis was performed to calculate enhancing tumor volume (ETV), enhancing tumor burden (ETB, calculated as ETV/TLV), enhancing liver volume (ELV), and enhancing liver burden (ELB, calculated as ELV/TLV). Overall and enhancing tumor diameters were also measured. A modified Kaplan-Meier method was used to determine appropriate cutoff values for each metric. The predictive value of each parameter was assessed by Kaplan-Meier survival curves and univariable and multivariable cox proportional hazard models.

RESULTS

All methods except whole liver (ELB, ELV) and one-dimensional/non-enhancement-based methods were independent predictors of survival. Multivariable analysis showed a HR of 2.1 (95% CI 1.3-3.4, p = 0.004) for enhancing tumor diameter, HR 1.7 (95% CI 1.1-2.8, p = 0.04) for TTV, HR 2.3 (95% CI 1.4-3.9, p < 0.001) for ETV, and HR 2.4 (95% CI 1.4-4.0, p = 0.001) for ETB.

CONCLUSIONS

Tumor enhancement of CRCLM on baseline MRI is strongly associated with patient survival after intra-arterial therapy, suggesting that enhancing tumor volume and enhancing tumor burden are better prognostic indicators than non-enhancement-based and one-dimensional-based markers.

KEY POINTS

• Tumor enhancement of colorectal cancer liver metastases on MRI prior to treatment with intra-arterial therapies is strongly associated with patient survival. • Three-dimensional, enhancement-based imaging biomarkers such as enhancing tumor volume and enhancing tumor burden may serve as the basis of a novel prognostic staging system for patients with liver-dominant colorectal cancer metastases.

Lipiodol as an intra-procedural imaging biomarker for liver tumor response to transarterial chemoembolization: Post-hoc analysis of a prospective clinical trial

BACKGROUND

The use of the ethiodized oil- Lipiodol in conventional trans-arterial chemoembolization (cTACE) ensures radiopacity to visualize drug delivery in the process of providing selective drug targeting to hepatic cancers and arterial embolization. Lipiodol functions as a carrier of chemo drugs for targeted therapy, as an embolic agent, augmenting the drug effect by efflux into the portal veins as well as a predictor for the tumor response and survival.

PURPOSE

To prospectively evaluate the role of 3D quantitative assessment of intra-procedural Lipiodol deposition in liver tumors on CBCT immediately after cTACE as a predictive biomarker for the outcome of cTACE.

MATERIALS & METHODS

This was a post-hoc analysis of data from an IRB-approved prospective clinical trial. Thirty-two patients with hepatocellular carcinoma or liver metastases underwent contrast enhanced CBCT obtained immediately after cTACE, unenhanced MDCT at 24 h after cTACE, and follow-up imaging 30-, 90- and 180-days post-procedure. Lipiodol deposition was quantified on CBCT after cTACE and was characterized by 4 ordinal levels: ≤25%, >25-50%, >50-75%, >75%. Tumor response was assessed on follow-up MRI. Lipiodol deposition on imaging, correlation between Lipiodol deposition and tumor response criteria, and correlation between Lipiodol coverage and median overall survival (MOS) were evaluated.

RESULTS

Image analysis demonstrated a high degree of agreement between the Lipiodol deposition on CBCT and the 24 h post-TACE CT, with a Bland-Altman plot of Lipiodol deposition on imaging demonstrated a bias of 2.75, with 95%-limits-of-agreement: -16.6 to 22.1%. An inverse relationship between Lipiodol deposition in responders versus non-responders for two-dimensional EASL reached statistical significance at 30 days (p = 0.02) and 90 days (p = 0.05). Comparing the Lipiodol deposition in Modified Response Evaluation Criteria in Solid Tumors (mRECIST) responders versus non-responders showed a statistically significant higher volumetric deposition in responders for European Association for the Study of the Liver (EASL)-30d, EASL-90d, and quantitative EASL-180d. The correlation between the relative Lipiodol deposition and the change in enhancing tumor volume showed a negative association post-cTACE (30-day: p < 0.001; rho = -0.63). A Kaplan-Meier analysis for patients with high vs. low Lipiodol deposition showed a MOS of 46 vs. 33 months (p = 0.05).

CONCLUSION

3D quantification of Lipiodol deposition on intra-procedural CBCT is a predictive biomarker of outcome in patients with primary or metastatic liver cancer undergoing cTACE. There are spatial and volumetric agreements between 3D quantification of Lipiodol deposition on intra-procedural CBCT and 24 h post-cTACE MDCT. The spatial and volumetric agreement between Lipiodol deposition on intra-procedural CBCT and 24 h post-cTACE MDCT could suggest that acquiring MDCT 24 h after cTACE is redundant. Importantly, the demonstrated relationship between levels of tumor coverage with Lipiodol and degree and timeline of tumor response after cTACE underline the role of Lipiodol as an intra-procedural surrogate for tumor response, with potential implications for the prediction of survival.

Elastin-specific MRI of extracellular matrix-remodelling following hepatic radiofrequency-ablation in a VX2 liver tumor model

Hepatic radiofrequency ablation (RFA) induces a drastic alteration of the biomechanical environment in the peritumoral liver tissue. The resulting increase in matrix stiffness has been shown to significantly influence carcinogenesis and cancer progression after focal RF ablation. To investigate the potential of an elastin-specific MR agent (ESMA) for the assessment of extracellular matrix (ECM) remodeling in the periablational rim following RFA in a VX2 rabbit liver tumor-model, twelve New-Zealand-White-rabbits were implanted in the left liver lobe with VX2 tumor chunks from donor animals. RFA of tumors was performed using a perfused RF needle-applicator with a mean tip temperature of 70 °C. Animals were randomized into four groups for MR imaging and scanned at four different time points following RFA (week 0 [baseline], week 1, week 2 and week 3 after RFA), followed by sacrifice and histopathological analysis. ESMA-enhanced MR imaging was used to assess ECM remodeling. Gadobutrol was used as a third-space control agent. Molecular MR imaging using an elastin-specific probe demonstrated a progressive increase in contrast-to-noise ratio (CNR) (week 3: ESMA: 28.1 ± 6.0; gadobutrol: 3.5 ± 2.0), enabling non-invasive imaging of the peritumoral zone with high spatial-resolution, and accurate assessment of elastin deposition in the periablational rim. In vivo CNR correlated with ex vivo histomorphometry (ElasticaVanGiesson-stain, y = 1.2x - 1.8, R2 = 0.89, p < 0.05) and gadolinium concentrations at inductively coupled mass spectroscopy (ICP-MS, y = 0.04x + 1.2, R2 = 0.95, p < 0.05). Laser-ICP-MS confirmed colocalization of elastin-specific probe with elastic fibers. Following thermal ablation, molecular imaging using an elastin-specific MR probe is feasible and provides a quantifiable biomarker for the assessment of the ablation-induced remodeling of the ECM in the periablational rim.

Thermal ablation alone vs thermal ablation combined with transarterial chemoembolization for patients with small (<3 cm) hepatocellular carcinoma

PURPOSE

Thermal ablation (TA) and transarterial chemoembolization (TACE) may be used alone or in combination (TACE+TA) for the treatment of hepatocellular carcinoma (HCC). The aim of our study was to compare the time to tumor progression (TTP) and overall survival (OS) for patients who received TA alone or TACE+TA for HCC tumors under 3 cm.

MATERIALS AND METHODS

This HIPAA-compliant IRB-approved retrospective analysis included 85 therapy-naïve patients from 2010 to 2018 (63 males, 22 females, mean age 62.4 ± 8.5 years) who underwent either TA alone (n = 64) or TA in combination with drug-eluting beads (DEB)-TACE (n = 18) or Lipiodol-TACE (n = 3) for locoregional therapy of early stage HCC with maximum tumor diameter under 3 cm. Kaplan-Meier analysis was performed using the log-rank test to assess TTP and OS.

RESULTS

All TA and TACE+TA treatments included were technically successful. TTP was 23.0 months in the TA group and 22.0 months in the TACE+TA group. There was no statistically significant difference in TTP (p = 0.64). Median OS was 69.7 months in the TA group and 64.6 months in the TACE+TA group. There was no statistically significant difference in OS (p = 0.14). The treatment cohorts had differences in AFP levels (p = 0.03) and BCLC stage (p = 0.047). Complication rates between patient groups were similar (p = 0.61).

CONCLUSION

For patients with HCC under 3 cm, TA alone and TACE+TA have similar outcomes in terms of TTP and OS, suggesting that TACE+TA may not be needed for these tumors unless warranted by tumor location or other technical consideration.

Deep learning-assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver

OBJECTIVES

To train a deep learning model to differentiate between pathologically proven hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical imaging features on MRI.

METHODS

This IRB-approved retrospective study included 118 patients with 150 lesions (93 (62%) HCC and 57 (38%) non-HCC) pathologically confirmed through biopsies (n = 72), resections (n = 29), liver transplants (n = 46), and autopsies (n = 3). Forty-seven percent of HCC lesions showed atypical imaging features (not meeting Liver Imaging Reporting and Data System [LI-RADS] criteria for definitive HCC/LR5). A 3D convolutional neural network (CNN) was trained on 140 lesions and tested for its ability to classify the 10 remaining lesions (5 HCC/5 non-HCC). Performance of the model was averaged over 150 runs with random sub-sampling to provide class-balanced test sets. A lesion grading system was developed to demonstrate the similarity between atypical HCC and non-HCC lesions prone to misclassification by the CNN.

RESULTS

The CNN demonstrated an overall accuracy of 87.3%. Sensitivities/specificities for HCC and non-HCC lesions were 92.7%/82.0% and 82.0%/92.7%, respectively. The area under the receiver operating curve was 0.912. CNN's performance was correlated with the lesion grading system, becoming less accurate the more atypical imaging features the lesions showed.

CONCLUSION

This study provides proof-of-concept for CNN-based classification of both typical- and atypical-appearing HCC lesions on multi-phasic MRI, utilizing pathologically confirmed lesions as "ground truth."

KEY POINTS

• A CNN trained on atypical appearing pathologically proven HCC lesions not meeting LI-RADS criteria for definitive HCC (LR5) can correctly differentiate HCC lesions from other liver malignancies, potentially expanding the role of image-based diagnosis in primary liver cancer with atypical features. • The trained CNN demonstrated an overall accuracy of 87.3% and a computational time of < 3 ms which paves the way for clinical application as a decision support instrument.

Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning

PURPOSE

Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic contrast-enhanced imaging for hepatocellular carcinoma (HCC) diagnosis. The goal of this feasibility study was to establish a proof-of-principle concept towards automating the application of LI-RADS, using a deep learning algorithm trained to segment the liver and delineate HCCs on MRI automatically.

METHODS

In this retrospective single-center study, multiphasic contrast-enhanced MRIs using T1-weighted breath-hold sequences acquired from 2010 to 2018 were used to train a deep convolutional neural network (DCNN) with a U-Net architecture. The U-Net was trained (using 70% of all data), validated (15%) and tested (15%) on 174 patients with 231 lesions. Manual 3D segmentations of the liver and HCC were ground truth. The dice similarity coefficient (DSC) was measured between manual and DCNN methods. Postprocessing using a random forest (RF) classifier employing radiomic features and thresholding (TR) of the mean neural activation was used to reduce the average false positive rate (AFPR).

RESULTS

73 and 75% of HCCs were detected on validation and test sets, respectively, using > 0.2 DSC criterion between individual lesions and their corresponding segmentations. Validation set AFPRs were 2.81, 0.77, 0.85 for U-Net, U-Net + RF, and U-Net + TR, respectively. Combining both RF and TR with the U-Net improved the AFPR to 0.62 and 0.75 for the validation and test sets, respectively. Mean DSC between automatically detected lesions using the DCNN + RF + TR and corresponding manual segmentations was 0.64/0.68 (validation/test), and 0.91/0.91 for liver segmentations.

CONCLUSION

Our DCNN approach can segment the liver and HCCs automatically. This could enable a more workflow efficient and clinically realistic implementation of LI-RADS.

Quantification of contrast-uptake as imaging biomarker for disease progression of renal cell carcinoma after tumor ablation

BACKGROUND

The prognosis of patients with renal cell carcinoma (RCC) depends greatly on the presence of extra-renal metastases.

PURPOSE

To investigate the value of total tumor volume (TTV) and enhancing tumor volume (ETV) as three-dimensional (3D) quantitative imaging biomarkers for disease aggressiveness in patients with RCC.

MATERIAL AND METHODS

Retrospective, HIPAA-compliant, IRB-approved study including 37 patients with RCC treated with image-guided thermal ablation during 2007-2015. TNM stage, RENAL Nephrometry Score, largest tumor diameter, TTV, and ETV were assessed on cross-sectional imaging at baseline and correlated with outcome measurements. The primary outcome was time-to-occurrence of extra-renal metastases and the secondary outcome was progression-free survival (PFS). Correlation was assessed using a Cox regression model and differences in outcomes were shown by Kaplan-Meier plots with significance and odds ratios (OR) calculated by Log-rank test/generalized Wilcoxon and continuity-corrected Woolf logit method.

RESULTS

Patients with a TTV or ETV > 5 cm³ were more likely to develop distant metastases compared to patients with TTV (OR 6.69, 95% confidence interval [CI] 0.33-134.4, P=0.022) or ETV (OR 8.48, 95% CI 0.42-170.1, P=0.016) < 5 cm³. Additionally, PFS was significantly worse in patients with larger ETV (P = 0.039; median PFS 51.87 months vs. 69.97 months). In contrast, stratification by median value of the established, caliper-based measurements showed no significant correlation with outcome parameters.

CONCLUSION

ETV, as surrogate of lesion vascularity, is a sensitive imaging biomarker for occurrence of extra-renal metastatic disease and PFS in patients with RCC.

Therapy of Intermediate-Stage Hepatocellular Carcinoma: Current Evidence and Clinical Practice

Intermediate-stage Hepatocellular Carcinoma (HCC) represents a wide range of disease burden. Patients with different levels of liver function, tumor size, and number of lesions may all have intermediate-stage disease according to the Barcelona Clinic Liver Cancer (BCLC) staging system. Several minimally invasive image-guided locoregional therapies are available for the treatment of intermediate-stage HCC, including conventional transarterial chemoembolization (cTACE), drug-eluting bead TACE (DEB-TACE), yttrium-90 radioembolization (Y-90 RE), thermal ablation, bland embolization, and combination therapy. Available clinical evidence points to cTACE as the current gold standard for the locoregional treatment of intermediate-stage HCC. DEB-TACE is at best non-inferior to cTACE in terms of survival benefit. Y-90 RE is a maturing therapy, and some institutions have adopted it as first-line therapy for intermediate-stage HCC. Thermal ablation combined with TACE may be used in select patients, while bland embolization has only limited evidence for its use. The combination of locoregional therapy with VEGF inhibitors or immune checkpoint inhibitors has also been explored. This article will examine in detail the clinical evidence supporting available locoregional treatment options for intermediate-stage HCC.