Current status of imaging biomarkers predicting the biological nature of hepatocellular carcinoma

Outcomes in Patients with Macrotrabecular-Massive Subtype Hepatocellular Carcinoma Treated with Yttrium-90 Transarterial Radioembolization

PURPOSE

To compare the outcomes of yttrium-90 transarterial radioembolization (TARE) in patients with hepatocellular carcinoma (HCC) with and without macrotrabecular-massive (MTM) subtypes.

MATERIALS AND METHODS

Forty-one consecutive patients with HCC (male, 90.3%; mean age, 65.3 years [SD ± 10.7]) who underwent yttrium-90 TARE between September 2014 and January 2022 were grouped into the MTM-HCC (n = 17, 41.5%) and non-MTM-HCC (n = 24, 58.5%) groups based on their histopathological subtypes. Demographic, clinical, and radiological characteristics were compared. Survival, univariate, and multivariate analyses were performed, and prognostic factors were evaluated.

RESULTS

In MTM-HCC group, the rates of moderately to poorly differentiated tumors were significantly higher (13/17 vs 8/16, P = .007), and new intrahepatic/extrahepatic metastases were detected more frequently (12/17 vs 15/24, P = .038). Median overall survival (OS) in the cohort was 29 months (range, 17.1-40.9 months), whereas patients with MTM-HCC had a significantly shorter median OS (20 vs 44 months, P = .014). In univariate analysis, MTM-HCC subtype (hazard ratio [HR], 2.690; P = .021), the presence of satellite nodules (HR, 3.810; P = .004), and macrovascular invasion (HR, 3.321; P = .012) were identified as significant prognostic factors. In multivariate analysis, MTM-HCC subtype and macrovascular invasion were determined as independent poor prognostic factors (P = .038 and P = .012, respectively).

CONCLUSIONS

In patients with HCC treated with yttrium-90 TARE, both the rates of moderately to poorly differentiated histopathological classes and the development of intrahepatic or extrahepatic metastases were significantly higher in the MTM-HCC subtype. OS was worse in patients with MTM-HCC, and macrovascular invasion and MTM-HCC subtype were identified as independent poor prognostic factors.

Anticancer Effects of Wild Baicalin on Hepatocellular Carcinoma: Downregulation of AKR1B10 and PI3K/AKT Signaling Pathways

Introduction

Hepatocellular carcinoma (HCC) is a common and deadly malignancy. Traditional Chinese medicine, such as the compound Astragalus (wild Baicalin), has shown promise in improving outcomes for HCC patients. This study aimed to investigate the effects of wild Baicalin on the human hepatoma cell line HepG2 and elucidate the underlying mechanisms, particularly the role of the AKR1B10 and PI3K/AKT signaling pathways.

Methods

HepG2 cells were treated with varying concentrations of wild Baicalin. Cell proliferation, apoptosis, migration, invasion, and cell cycle were evaluated using CCK-8, flow cytometry, scratch, Transwell, and clonogenic assays, respectively. Transcriptome sequencing was performed to analyze gene expression changes induced by wild Baicalin. Differentially expressed genes were identified and analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The expression of AKR1B10 and PI3K was validated by qPCR.

Results

Wild Baicalin inhibited HepG2 cell proliferation, induced apoptosis, suppressed migration and invasion, and caused cell cycle arrest in a dose-dependent manner. Transcriptome sequencing revealed 1202 differentially expressed genes, including 486 upregulated and 716 downregulated genes. GO analysis indicated that biological processes were pivotal in the anticancer mechanism of wild Baicalin, while KEGG analysis identified metabolic pathways as the most significantly regulated. AKR1B10 and PI3K, key genes in metabolic pathways, were downregulated by wild Baicalin, which was confirmed by qPCR.

Discussion

The findings suggest that wild Baicalin exhibits potent anticancer effects against HepG2 cells by inducing apoptosis, inhibiting proliferation, migration, and invasion, and causing cell cycle arrest. The regulatory effects of wild Baicalin on the AKR1B10 and PI3K/AKT signaling pathways appear to be critical for its inhibitory effects on HCC cell proliferation. These results provide new insights into the mechanism of action of wild Baicalin and support its potential as a therapeutic approach for HCC treatment.

Quantitative information from gadobenate dimeglumine-enhanced MRI can predict proliferative subtype of solitary hepatocellular carcinoma: a multicenter retrospective study

OBJECTIVES

To investigate the value of quantitative parameters derived from gadobenate dimeglumine-enhanced magnetic resonance imaging (MRI) for predicting molecular subtype of hepatocellular carcinoma (HCC) and overall survival.

METHODS

This multicenter retrospective study included 218 solitary HCC patients who underwent gadobenate dimeglumine-enhanced MRI. All HCC lesions were resected and pathologically confirmed. The lesion-to-liver contrast enhancement ratio (LLCER) and lesion-to-liver contrast (LLC) were measured in the hepatobiliary phase. Potential risk factors for proliferative HCC were assessed by logistic regression. The ability of LLCER and LLC to predict proliferative HCC was assessed by the receiver operating characteristic (ROC) curve. Prognostic factors were evaluated using the Cox proportional hazards regression model for survival outcomes.

RESULTS

LLCER was an independent predictor of proliferative HCC (odds ratio, 0.015; 95% confidence interval [CI], 0.008-0.022; p < 0.001). The area under the ROC curve was 0.812 (95% CI, 0.748-0.877), higher than that of LLC, alpha-fetoprotein > 100 ng/ml, satellite nodules, and rim arterial phase hyperenhancement (all p ≤ 0.001). HCC patients with LLCER < -4.59% had a significantly higher incidence of proliferative HCC than those with the LLCER ≥ -4.59%. During the follow-up period, LLCER was an independent predictor of overall survival (hazard ratio, 0.070; 95% CI, 0.015-0.324; p = 0.001) in HCC patients.

CONCLUSIONS

Gadobenate dimeglumine-enhanced quantitative parameter in the hepatobiliary phase can predict the proliferative subtype of solitary HCC with a moderately high accuracy.

CLINICAL RELEVANCE STATEMENT

Quantitative information from gadobenate dimeglumine-enhanced MRI can provide crucial information on hepatocellular carcinoma subtypes. It might be valuable to design novel therapeutic strategies, such as targeted therapies or immunotherapy.

KEY POINTS

• The lesion-to-liver contrast enhancement ratio (LLCER) is an independent predictor of proliferative hepatocellular carcinoma (HCC). • The ability of LLCER to predict proliferative HCC outperformed lesion-to-liver contrast, alpha-fetoprotein > 100 ng/ml, satellite nodules, and rim arterial phase hyperenhancement. • HCC patients with LLCER < -4.59% had a significantly higher incidence of proliferative HCC than those with the LLCER ≥ -4.59%.

Simulation of Image-Guided Microwave Ablation Therapy Using a Digital Twin Computational Model

Emerging computational tools such as healthcare digital twin modeling are enabling the creation of patient-specific surgical planning, including microwave ablation to treat primary and secondary liver cancers. Healthcare digital twins (DTs) are anatomically one-to-one biophysical models constructed from structural, functional, and biomarker-based imaging data to simulate patient-specific therapies and guide clinical decision-making. In microwave ablation (MWA), tissue-specific factors including tissue perfusion, hepatic steatosis, and fibrosis affect therapeutic extent, but current thermal dosing guidelines do not account for these parameters. This study establishes an MR imaging framework to construct three-dimensional biophysical digital twins to predict ablation delivery in livers with 5 levels of fat content in the presence of a tumor. Four microwave antenna placement strategies were considered, and simulated microwave ablations were then performed using 915 MHz and 2450 MHz antennae in Tumor Naïve DTs (control), and Tumor Informed DTs at five grades of steatosis. Across the range of fatty liver steatosis grades, fat content was found to significantly increase ablation volumes by approximately 29-l42% in the Tumor Naïve and 55-60% in the Tumor Informed DTs in 915 MHz and 2450 MHz antenna simulations. The presence of tumor did not significantly affect ablation volumes within the same steatosis grade in 915 MHz simulations, but did significantly increase ablation volumes within mild-, moderate-, and high-fat steatosis grades in 2450 MHz simulations. An analysis of signed distance to agreement for placement strategies suggests that accounting for patient-specific tumor tissue properties significantly impacts ablation forecasting for the preoperative evaluation of ablation zone coverage.

MRI proton density fat fraction for estimation of tumor grade in steatotic hepatocellular carcinoma

OBJECTIVES

Image-based detection of intralesional fat in focal liver lesions has been established in diagnostic guidelines as a feature indicative of hepatocellular carcinoma (HCC) and associated with a favorable prognosis. Given recent advances in MRI-based fat quantification techniques, we investigated a possible relationship between intralesional fat content and histologic tumor grade in steatotic HCCs.

METHODS

Patients with histopathologically confirmed HCC and prior MRI with proton density fat fraction (PDFF) mapping were retrospectively identified. Intralesional fat of HCCs was assessed using an ROI-based analysis and the median fat fraction of steatotic HCCs was compared between tumor grades G1-3 with non-parametric testing. ROC analysis was performed in case of statistically significant differences (p < 0.05). Subgroup analyses were conducted for patients with/without liver steatosis and with/without liver cirrhosis.

RESULTS

A total of 57 patients with steatotic HCCs (62 lesions) were eligible for analysis. The median fat fraction was significantly higher for G1 lesions (median [interquartile range], 7.9% [6.0─10.7%]) than for G2 (4.4% [3.2─6.6%]; p = .001) and G3 lesions (4.7% [2.8─7.8%]; p = .036). PDFF was a good discriminator between G1 and G2/3 lesions (AUC .81; cut-off 5.8%, sensitivity 83%, specificity 68%) with comparable results in patients with liver cirrhosis. In patients with liver steatosis, intralesional fat content was higher than in the overall sample, with PDFF performing better in distinguishing between G1 and G2/3 lesions (AUC .92; cut-off 8.8%, sensitivity 83%, specificity 91%).

CONCLUSIONS

Quantification of intralesional fat using MRI PDFF mapping allows distinction between well- and less-differentiated steatotic HCCs.

CLINICAL RELEVANCE

PDFF mapping may help optimize precision medicine as a tool for tumor grade assessment in steatotic HCCs. Further investigation of intratumoral fat content as a potential prognostic indicator of treatment response is encouraged.

KEY POINTS

• MRI proton density fat fraction mapping enables distinction between well- (G1) and less- (G2 and G3) differentiated steatotic hepatocellular carcinomas. • In a retrospective single-center study with 62 histologically proven steatotic hepatocellular carcinomas, G1 tumors showed a higher intralesional fat content than G2 and G3 tumors (7.9% vs. 4.4% and 4.7%; p = .004). • In liver steatosis, MRI proton density fat fraction mapping was an even better discriminator between G1 and G2/G3 steatotic hepatocellular carcinomas.

Hepatobiliary-phase gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid MRI for pretreatment prediction of efficacy-to-standard-therapies based on Barcelona Clinic Liver Cancer algorithm: an up-to-date review

Recent advances in systemic therapy have had major impacts on treatment strategies for hepatocellular carcinoma (HCC). The 2022 Barcelona Clinic Liver Cancer (BCLC) guidelines incorporate a new section on clinical decision-making for personalized medicine, although the first treatment suggested by the BCLC guidelines is based on solid scientific evidence. More than ever before, the appropriate treatment strategy must be selected prior to the initiation of therapy for HCC. Gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid magnetic resonance imaging (Gd-EOB-DTPA-MRI) is essential for liver imaging and the hepatobiliary phase (HBP) of EOB-MRI reflects the expression of organic anion transporting polypeptide (OATP) transporters. Molecules associated with OATP expression are relevant in the molecular classification of HCC subclasses, and EOB-MRI is becoming increasingly important with advances in the molecular and genetic understanding of HCC. In this review, we describe imaging findings for the pretreatment prediction of response to standard therapies for HCC based on the BCLC algorithm using the HBP of EOB-MRI, with specific attention to the molecular background of OATPs. A more complete understanding of these findings will help radiologists suggest appropriate treatments and clinical follow-ups and could lead to the development of more personalized treatment strategies in the future. CLINICAL RELEVANCE STATEMENT: In the coming era of personalized medicine, HBP of EOB-MRI reflecting molecular and pathological factors could play a predictive role in the therapeutic efficacy of HCC and contribute to treatment selection. KEY POINTS: • Imaging features of hepatobiliary phase predict treatment efficacy prior to therapy and contribute to treatment choice. • Wnt/β-catenin activation associated with organic anion transporting polypeptide expression is involved in the tumor immune microenvironment and chemo-responsiveness. • Peritumoral hypointensity of hepatobiliary phase reflecting microvascular invasion affects the therapeutic efficacy of locoregional to systemic therapy.

Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know

Ongoing discoveries in cancer genomics and epigenomics have revolutionized clinical oncology and precision health care. This knowledge provides unprecedented insights into tumor biology and heterogeneity within a single tumor, among primary and metastatic lesions, and among patients with the same histologic type of cancer. Large-scale genomic sequencing studies also sparked the development of new tumor classifications, biomarkers, and targeted therapies. Because of the central role of imaging in cancer diagnosis and therapy, radiologists need to be familiar with the basic concepts of genomics, which are now becoming the new norm in oncologic clinical practice. By incorporating these concepts into clinical practice, radiologists can make their imaging interpretations more meaningful and specific, facilitate multidisciplinary clinical dialogue and interventions, and provide better patient-centric care. This review article highlights basic concepts of genomics and epigenomics, reviews the most common genetic alterations in cancer, and discusses the implications of these concepts on imaging by organ system in a case-based manner. This information will help stimulate new innovations in imaging research, accelerate the development and validation of new imaging biomarkers, and motivate efforts to bring new molecular and functional imaging methods to clinical radiology. Keywords: Oncology, Cancer Genomics, Epignomics, Radiogenomics, Imaging Markers Supplemental material is available for this article. © RSNA, 2023.

Head-to-head comparison of 18F-FAPI and 18F-FDG PET/CT in staging and therapeutic management of hepatocellular carcinoma

BACKGROUND

Fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) has limitations in staging hepatocellular carcinoma (HCC). The recently introduced 18F-labeled fibroblast-activation protein inhibitor (FAPI) has shown promising prospects in detection of HCC lesions. This study aimed to investigate the initial staging and restaging performance of 18F-FAPI PET/CT compared to 18F-FDG PET/CT in HCC.

METHODS

This prospective study enrolled histologically confirmed HCC patients from March 2021 to September 2022. All patients were examined with 18F-FDG PET/CT and 18F-FAPI PET/CT within 1 week. The maximum standard uptake value (SUVmax), tumor-to-background ratio (TBR), and diagnostic accuracy were compared between the two modalities.

RESULTS

A total of 67 patients (57 men; median age, 57 [range, 32-83] years old) were included. 18F-FAPI PET showed higher SUVmax and TBR values than 18F-FDG PET in the intrahepatic lesions (SUVmax: 6.7 vs. 4.3, P < 0.0001; TBR: 3.9 vs. 1.7, P < 0.0001). In diagnostic performance, 18F-FAPI PET/CT had higher detection rate than 18F-FDG PET/CT in intrahepatic lesions [92.2% (238/258) vs 41.1% (106/258), P < 0.0001] and lymph node metastases [97.9% (126/129) vs 89.1% (115/129), P = 0.01], comparable in distant metastases [63.6% (42/66) vs 69.7% (46/66), P > 0.05]. 18F-FAPI PET/CT detected primary tumors in 16 patients with negative 18F-FDG, upgraded T-stages in 12 patients and identified 4 true positive findings for local recurrence than 18F-FDG PET, leading to planning therapy changes in 47.8% (32/67) of patients.

CONCLUSIONS

18F-FAPI PET/CT identified more primary lesions, lymph node metastases than 18F-FDG PET/CT in HCC, which is helpful to improve the clinical management of HCC patients.

TRIAL REGISTRATION

Clinical Trials, NCT05485792 . Registered 1 August 2022, Retrospectively registered.

Imaging prognostication and tumor biology in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, and represents a significant global health burden with rising incidence rates, despite a more thorough understanding of the etiology and biology of HCC, as well as advancements in diagnosis and treatment modalities. According to emerging evidence, imaging features related to tumor aggressiveness can offer relevant prognostic information, hence validation of imaging prognostic features may allow for better noninvasive outcomes prediction and inform the selection of tailored therapies, ultimately improving survival outcomes for patients with HCC.

Identifying Proliferative Hepatocellular Carcinoma at Pretreatment CT: Implications for Therapeutic Outcomes after Transarterial Chemoembolization

Background Hepatocellular carcinomas (HCCs) can be divided into proliferative and nonproliferative types, which may have implications for outcomes after conventional transarterial chemoembolization (cTACE). Biopsy to identify proliferative HCC is not routinely performed before cTACE. Purpose To develop and validate a predictive model for identifying proliferative HCCs using CT imaging features and to compare therapeutic outcomes between predicted proliferative and nonproliferative HCCs after cTACE according to this model. Materials and Methods This retrospective multicenter study included adults with HCC who underwent liver resection or cTACE between August 2013 and December 2020. A CT-based predictive model for identifying proliferative HCCs was developed and externally validated in a cohort that underwent resection. Diagnostic performance was calculated for the model. Thereafter, patients in the cTACE cohort were stratified into groups with predicted proliferative or nonproliferative HCCs according to the model. The primary outcome was overall survival (OS), and the secondary outcomes were tumor response rate and progression-free survival (PFS). These were compared between the two groups with use of the χ2 test and the log-rank test. Results A total of 1194 patients (1021 men; mean age, 54 years ± 12 [SD]; median follow-up time, 29.1 months) were included. The predictive model, named the SMARS score, incorporated lobulated shape, mosaic architecture, α-fetoprotein levels, rim arterial phase hyperenhancement, and satellite lesions. The area under the receiver operating characteristic curve for the SMARS score was 0.83 for the training cohort and 0.80 for the validation cohort. According to the SMARS score, patients with predicted proliferative HCCs (n = 114) had lower tumor response rate (48% vs 71%; P < .001) and worse PFS (6.6 months vs 12.4 months; P < .001) and OS (14.4 months vs 38.7 months; P < .001) than those with nonproliferative HCCs (n = 263). Conclusion The predictive model demonstrated good performance for identifying proliferative HCCs. According to the SMARS score, patients with predicted proliferative HCCs have worse prognosis than those with predicted nonproliferative HCCs after cTACE. © RSNA, 2023 Supplemental material is available for this article.

Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma through dynamic contrast-enhanced magnetic resonance imaging-based radiomics

BACKGROUND

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is closely related to aggressive phenotype, gene mutation, carcinogenic pathway, and immunohistochemical markers and is a strong independent predictor of early recurrence and poor prognosis. With the development of imaging technology, successful applications of contrast-enhanced magnetic resonance imaging (MRI) have been reported in identifying the MTM-HCC subtype. Radiomics, as an objective and beneficial method for tumour evaluation, is used to convert medical images into high-throughput quantification features that greatly push the development of precision medicine.

AIM

To establish and verify a nomogram for preoperatively identifying MTM-HCC by comparing different machine learning algorithms.

METHODS

This retrospective study enrolled 232 (training set, 162; test set, 70) hepatocellular carcinoma patients from April 2018 to September 2021. A total of 3111 radiomics features were extracted from dynamic contrast-enhanced MRI, followed by dimension reduction of these features. Logistic regression (LR), K-nearest neighbour (KNN), Bayes, Tree, and support vector machine (SVM) algorithms were used to select the best radiomics signature. We used the relative standard deviation (RSD) and bootstrap methods to quantify the stability of these five algorithms. The algorithm with the lowest RSD represented the best stability, and it was used to construct the best radiomics model. Multivariable logistic analysis was used to select the useful clinical and radiological features, and different predictive models were established. Finally, the predictive performances of the different models were assessed by evaluating the area under the curve (AUC).

RESULTS

The RSD values based on LR, KNN, Bayes, Tree, and SVM were 3.8%, 8.6%, 4.3%, 17.7%, and 17.4%, respectively. Therefore, the LR machine learning algorithm was selected to construct the best radiomics signature, which performed well with AUCs of 0.766 and 0.739 in the training and test sets, respectively. In the multivariable analysis, age [odds ratio (OR) = 0.956, P = 0.034], alpha-fetoprotein (OR = 10.066, P < 0.001), tumour size (OR = 3.316, P = 0.002), tumour-to-liver apparent diffusion coefficient (ADC) ratio (OR = 0.156, P = 0.037), and radiomics score (OR = 2.923, P < 0.001) were independent predictors of MTM-HCC. Among the different models, the predictive performances of the clinical-radiomics model and radiological-radiomics model were significantly improved compared to those of the clinical model (AUCs: 0.888 vs 0.836, P = 0.046) and radiological model (AUCs: 0.796 vs 0.688, P = 0.012), respectively, in the training set, highlighting the improved predictive performance of radiomics. The nomogram performed best, with AUCs of 0.896 and 0.805 in the training and test sets, respectively.

CONCLUSION

The nomogram containing radiomics, age, alpha-fetoprotein, tumour size, and tumour-to-liver ADC ratio revealed excellent predictive ability in preoperatively identifying the MTM-HCC subtype.

Deep Learning Model Based on Contrast-Enhanced Computed Tomography Imaging to Predict Postoperative Early Recurrence after the Curative Resection of a Solitary Hepatocellular Carcinoma

We aimed to develop the deep learning (DL) predictive model for postoperative early recurrence (within 2 years) of hepatocellular carcinoma (HCC) based on contrast-enhanced computed tomography (CECT) imaging. This study included 543 patients who underwent initial hepatectomy for HCC and were randomly classified into training, validation, and test datasets at a ratio of 8:1:1. Several clinical variables and arterial CECT images were used to create predictive models for early recurrence. Artificial intelligence models were implemented using convolutional neural networks and multilayer perceptron as a classifier. Furthermore, the Youden index was used to discriminate between high- and low-risk groups. The importance values of each explanatory variable for early recurrence were calculated using permutation importance. The DL predictive model for postoperative early recurrence was developed with the area under the curve values of 0.71 (test datasets) and 0.73 (validation datasets). Postoperative early recurrence incidences in the high- and low-risk groups were 73% and 30%, respectively (p = 0.0057). Permutation importance demonstrated that among the explanatory variables, the variable with the highest importance value was CECT imaging analysis. We developed a DL model to predict postoperative early HCC recurrence. DL-based analysis is effective for determining the treatment strategies in patients with HCC.

A new intronic quantitative PCR method led to the discovery of transformation from human ascites to murine malignancy in a mouse model

Purpose

To establish a fast and accurate detection method for interspecies contaminations in the patient-derived xenograft (PDX) models and cell lines, and to elucidate possible mechanisms if interspecies oncogenic transformation is detected.

Methods

A fast and highly sensitive intronic qPCR method detecting Gapdh intronic genomic copies was developed to quantify if cells were human or murine or a mixture. By this method, we documented that murine stromal cells were abundant in the PDXs; we also authenticated our cell lines to be human or murine.

Results

In one mouse model, GA0825-PDX transformed murine stromal cells into a malignant tumorigenic murine P0825 cell line. We traced the timeline of this transformation and discovered three subpopulations descended from the same GA0825-PDX model: epithelium-like human H0825, fibroblast-like murine M0825, and main passaged murine P0825 displayed differences in tumorigenic capability in vivo. P0825 was the most aggressive and H0825 was weakly tumorigenic. Immunofluorescence (IF) staining revealed that P0825 cells highly expressed several oncogenic and cancer stem cell markers. Whole exosome sequencing (WES) analysis revealed that TP53 mutation in the human ascites IP116-generated GA0825-PDX may have played a role in the human-to-murine oncogenic transformation.

Conclusion

This intronic qPCR is able to quantify human/mouse genomic copies with high sensitivity and within a time frame of a few hours. We are the first to use intronic genomic qPCR for authentication and quantification of biosamples. Human ascites transformed murine stroma into malignancy in a PDX model.

18F-FDG PET/CT-based radiomics nomogram for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma: a two-center study

PURPOSE

To explore the potential of β-2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in the evaluation of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC) and to apply radiomics approach to build a radiomics nomogram for predicting MTM-HCC.

METHODS

This study included 140 (training cohort:101; validation cohort:39) HCC patients who underwent preoperative ¹⁸F-FDG PET/CT at two institutions. The clinical features and tumor FDG metabolism measured by the tumor-to-liver ratio (TLR) via ¹⁸F-FDG PET/CT were retrospectively collected. Radiomics features were extracted from ¹⁸F-FDG PET/CT images and a radiomics score (Rad-score) was calculated. A radiomics nomogram was then constructed by combining Rad-score and independent clinical features and was assessed with a calibration curve. The performance of the radiomics nomogram, Rad-score and TLR was evaluated by receiver operating characteristic (ROC) curves and decision curve analysis (DCA).

RESULTS

A total of six top weighted radiomics features were selected from PET/CT images by the least absolute shrinkage and selection operator (LASSO) regression algorithm and were used to construct a Rad-score. Multivariate analysis identified Rad-score (OR = 2.183, P = 0.004), age ≤ 50 years (OR = 3.136, P = 0.036), AST > 40U/L (OR = 0.270, P = 0.017) and TLR (OR = 1.641, P = 0.049) as independent predictors of MTM-HCC. The radiomics nomogram had a higher area under the curves (AUCs) than the Rad-score and TLR for predicting MTM-HCC in both training (0.849 [95% CI 0.774-0.924] vs. 0.764 [95% CI 0.669-0.843], 0.763 [95% CI 0.668-0.842]) and validation (0.749 [95% CI 0.584-0.873] vs. 0.690 [95% CI 0.522-0.828], 0.541 [95% CI 0.374-0.701]) cohorts. DCA showed the radiomics nomogram to be more clinically useful than Rad-score and TLR.

CONCLUSIONS

Tumor FDG metabolism is significantly associated with MTM-HCC. A ¹⁸F-FDG PET/CT-based radiomics nomogram may be useful for preoperatively predicting the MTM subtype in primary HCC patients, contributing to pretreatment decision-making.

Identification and development of TP53 mutation-associated Long non-coding RNAs signature for optimized prognosis assessment and treatment selection in hepatocellular carcinoma

BACKGROUND

The TP53 gene is estimated to be mutated in over 50% of tumors, with the majority of tumors exhibiting abnormal TP53 signaling pathways. However, the exploration of TP53 mutation-related LncRNAs in Hepatocellular carcinoma (HCC) remains incomplete. This study aims to identify such LncRNAs and enhance the prognostic accuracy for Hepatoma patients.

MATERIAL AND METHODS

Differential gene expression was identified using the "limma" package in R. Prognosis-related LncRNAs were identified via univariate Cox regression analysis, while a prognostic model was crafted using multivariate Cox regression analysis. Survival analysis was conducted using Kaplan-Meier curves. The precision of the prognostic model was assessed through ROC analysis. Subsequently, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were executed on the TCGA dataset via the TIDE database. Fractions of 24 types of immune cell infiltration were obtained from NCI Cancer Research Data Commons using deconvolution techniques. The protein expression levels encoded by specific genes were obtained through the TPCA database.

RESULTS

In this research, we have identified 85 LncRNAs associated with TP53 mutations and developed a corresponding signature referred to as TP53MLncSig. Kaplan-Meier analysis revealed a lower 3-year survival rate in high-risk patients (46.9%) compared to low-risk patients (74.2%). The accuracy of the prognostic TP53MLncSig was further evaluated by calculating the area under the ROC curve. The analysis yielded a 5-year ROC score of 0.793, confirming its effectiveness. Furthermore, a higher score for TP53MLncSig was found to be associated with an increased response rate to immune checkpoint blocker (ICB) therapy (p = .005). Patients possessing high-risk classification exhibited lower levels of P53 protein expression and higher levels of genomic instability.

CONCLUSION

The present study aimed to identify and validate LncRNAs associated with TP53 mutations. We constructed a prognostic model that can predict chemosensitivity and response to ICB therapy in HCC patients. This novel approach sheds light on the role of LncRNAs in TP53 mutation and provides valuable resources for analyzing patient prognosis and treatment selection.

Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study

PURPOSE

To develop a radiomics-based hepatocellular carcinoma (HCC) grade classifier model based on data from gadoxetic acid-enhanced MRI.

METHODS

This retrospective study included 137 patients who underwent hepatectomy for a single HCC and gadoxetic acid-enhanced MRI within 60 days before surgery. HCC grade was categorized as low or high (modified Edmondson-Steiner grade I-II vs. III-IV). We used the hepatobiliary phase (HBP), portal venous phase, T2-weighted image(T2WI), and T1-weighted image(T1WI). From the volume of interest in HCC, 833 radiomic features were extracted. Radiomic and clinical features were selected using a random forest regressor, and the classification model was trained and validated using a random forest classifier and tenfold stratified cross-validation. Eight models were developed using the radiomic features alone or by combining the radiomic and clinical features. Models were validated with internal enrolled data (internal validation) and a dataset (28 patients) at a separate institution (external validation). The area under the curve (AUC) of the validation results was compared using the DeLong test.

RESULTS

In internal and external validation, the HBP radiomics-only model showed the highest AUC (internal 0.80 ± 0.09, external 0.70 ± 0.09). In external validation, all models showed lower AUC than those for internal validation, while the T2WI and T1WI models failed to predict the HCC grade (AUC 0.30-0.58) in contrast to the internal validation results (AUC 0.67-0.78).

CONCLUSION

The radiomics-based machine learning model from gadoxetic acid-enhanced liver MRI could distinguish between low- and high-grade HCCs. The radiomics-only HBP model showed the best AUC among the eight models, good performance in internal validation, and fair performance in external validation.

Intravoxel incoherent motion imaging used to assess tumor microvascular changes after transarterial chemoembolization in a rabbit VX2 liver tumor model

Purpose

To evaluate the correlation between microvascular density (MVD) and intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) parameters and the effect of glycolytic flux after transarterial chemoembolization (TACE) in a rabbit VX2 liver tumor.

Materials and methods

VX2 liver tumor allografts in 15 New Zealand white rabbits were treated with sterile saline (control group, n = 5) or lipiodol-doxorubicin emulsion (experimental group, n = 10). MRI was performed 2 weeks after the procedure to evaluate IVIM parameters, including apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (PF). All animal samples were taken of the tumor and surrounding liver. Immunostaining for CD31, CD34, CD105, and VEGF was used to evaluate MVD. The protein expression of Glut4, HK2, PKM2, LDHA, and MCT1 was determined using western blotting. Pearson correlation tests were used to analyze the relationship between MVD and IVIM parameters.

Results

D* value in the peritumoral region was negatively correlated with CD34 (r = -0.71, P = 0.01). PF value positively correlated with CD34 (r = 0.68, P = 0.015), CD105 (r = 0.76, P = 0.004) and VEGF (r = 0.72, P = 0.008) in the peritumoral region. Glut4, HK2, PKM2, and MCT1 in the peritumoral regions were higher in the experimental group than in the control group (all P < 0.05).

Conclusion

IVIM parameters were correlated with MVD in the intratumoral and peritumoral regions after TACE in a rabbit liver tumor model. The angiogenesis reflected by MVD may be related to changes of glycolytic flux.

Serum alanine aminotransferase to hemoglobin ratio and radiological features predict the prognosis of postoperative adjuvant TACE in patients with hepatocellular carcinoma

Objective

To explore the prognostic value of radiological features and serum indicators in patients treated with postoperative adjuvant transarterial chemoembolization (PA-TACE) and develop a prognostic model to predict the overall survival (OS) of patients with hepatocellular carcinoma (HCC) treated with PA-TACE.

Method

We enrolled 112 patients (75 in the training cohort and 37 in the validation cohort) with HCC treated with PA-TACE after surgical resection at the Affiliated Hospital of Nantong University between January 2012 and June 2015. The independent OS predictors were determined using univariate and multivariate regression analyses. Decision curve analyses and time-dependent receiver operating characteristic curve analysis was used to verify the prognostic performance of the different models; the best model was selected to establish a multi-dimensional nomogram for predicting the OS of HCC patients treated with PA-TACE.

Result

Multivariate regression analyses indicated that rim-like arterial phase enhancement (IRE), peritumor capsule (PTC), and alanine aminotransferase to hemoglobin ratio (AHR) were independent predictors of OS after PA-TACE. The combination of AHR had the best clinical net benefit and we constructed a prognostic nomogram based on IRE, PTC, and AHR. The calibration curve showed good fit between the predicted nomogram’s curve and the observed curve.

Conclusion

Our preliminary study confirmed the prognostic value of AHR, PTC, and IRE and established a nomogram that can predict the OS after PA-TACE treatment in patients with HCC.

Evaluation of the efficacy of transcatheter arterial embolization combined with apatinib on rabbit VX2 liver tumors by intravoxel incoherent motion diffusion-weighted MR imaging

Background and purpose

It is crucial to evaluate the efficacy, recurrence, and metastasis of liver tumors after clinical treatment. This study aimed to investigate the value of Introvoxel Incoherent Motion (IVIM) imaging in the evaluation of rabbit VX2 liver tumors treated with Transcatheter Arterial Embolization (TAE) combined with apatinib.

Methods

Twenty rabbit VX2 liver tumor models were established and randomly divided into either the experimental group (n=15) or the control group (n=5). The experimental group was treated with TAE combined with oral apatinib after successful tumor inoculation, while no treatment was administered following inoculation in the control group. IVIM sequence scan was performed in the experimental group before treatment, at 7 and 14 days after treatment. All rabbits were sacrificed after the last scan of the experimental group. Marginal tissues from the tumors of both groups were excised for immunohistochemical analysis to observe and compare the expression of microvessel density (MVD). The alterations of IVIM-related parameters of tumor tissues in the experimental group, including Apparent Diffusion Coefficient (ADC), True Diffusion Coefficient (D), Pseudodiffusion Coefficient (D*), and Perfusion Fraction (f) were compared at different periods, and the correlation between these parameters and MVD was analyzed.

Results

After treatment, ADC and D values significantly increased, whereas D* and f values both decreased, with statistically significant differences.(P<0.05). The average tumor MVD of the experimental group after TAE combined with apatinib ((33.750 ± 6.743) bars/high power field (HPF)) was significantly lower than that in the control group ((64.200 ± 10.164) bars/HPF)). Moreover, D and f were positively correlated with tumor MVD in the experimental group (r=0.741 for D and r=0.668 for f, P<0.05). However, there was no significant correlation between ADC and D* values of the experimental group and tumor MVD (r=0.252 for ADC and r=0.198 for D*, P>0.05).

Conclusion

IVIM imaging can be employed to evaluate the efficacy of TAE combined with apatinib in rabbit VX2 liver tumors. Alterations in D and f values were closely related to the MVD of liver tumor tissues.

MRI features of histologic subtypes of hepatocellular carcinoma: correlation with histologic, genetic, and molecular biologic classification

HCC is a heterogeneous group of tumors in terms of histology, genetic aberration, and protein expression. Advancements in imaging techniques have allowed imaging diagnosis to become a critical part of managing HCC in the clinical setting, even without pathologic diagnosis. With the identification of many HCC subtypes, there is increasing correlative evidence between imaging phenotypes and histologic, molecular, and genetic characteristics of various HCC subtypes. In this review, current knowledge of histologic heterogeneity of HCC correlated to features on gadolinium-enhanced dynamic liver MRI will be discussed. In addition, HCC subtype classification according to transcriptomic profiles will be outlined with descriptions of histologic, genetic, and molecular characteristics of some relatively well-established morphologic subtypes, namely the low proliferation class (steatohepatitic HCC and CTNNB1-mutated HCC) and the high proliferation class (macrotrabecular-massive HCC (MTM-HCC), scirrhous HCC, and CK19-positive HCC). Characteristics of sarcomatoid HCC and fibrolamellar HCC will also be discussed. Further research on radiological characteristics of HCC subtypes may ultimately enable non-invasive diagnosis and serve as a biomarker in predicting prognosis, molecular characteristics, and therapeutic response. In the era of precision medicine, a multidisciplinary effort to develop an integrated radiologic and clinical diagnostic system of various HCC subtypes is necessary. KEY POINTS: • HCC is a heterogeneous group of tumors in terms of histology, genetic aberration, and protein expression, which can be divided into many subtypes according to transcriptome profiles. • There is increasing evidence of a correlation between imaging phenotypes and histologic, genetic, and molecular biologic characteristics of various HCC subtypes. • Imaging characteristics may ultimately enable non-invasive diagnosis and subtype characterization, serving as a biomarker for predicting prognosis, molecular characteristics, and therapeutic response.

Morphomolecular Classification Update on Hepatocellular Adenoma, Hepatocellular Carcinoma, and Intrahepatic Cholangiocarcinoma

Hepatocellular adenomas (HCAs), hepatocellular carcinomas (HCCs), and intrahepatic cholangiocarcinomas (iCCAs) are a highly heterogeneous group of liver tumors with diverse pathomolecular features and prognoses. High-throughput gene sequencing techniques have allowed discovery of distinct genetic and molecular underpinnings of these tumors and identified distinct subtypes that demonstrate varied clinicobiologic behaviors, imaging findings, and complications. The combination of histopathologic findings and molecular profiling form the basis for the morphomolecular classification of liver tumors. Distinct HCA subtypes with characteristic imaging findings and complications include HNF1A-inactivated, inflammatory, β-catenin-activated, β-catenin-activated inflammatory, and sonic hedgehog HCAs. HCCs can be grouped into proliferative and nonproliferative subtypes. Proliferative HCCs include macrotrabecular-massive, TP53-mutated, scirrhous, clear cell, fibrolamellar, and sarcomatoid HCCs and combined HCC-cholangiocarcinoma. Steatohepatitic and β-catenin-mutated HCCs constitute the nonproliferative subtypes. iCCAs are classified as small-duct and large-duct types on the basis of the level of bile duct involvement, with significant differences in pathogenesis, molecular signatures, imaging findings, and biologic behaviors. Cross-sectional imaging modalities, including multiphase CT and multiparametric MRI, play an essential role in diagnosis, staging, treatment response assessment, and surveillance. Select imaging phenotypes can be correlated with genetic abnormalities, and identification of surrogate imaging markers may help avoid genetic testing. Improved understanding of morphomolecular features of liver tumors has opened new areas of research in the targeted therapeutics and management guidelines. The purpose of this article is to review imaging findings of select morphomolecular subtypes of HCAs, HCCs, and iCCAs and discuss therapeutic and prognostic implications. Online supplemental material is available for this article. ^©RSNA, 2022.

Repeatability and Image Quality of IDEAL-IQ in Human Lumbar Vertebrae for Fat and Iron Quantification across Acquisition Parameters

Echo asymmetry and least square estimation-IQ (IDEAL-IQ) were used to quantify fat and iron to verify the effects of collection parameters on repeatability and image quality of water and fat in human vertebral body. Six IDEAL-IQ sequences were used to scan 48 healthy adult women. Reproducibility of fat and iron quantification and image quality were assessed for six IDEAL-IQ sequences. The results showed that the correlation index (0.987, 0.721) of FF and R2∗ between scans of sequence 2 was higher than that of other sequences, and the consistency of fat quantification was better than that of iron (0.860 vs. 0.579) (P < 0.001). Sequence 2 had the highest image quality score (4.9) and the lowest CV score (9.2%). In the FF figure, SNR (18.8) and CNR (17.8 ± 6.4) were the highest, while CV was the lowest (36.7%, 36.1%). In the R2∗ figure, sequence 3 had the highest SNR (21.8) and CNR (20.5), but its CV (51.8% and 56.1%) was significantly higher than that of sequence 2. The occurrence of fat-water exchange (FWS) was lowest in sequence 2 and sequence 4 (0, N = 96). In conclusion, the fat quantification of IDEAL-IQ was robust to the changes of collection parameters, and section thickness (ST) had a certain effect on maintaining good repeatability of R2∗ quantification. The higher the ST was, the better the image quality of FF and R2∗ was maintained and stable and the less the occurrence of FWS.

Macrotrabecular-Massive Hepatocellular Carcinoma: What Should We Know?

Hepatocellular carcinoma is one of the most common malignancies globally. Recently, a newly identified histological subtype, designated as "macrotrabecular-massive hepatocellular carcinoma" (MTM-HCC), has been associated with an aggressive phenotype and has received extensive attention. MTM-HCC was a strong independent prognostic predictor of early and overall recurrence because it is closely related to tumor molecular subclass, gene mutation, carcinogenesis pathways, and immunohistochemical markers. In addition, preoperative imaging examination can potentially provide an essential clue for diagnosing MTM-HCC, intratumor necrosis or ischemia is an independent predictor for MTM-HCC on Gd-EOB-DTPA enhanced MRI or CT. Early diagnosis and appropriate treatment of MTM-HCC could prove beneficial for preventing early recurrence and could improve outcomes.

MR Elastography-Based Shear Strain Mapping for Assessment of Microvascular Invasion in Hepatocellular Carcinoma

OBJECTIVES

To evaluate the potential of MR elastography (MRE)-based shear strain mapping to noninvasively predict the presence of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

METHODS

Fifty-nine histopathology-proven HCC patients with conventional 60-Hz MRE examinations (+/-MVI, n = 34/25) were enrolled retrospectively between December 2016 and October 2019, with one subgroup comprising 29/59 patients (+/-MVI, n = 16/13) who also underwent 40- and 30-Hz MRE examinations. Octahedral shear strain (OSS) maps were calculated, and the percentage of peritumoral interface length with low shear strain (i.e., a low-shear-strain length, pLSL, %) was recorded. For OSS-pLSL, differences between the MVI (+) and MVI (-) groups and diagnostic performance at different MRE frequencies were analyzed using the Mann-Whitney test and area under the receiver operating characteristic curve (AUC), respectively.

RESULTS

The peritumor OSS-pLSL was significantly higher in the MVI (+) group than in the MVI (-) group at the three frequencies (all p < 0.01). The AUC of peritumor OSS-pLSL for predicting MVI was good/excellent in all frequency groups (60-Hz: 0.73 (n = 59)/0.80 (n = 29); 40-Hz: 0.84; 30-Hz: 0.90). On further analysis of the 29 cases with all frequencies, the AUCs were not significantly different. As the frequency decreased from 60-Hz, the specificity of OSS increased at 40-Hz (53.8-61.5%) and further increased at 30-Hz (53.8-76.9%), and the sensitivity remained high at lower frequencies (100.0-93.8%) (all p > 0.05).

CONCLUSIONS

MRE-based shear strain mapping is a promising technique for noninvasively predicting the presence of MVI in patients with HCC, and the most recommended frequency for OSS is 30-Hz.

KEY POINTS

• MR elastography (MRE)-based shear strain mapping has the potential to predict the presence of microvascular invasion (MVI) in hepatocellular carcinoma preoperatively. • The low interface shear strain identified at tumor-liver boundaries was highly correlated with the presence of MVI.

[Radiologic Diagnosis of Hepatocellular Carcinoma]

There are various causes of hepatocellular carcinoma, including viral hepatitis, and treatment strategies are often established based on the radiology diagnosis, unlike other carcinomas. The liver imaging reporting and data system (LI-RADS) is a diagnostic system developed by the American College of Radiologists for clear communication and standardized reports of the liver imaging findings. It was recently included in the clinical guidance of the American Association for the Study of Liver Diseases. In addition, the radiologic findings of hepatocellular carcinoma (HCC) enable a prediction of the prognosis after treatment and a diagnosis of diseases because the use of gadoxetic acid MRI has become more common. Thus, the role of radiology for the diagnosis and treatment of HCC is expected to be developed further.

Hepatocellular Carcinoma Staging: Differences Between Radiologic and Pathologic Systems and Relevance to Patient Selection and Outcomes in Liver Transplantation

Liver transplant is indicated with curative intent for patients with early-stage hepatocellular carcinoma (HCC). The radiologic T category is used to determine candidacy and priority of patients on the waiting list. After transplant, the explant liver pathologic TNM stage is used as a predictor of postoperative outcomes and overall prognosis. Although the comparison of radiologic and pathologic T categories for concordance is often considered to be straightforward, the staging conventions significantly differ. Not accounting for these differences is in part the reason for the high rates of radiologic-pathologic discordance reported in the literature, with inconsistent terminology being an additional source of confusion when evaluating concordance. These factors may affect the understanding of important radiopathologic phenotypes of disease and the adequate investigation of their prognostic capabilities. The aims of this article are to provide an overview of the pathologic and radiologic TNM staging systems for HCC while describing staging procedures, emphasize the differences between these staging systems to highlight the limitations of radiologic-pathologic stage correlation, present a review of the literature on the prognostic value of individual features used for HCC staging; and signal significant aspects of preoperative risk stratification that could be improved to positively impact posttransplant outcomes.

Multimodal Imaging-Based Potential Visualization of the Tumor Microenvironment in Bone Metastasis

Bone metastasis (BM) is the most common malignant bone tumor and a significant cause of morbidity and mortality for patients with cancer. Compared to other metastatic organs, bone has unique characteristics in terms of the tumor microenvironment (TME). Precise assessments of the TME in BM could be an important step for developing an optimized management plan for patient care. Imaging approaches for BM have several advantages, such as biopsy not being required, multiple site evaluation, and serial assessment in the same sites. Owing to the developments of new imaging tracers or imaging modalities, bone TME could be visualized using multimodal imaging techniques. In this review, we describe the BM pathophysiology, diagnostic principles of major imaging modalities, and clinically available imaging modalities to visualize the TME in BM. We also discuss how the interactions between various factors affecting the TME could be visualized using multimodal imaging techniques.

Progress of MRI Radiomics in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively.

Objective

This study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC.

Methods

A literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis.

Results

Radiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients.

Conclusion

Radiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.

Pathologic, Molecular, and Prognostic Radiologic Features of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a malignancy with variable biologic aggressiveness based on the tumor grade, presence or absence of vascular invasion, and pathologic and molecular classification. Knowledge and understanding of the prognostic implications of different pathologic and molecular phenotypes of HCC are emerging, with therapeutics that promise to provide improved outcomes in what otherwise remains a lethal cancer. Imaging has a central role in diagnosis of HCC. However, to date, the imaging algorithms do not incorporate prognostic features or subclassification of HCC according to its biologic aggressiveness. Emerging data suggest that some imaging features and further radiologic, pathologic, or radiologic-molecular phenotypes may allow prediction of the prognosis of patients with HCC. An invited commentary by Bashir is available online. Online supplemental material is available for this article. ^©RSNA, 2021.

Decoding Genes: Current Update on Radiogenomics of Select Abdominal Malignancies

Technologic advances in chromosomal analysis and DNA sequencing have enabled genome-wide analysis of cancer cells, yielding considerable data on the genetic basis of malignancies. Evolving knowledge of tumor genetics and oncologic pathways has led to a better understanding of histopathologic features, tumor classification, tumor biologic characteristics, and imaging findings and discovery of targeted therapeutic agents. Radiogenomics is a rapidly evolving field of imaging research aimed at correlating imaging features with gene mutations and gene expression patterns, and it may provide surrogate imaging biomarkers that may supplant genetic tests and be used to predict treatment response and prognosis and guide personalized treatment options. Multidetector CT, multiparametric MRI, and PET with use of multiple radiotracers are some of the imaging techniques commonly used to assess radiogenomic associations. Select abdominal malignancies demonstrate characteristic imaging features that correspond to gene mutations. Recent advances have enabled us to understand the genetics of steatotic and nonsteatotic hepatocellular adenomas, a plethora of morphologic-molecular subtypes of hepatic malignancies, a variety of clear cell and non-clear cell renal cell carcinomas, a myriad of hereditary and sporadic exocrine and neuroendocrine tumors of the pancreas, and the development of targeted therapeutic agents for gastrointestinal stromal tumors based on characteristic KIT gene mutations. Mutations associated with aggressive phenotypes of these malignancies can sometimes be predicted on the basis of their imaging characteristics. Radiologists should be familiar with the genetics and pathogenesis of common cancers that have associated imaging biomarkers, which can help them be integral members of the cancer management team and guide clinicians and pathologists. Online supplemental material is available for this article. ^©RSNA, 2020 See discussion on this article by Luna (pp 1627-1630).

Comparison of Sonazoid contrast-enhanced ultrasound and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid MRI for the histological diagnosis of hepatocellular carcinoma

Background

This study aimed to compare the value of Sonazoid contrast-enhanced ultrasound (SCEUS) with gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid magnetic resonance imaging (EOB-MRI) for histological grading diagnosis, especially for early hepatocellular carcinoma (eHCC).

Methods

A total of 163 histopathologically confirmed HCC lesions were retrospectively collected, including 71 eHCCs (27 hypervascular, 44 non-hypervascular) and 92 advanced HCCs (adHCC) (73 hypervascular, 19 non-hypervascular). We performed SCEUS to evaluate the lesions' vascularity during the portal phase (PP) and the echogenicity during the post-vascular phase (PVP). EOB-MRI was used to determine the signal intensity between lesions and the surrounding liver parenchyma on unenhanced T1-weighted images (pre-contrast ratio) in the hepatobiliary phase (HBP) (post-contrast ratio).

Results

For the PP and PVP of SCEUS (for all lesions), the pre-and post-contrast ratios of EOB-MRI (for all hypervascular lesions) showed statistical differences in the diagnosis of some (but not all) histological grades. For the diagnosis of eHCC, isoechogenicity in the PVP achieved the best diagnostic efficacy [area under the receiver operating characteristic curve (AUC) =0.892]. Whether used independently or in a combination of any form, all indicators failed to produce a higher diagnostic efficacy than PVP. Post- (≥0.610) and pre-contrast ratios (≥0.981) yielded acceptable diagnostic efficacy, with, respectively, accuracy levels of 69.3% and 75.5% and AUC values of 0.719 and 0.736. For eHCC diagnosis, the post-contrast ratio (≥0.625) and combined diagnosis using pre- (≥0.907) and post-contrast ratios (≥0.609) revealed the highest sensitivity (92.6%) for hypervascular lesions and perfect specificity (100%) for non-hypervascular lesions.

Conclusions

Unenhanced T1-weighted images and the HBP of EOB-MRI [regardless of the vascularity in the arterial phase (AP)], and the PP and PVP of SCEUS showed their value in the histological grading diagnosis of HCC. In particular, isoechogenicity in the PVP may have promising diagnostic utility for eHCC.

Advanced CT techniques for assessing hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the sixth-most common cancer in the world, and hepatic dynamic CT studies are routinely performed for its evaluation. Ongoing studies are examining advanced imaging techniques that may yield better findings than are obtained with conventional hepatic dynamic CT scanning. Dual-energy CT-, perfusion CT-, and artificial intelligence-based methods can be used for the precise characterization of liver tumors, the quantification of treatment responses, and for predicting the overall survival rate of patients. In this review, the advantages and disadvantages of conventional hepatic dynamic CT imaging are reviewed and the general principles of dual-energy- and perfusion CT, and the clinical applications and limitations of these technologies are discussed with respect to HCC. Finally, we address the utility of artificial intelligence-based methods for diagnosing HCC.

Pathologic and molecular features of hepatocellular carcinoma: An update

Morphological diversity and several new distinct pathologic subtypes of hepatocellular carcinoma (HCC) are now well-recognized. Recent advances in tumor genomics and transcriptomics have identified several recurrent somatic/genetic alterations that are closely related with histomorphological subtypes and have therefore, greatly improved our understanding of HCC pathogenesis. Pathologic subtyping allows for a diagnosis which is clinically helpful and can have important implication in patient prognostication as some of these subtypes are extremely aggressive with vascular invasion, early recurrence, and worst outcomes. Several targeted treatments are now being considered in HCC, and the reporting of subtypes may be quite useful for personalized therapeutic purpose. This manuscript reviews the recently identified histomorphological subtypes and molecular alterations in HCC.

Macrotrabecular-massive hepatocellular carcinoma: imaging identification and prediction based on gadoxetic acid-enhanced magnetic resonance imaging

OBJECTIVES

To identify image features of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC) and to determine its role in predicting MTM-HCC.

METHODS

Patients who underwent preoperative gadoxetic acid-enhanced MRI and with surgery proven HCC were retrospectively included. Imaging features were assessed according to Liver Imaging Reporting and Data System. Quantitative measurements were recorded. Clinical characteristics and imaging findings were compared between MTM-HCCs and non-MTM-HCCs. Predictive factors of MTM-HCC were screened with univariate analyses and then identified with multivariate logistic regression. A regression-based diagnostic model was constructed. ROC analyses were used to determine cutoff values, AUC, and corresponding 95% confidence interval (CI) of findings. The diagnostic performance was validated by 10-fold cross-validation.

RESULTS

One hundred and forty-one patients with 37 MTM-HCCs were included. Multivariate analyses identified high platelet count (≥ 163.5 × 10³/ul, odds ratio = 3.20; 95% CI: 1.29, 7.96; p = 0.012), low tumor-to-liver ADC ratio (≤ 1.05, odds ratio = 3.05; 95% CI, 1.23 - 7.55; p = 0.016), and necrosis or severe ischemia (odds ratio = 11.61; 95% CI, 3.99 - 33.76, p < 0.001) as independent predictors of MTM-HCC. Necrosis or severe ischemia alone helped identify 86% MTM-HCCs with a specificity of 66%. The average AUCs were 0.81 (95% CI: 0.71, 0.90) for the regression-based diagnostic model, with a sensitivity of 57% and specificity of 92%.

CONCLUSIONS

Necrosis or severe ischemia was a sensitive imaging feature of MTM-HCC. Noninvasive prediction of this subtype can be achieved with good accuracy and excellent specificity when findings were combined.

KEY POINTS

• The macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC) represents an aggressive subtype of HCC and is associated with poor prognosis. • Imaging features of necrosis or severe ischemia alone helped identify 86% MTM-HCCs with a specificity of 66%. • A regression-based diagnostic model including high platelet count (≥ 163.5 × 10³/ul), low tumor-to-liver ADC ratio (≤ 1.05), and necrosis or severe ischemia can provide noninvasive assessment of MTM-HCC with good accuracy and high specificity.

Machine-learning analysis of contrast-enhanced computed tomography radiomics predicts patients with hepatocellular carcinoma who are unsuitable for initial transarterial chemoembolization monotherapy: A multicenter study

•

Patients who are unsuitable for chemoembolization could progress with extrahepatic spread or vascular invasion after initial chemoembolization monotherapy.

•

A radiomics signature based on the machine learning algorithm was identified.

•

The signature combined with clinicoradiologicial predictors could predict TACE-unsuitable patients.

•

The combined model showed improved predictive performance compared with the model without radiomics signature.

•

The combined model could stratify patients into three strata with a low, intermediate, or high risk in training and external testing sets.

Introduction

Due to the high heterogeneity of hepatocellular carcinoma (HCC), patients with non-advanced disease who are unsuitable for initial transarterial chemoembolization (TACE) monotherapy may have the potential to develop extrahepatic spread or vascular invasion. We aimed to develop and independently validate a radiomics-based model for predicting which patients will develop extrahepatic spread or vascular invasion after initial TACE monotherapy (EVIT).

Materials and methods

This retrospective study included 256 HCC patients (training set: n = 136; testing set: n = 120) who underwent TACE as initial therapy between April 2007 and June 2018. Clinicoradiological predictors were selected using multivariate logistic regression and a clinicoradiological model was constructed. The radiomic features were extracted from contrast-enhanced computed tomography (CT) images and a radiomics signature was constructed based on a machine learning algorithm. A combined model integrated clinicoradiological predictor and radiomics signature was developed. The predictive performance of the two models was evaluated and compared based on its discrimination, calibration, and clinical usefulness.

Results

In the training set, 34 (25.0%) patients were confirmed to have EVIT, whereas 26 (21.7%) patients in the testing set had EVIT. When the radiomics signature was added, the combined model showed improved discrimination performance compared to the clinicoradiological model (area under the curves [AUCs] 0.911 vs. 0.772 in the training set; AUCs 0.847 vs. 0.746 in the testing set) and could divide HCC patients into three strata of low, intermediate, or high risk in the two sets. Decision curve analysis demonstrated that the two models were clinically useful, and the combined model provided greater benefits for discriminating patients than the clinicoradiological model.

Conclusions

This study presents a model that integrates clinicoradiological predictors and CT-based radiomics signature that could provide a preoperative individualized prediction of EVIT in patients with HCC.

Nomograms predicting extra- and early intrahepatic recurrence after hepatic resection of hepatocellular carcinoma

BACKGROUND

Extrahepatic recurrence and early intrahepatic recurrence of hepatocellular carcinoma after hepatic resection are indicative of poor prognoses. We aimed to develop nomograms to predict extrahepatic recurrence and early intrahepatic recurrence after hepatic resection.

METHODS

The participants of this study were 1,206 patients who underwent initial and curative hepatic resection for hepatocellular carcinoma. Multivariate logistic regression analyses using the Akaike information criterion were used to construct nomograms to predict extrahepatic recurrence and early intrahepatic recurrence (within 1 year of surgery) at the first recurrence sites after hepatic resection. Performance of each nomogram was evaluated by calibration plots with bootstrapping.

RESULTS

Extrahepatic recurrence was identified in 95 patients (7.9%) and early intrahepatic recurrence in 296 patients (24.5%). Three predictive factors, α-fetoprotein >200 ng/mL, tumor size (3-5 cm or >5 cm vs ≤3 cm), and image-diagnosed venous invasion by computed tomography, were adopted in the final model of the extrahepatic recurrence nomogram with a concordance index of 0.75. Tumor size and 2 additional predictors (ie, multiple tumors and image-diagnosed portal invasion) were adopted in the final model of the early intrahepatic recurrence nomogram with a concordance index of 0.67. The calibration plots showed good agreement between the nomogram predictions of extrahepatic recurrence and early intrahepatic recurrence and the actual observations of extrahepatic recurrence and early intrahepatic recurrence, respectively.

CONCLUSION

We have developed reliable nomograms to predict extrahepatic recurrence and early intrahepatic recurrence of hepatocellular carcinoma after hepatic resection. These are useful for the diagnostic prediction of extrahepatic recurrence and early intrahepatic recurrence and could guide the surgeon's selection of treatment strategies for hepatocellular carcinoma patients.

Transarterial- chemoembolization remains an effective therapy for intermediate-stage hepatocellular carcinoma with preserved liver function

AIM

To evaluate outcomes as well as prognostic factors of transarterial chemoembolization (TACE) in intermediate-stage hepatocellular carcinoma (HCC) with preserved liver function to determine positioning of TACE.

METHODS

Of 158 treatment-naïve patients with intermediate-stage HCC who received initial TACE from February 2007 to January 2016, 113 patients met the following inclusion criteria: no combined therapy within 4 weeks after initial TACE, and Child-Pugh score under 7. Response rate and overall survival were evaluated. The prognostic factors were investigated in univariate and multivariate analyses using Cox proportional hazards models. The deterioration of liver function after repeated TACE was also evaluated.

RESULTS

The response rate was 92.7% (complete response, 63.3%; partial response, 29.4%). The median survival time was 45.2 months. Survival rates at 1, 2, and 3 years were 90.4%, 77.0%, and 60.8% respectively. Age ≥ 75 years (P = 0.022), serum α-fetoprotein level ≥ 200 ng/mL (P = .010), tumor number ≥ 11 (P = 0.008), and heterogeneous enhancement on dynamic computed tomography (P = 0.024) were poor prognostic factors. The deterioration rate of Child-Pugh score and albumin-bilirubin grade was 18.5% and 12.3%, respectively, after the first TACE, 15.6% and 5.1%, respectively, after the second TACE, and 14.5% and 11.1%, respectively, after the third TACE.

CONCLUSION

Superselective TACE can achieve high tumor response rates with prolonged overall survival for patients with intermediate-stage HCC with preserved liver function. Age, serum α-fetoprotein level, tumor number ≥ 11, and heterogeneous enhancement on dynamic computed tomography indicated significantly poor prognosis.

Noninvasive prediction of HCC with progenitor phenotype based on gadoxetic acid-enhanced MRI

OBJECTIVES

To explore the noninvasive prediction of hepatocellular carcinoma (HCC) with progenitor phenotype based on gadoxetic acid-enhanced magnetic resonance imaging (MRI).

METHODS

This retrospective study included 115 surgery-proven HCCs with preoperative gadoxetic acid-enhanced MRI from August 2015 to September 2018. Image features were reviewed. Quantitative image analysis was performed using histogram analysis. HCC with progenitor phenotype was defined as positive for either cytokeratin 19 (CK19) or epithelial cell adhesion molecule (EpCAM) expression. Statistically significant variables for identifying HCCs with progenitor phenotype were determined at multivariate analyses. ROC analyses were used to determined cutoff values and the diagnostic performance of significant variables and combinations. Prediction nomogram was constructed based on multivariate analysis.

RESULTS

At multivariate regression analyses, AFP ≥ 155.25 ng/mL (p < 0.001), skewness on T2WI ≤ 1.10 (p = 0.024), uniformity on pre-T1WI ≤ 0.91 (p = 0.024), irregular tumor margin (p = 0.006), targetoid appearance (p = 0.001), and the absence of mosaic architecture (p = 0.014) were significant predictors of HCCs expressing progenitor cell markers. Combing any three of those significant variables, it provides a diagnostic accuracy of 0.86 (95% CI 0.78-0.92) with sensitivity of 0.97 (95% CI 0.86-1.00), and specificity of 0.74 (95% CI 0.63-0.83). The C-index of the regression coefficient-based nomogram was 0.94 (95% CI 0.91-0.98).

CONCLUSIONS

Noninvasive prediction of HCCs with progenitor phenotype can be achieved with high accuracy by integrated interpretation of biochemical and radiological information, representing a handy tool for precise patient management and the prediction of prognosis.

KEY POINTS

• Qualitative image features of irregular tumor margin, targetoid appearance, and the absence of mosaic architecture are significant predictors of hepatocellular carcinoma with progenitor phenotype. • Quantitative analyses using whole-lesion histogram analysis provides additional information for the prediction of hepatocellular carcinoma with progenitor phenotype. • Noninvasive prediction of hepatocellular carcinoma with progenitor phenotype can be achieved with high accuracy by integrated interpretation of clinical information and qualitative and quantitative imaging analyses.

Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer

BACKGROUND

Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue.

RECENT FINDINGS

Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers.

CONCLUSION

MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.

Individualized Ablation of Hepatocellular Carcinoma: Tailored Approaches across the Phenotype Spectrum

Ablation is now recommended by international guidelines for the definitive treatment of hepatocellular carcinoma (HCC). Extensive clinical studies have demonstrated outcomes comparable to surgical resection with shorter hospital stays, decreased costs, and improved quality of life. Successful ablation requires complete treatment of both tumor and margin while preserving critical adjacent structures. HCC exhibits highly variable presentations in both anatomic involvement and biology which have significant implications on choice of ablative therapy. There are now abundant ablation modalities and adjunctive techniques which can be used to individualize ablation and maximize curative results. This article provides a patient-centered summary of approaches to HCC ablation in the context of patient performance, hepatic reserve, tumor phenotype and biology, intra- and extrahepatic anatomy, and ablation technology.

Progress in research of hepatocellular carcinoma with tumor thrombus

Hepatocellular carcinoma (HCC) with tumor thrombus is a hot and difficult issue in the study of HCC, and many key issues concerning this condition are still controversial. Clinical guidelines and treatment recommendations vary widely between the East and the West, and efficacy remains unsatisfactory. In recent years, with the progress of comprehensive tumor treatment concept and the rapid development of surgical techniques, perioperative management, interventional therapy, radiotherapy, targeted therapy, and other treatment methods, the overall survival rate of HCC with tumor thrombus has been significantly extended and encouraging efficacy has been achieved. However, the core issues on how to select individualized treatment to achieve optimal treatment and how to prevent postoperative recurrence still need to be studied and discussed. This article reviews the progress in the research of hepatic carcinoma with portal vein thrombus, inferior vena cava thrombus, or bile duct thrombus.

Hepatocellular carcinoma and macrovascular tumor thrombosis: treatment outcomes and prognostic factors for survival

PURPOSE

To determine the treatment outcome and prognostic factors for survival in patients with hepatocellular carcinoma (HCC) and macrovascular tumor thrombosis (MTT).

METHODS

Between January 2010 and December 2018, 66 patients diagnosed with HCC and MTT, who received specific treatment were included. Various clinical and imaging data, treatment methods, outcomes, prognostic factors, and overall survival were evaluated. Outcomes were compared with those of 24 patients treated with supportive care.

RESULTS

Most patients with HCC and MTT showed disease progression (80.3%) and a low 5-year survival rate. The median survival time after treatment was 13 months (vs. supportive care group 3 months, p < 0.001). Main branch MTT (p = 0.036), extent of tumor thrombus > 1 segment (p = 0.039), presence of ascites (p = 0.009) and among treatment methods, systemic therapy alone (p = 0.007), and supportive care (p < 0.001) compared with combined local with systemic therapies were prognostic factors for poor survival.

CONCLUSIONS

Although most patients with HCC and MTT showed disease progression, median survival time was significantly longer than that with supportive care. Main branch and > 1 segment involvement of MTT and presence of ascites were significant prognostic factors for poor survival. Combined local and systemic therapy over systemic therapy alone are recommended for patients with these advanced stage HCCs.