Diagnosis Of Hepatocellular Carcinoma Research Articles

Outcomes of Y90 Radioembolization for Hepatocellular Carcinoma in Patients Previously Treated with Transarterial Embolization.

The aim of this study was to evaluate outcomes of transarterial radioembolization (TARE) for hepatocellular carcinoma (HCC) in patients previously treated with transarterial embolization (TAE). In this retrospective study, all HCC patients who received TARE from 1/2012 to 12/2022 for treatment of residual or recurrent disease after TAE were identified. Overall survival (OS) was estimated using the Kaplan-Meier method. Univariate Cox regression was performed to determine significant predictors of OS after TARE. Twenty-one patients (median age 73.4 years, 18 male, 3 female) were included. Median dose to the perfused liver volume was 121 Gy (112-444, range), and 18/21 (85.7%) patients received 112-140 Gy. Median OS from time of HCC diagnosis was 32.9 months (19.4-61.4, 95% CI). Median OS after first TAE was 29.3 months (15.3-58.9, 95% CI). Median OS after first TARE was 10.6 months (6.8-27.0, 95% CI). ECOG performance status of 0 (p = 0.038), index tumor diameter < 4 cm (p = 0.022), and hepatic tumor burden < 25% (p = 0.018) were significant predictors of longer OS after TARE. TARE may provide a survival benefit for appropriately selected patients with HCC who have been previously treated with TAE.

Correlation between CEUS LI-RADS categorization of HCC < 20 mm and clinic-pathological features

ObjectiveTo retrospectively evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) LI-RADS in liver nodules < 20 mm at high risk of hepatocellular carcinoma (HCC) and their correlation with clinic-pathological features.MethodsA total of 432 pathologically proved liver nodules < 20 mm were included from January 2019 to June 2022. Each nodule was categorized as LI-RADS grade (LR)-1 to LR-5 through LR-M according to CEUS LI-RADS. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) of CEUS LI-RADS were evaluated using pathological reference standard. Correlations between clinic-pathological features and CEUS LI-RADS categorization, together with major CEUS features, were further explored.ResultsWith LR-5 to diagnose HCC, the sensitivity, specificity, PPV, NPV, and AUC were 50.3%, 70.0%, 91.2%, 18.5%, and 0.601, respectively. The proportion of LR-5 in primary HCCs was significantly higher than that in recurrent ones (p = 0.014). HCC 10–19 mm showed significantly more frequent arterial phase hyper-enhancement (APHE) and late washout (p < 0.05) and less no-washout (p = 0.003) compared with those in HCC < 10 mm. Well-differentiated HCCs showed more frequent non-APHE and no-washout than moderate- and poor-differentiated HCCs (p < 0.05). Upgrading “APHE without washout” LR-4 nodules 10–19 mm with HCC history and “APHE with late mild washout” LR-4 nodules < 10 mm to LR-5 could improve the diagnostic performance of LR-5. The corresponding sensitivity, specificity, PPV, NPV, and AUC are 60.2%, 70.0%, 92.6%, 22.1%, and 0.651, respectively.ConclusionsCEUS LI-RADS is valuable in the diagnosis of HCC < 20 mm and performance can be improved with the combination of clinic-pathological features.Critical relevance statementCEUS LI-RADS was valuable in the diagnosis of HCC < 20 mm and its diagnostic performance can be improved by combining clinic-pathological features. Further research is needed to define its value in this set of lesions.Key PointsContrast-enhanced ultrasound can detect small liver lesions where LI-RADS accuracy is uncertain.Many LI-RADS Grade-4 nodules were upgraded to Grade-5 by combining imaging with clinic-pathological factors.The reclassification of LI-RADS Grade-5 can improve sensitivity without decreasing positive predictive value.Graphical

Diagnostic and prognostic performances of GALAD score in staging and 1-year mortality of hepatocellular carcinoma: A prospective study

BACKGROUND The GALAD score has improved early hepatocellular carcinoma (HCC) detection rate. The role of the GALAD score in staging and predicting tumor characteristics or clinical outcome of HCC remains of particular interest. AIM To determine the diagnostic/prognostic performances of the GALAD score at various phases of initial diagnosis, tumor features, and 1-year mortality of HCC and compare the performance of the GALAD score with those of other serum biomarkers. METHODS This prospective, diagnostic/prognostic study was conducted among patients with newly diagnosed HCC at the liver center of Vajira Hospital. Eligible patients had HCC staging allocation using the Barcelona Clinic Liver Cancer (BCLC) categorization. Demographics, HCC etiology, and HCC features were recorded. Biomarkers and the GALAD score were obtained at baseline. The performance of the GALAD score and biomarkers were prospectively assessed. RESULTS Exactly 115 individuals were diagnosed with HCC. The GALAD score increased with disease severity. Between BCLC-0/A and BCLC-B/C/D, the GALAD score predicted HCC staging with an area under the curve (AUC) of 0.868 (95%CI: 0.80–0.93). For identifying the curative HCC, the AUC of GALAD score was significantly higher than that of Alpha-fetoprotein (AFP) (0.753) and Lens culinaris agglutinin-reactive fraction of AFP-L3 (0.706), and as good as that of Protein induced by vitamin K absence-II (PIVKA-II) (0.897). For detecting aggressive features, the GALAD score gave an AUC of 0.839 (95%CI: 0.75–0.92) and significantly outperformed compared to that of AFP (0.761) and AFP-L3 (0.697), with a trend of superiority to that of PIVKA-II (0.772). The performance to predict 1-year mortality of GALAD score (AUC: 0.711, 95%CI: 0.60–0.82) was better than that of AFP (0.541) and as good as that of PIVKA-II (0.736). The optimal cutoff value of GALAD score was ≥ 6.83, with a specificity of 72.63% for exhibiting substantial reduction in the 1-year mortality. CONCLUSION The GALAD model can diagnose HCC at the curative stage, including the characteristic of advanced disease, more than that by AFP and AFP-L3, but not PIVKA-II. The GALAD score can be used to predict the 1-year mortality of HCC.

Progression of chronic liver disease to hepatocellular carcinoma: implications for surveillance and management

BackgroundCurrent opinion holds that hepatocellular carcinoma (HCC) arises as a stepwise progression from chronic liver disease (CLD) to cirrhosis and then to HCC. However, some HCCs may develop in a non-cirrhotic liver, raising uncertainty about their origin.MethodsWe analysed a prospectively accrued cohort of 2592 CLD patients (median follow-up = 13 years) with no prior evidence of liver cirrhosis. To track the progression of liver fibrosis prior to HCC diagnosis, we examined serial measurements of Fib-4 (an index of liver fibrosis). We also evaluated fibrosis progression in response to antiviral treatment in patients with hepatitis C (HCV) and hepatitis B (HBV). Recognising the limitations of serologic fibrosis assessment, we correlated Fib-4 and fibrosis histology within this cohort.ResultsAmong HCC patients, 28% had no indication of cirrhosis prior to HCC diagnosis. Only 31% of HBV-related HCC cases followed the cirrhotic pathway. HCV patients who achieved sustained virological response (SVR) developed cirrhosis approximately 7 years before HCC diagnosis.ConclusionsOur analysis challenges the notion of cirrhosis as an obligatory stage of HCC development in CLD patients. We affirm HBV’s direct oncogenic potential and find that achieving SVR does not universally prevent HCC development. Our findings have major implications for HCC surveillance.

Early detection and prognosis evaluation for hepatocellular carcinoma by circulating tumour DNA methylation: A multicentre cohort study.

Early diagnosis of hepatocellular carcinoma (HCC) can significantly improve patient survival. We aimed to develop a blood-based assay to aid in the diagnosis, detection and prognostic evaluation of HCC. A three-phase multicentre study was conducted to screen, optimise and validate HCC-specific differentially methylated regions (DMRs) using next-generation sequencing and quantitative methylation-specific PCR (qMSP). Genome-wide methylation profiling was conducted to identify DMRs distinguishing HCC tumours from peritumoural tissues and healthy plasmas. The twenty most effective DMRs were verified and incorporated into a multilocus qMSP assay (HepaAiQ). The HepaAiQ model was trained to separate 293 HCC patients (Barcelona Clinic Liver Cancer (BCLC) stage 0/A, 224) from 266 controls including chronic hepatitis B (CHB) or liver cirrhosis (LC) (CHB/LC, 96), benign hepatic lesions (BHL, 23), and healthy controls (HC, 147). The model achieved an area under the curve (AUC) of 0.944 with a sensitivity of 86.0% in HCC and a specificity of 92.1% in controls. Blind validation of the HepaAiQ model in a cohort of 523 participants resulted in an AUC of 0.940 with a sensitivity of 84.4% in 205 HCC cases (BCLC stage 0/A, 167) and a specificity of 90.3% in 318 controls (CHB/LC, 100; BHL, 102; HC, 116). When evaluated in an independent test set, the HepaAiQ model exhibited a sensitivity of 70.8% in 65 HCC patients at BCLC stage 0/A and a specificity of 89.5% in 124 patients with CHB/LC. Moreover, HepaAiQ model was assessed in paired pre- and postoperative plasma samples from 103 HCC patients and correlated with 2-year patient outcomes. Patients with high postoperative HepaAiQ score showed a higher recurrence risk (Hazard ratio, 3.33, p<.001). HepaAiQ, a noninvasive qMSP assay, was developed to accurately measure HCC-specific DMRs and shows great potential for the diagnosis, detection and prognosis of HCC, benefiting at-risk populations.

Congestive Hepatopathy: Pathophysiology, Workup, and Imaging Findings with Pathologic Correlation.

Liver congestion is increasingly encountered in clinical practice and presents diagnostic pitfalls of which radiologists must be aware. The complex altered hemodynamics associated with liver congestion leads to diffuse parenchymal changes and the development of benign and malignant nodules. Distinguishing commonly encountered benign hypervascular lesions, such as focal nodular hyperplasia (FNH)-like nodules, from hepatocellular carcinoma (HCC) can be challenging due to overlapping imaging features. FNH-like lesions enhance during the hepatic arterial phase and remain isoenhancing relative to the background liver parenchyma but infrequently appear to wash out at delayed phase imaging, similar to what might be seen with HCC. Heterogeneity, presence of an enhancing capsule, washout during the portal venous phase, intermediate signal intensity at T2-weighted imaging, restricted diffusion, and lack of uptake at hepatobiliary phase imaging point toward the diagnosis of HCC, although these features are not sensitive individually. It is important to emphasize that the Liver Imaging Reporting and Data System (LI-RADS) algorithm cannot be applied in congested livers since major LI-RADS features lack specificity in distinguishing HCC from benign hypervascular lesions in this population. Also, the morphologic changes and increased liver stiffness caused by congestion make the imaging diagnosis of cirrhosis difficult. The authors discuss the complex liver macro- and microhemodynamics underlying liver congestion; propose a more inclusive approach to and conceptualization of liver congestion; describe the pathophysiology of liver congestion, hepatocellular injury, and the development of benign and malignant nodules; review the imaging findings and mimics of liver congestion and hypervascular lesions; and present a diagnostic algorithm for approaching hypervascular liver lesions. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Liver Imaging Reporting and Data System Contrast-Enhanced US Nonradiation Treatment Response Assessment Version 2024.

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) standardizes the imaging technique, reporting lexicon, disease categorization, and management for patients with or at risk for hepatocellular carcinoma (HCC). LI-RADS encompasses HCC surveillance with US; HCC diagnosis with CT, MRI, or contrast-enhanced US (CEUS); and treatment response assessment (TRA) with CT or MRI. LI-RADS was recently expanded to include CEUS TRA after nonradiation locoregional therapy or surgical resection. This report provides an overview of LI-RADS CEUS Nonradiation TRA v2024, including a lexicon of imaging findings, techniques, and imaging criteria for posttreatment tumor viability assessment. LI-RADS CEUS Nonradiation TRA v2024 takes into consideration differences in the CEUS appearance of viable tumor and posttreatment changes within and in close proximity to a treated lesion. Due to the high sensitivity of CEUS to vascular flow, posttreatment reactive changes commonly manifest as areas of abnormal perilesional enhancement without washout, especially in the first 3 months after treatment. To improve the accuracy of CEUS for nonradiation TRA, different diagnostic criteria are used to evaluate tumor viability within and outside of the treated lesion margin. Broader criteria for intralesional enhancement increase sensitivity for tumor viability detection. Stricter criteria for perilesional enhancement limit miscategorization of posttreatment reactive changes as viable tumor. Finally, the TRA algorithm reconciles intralesional and perilesional tumor viability assessment and assigns a single LI-RADS treatment response (LR-TR) category: LR-TR nonviable, LR-TR equivocal, or LR-TR viable.

Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing to Identify an Immunogenic Cell Death-Related 5-Gene Prognostic Signature in Hepatocellular Carcinoma.

Immunogenic cell death (ICD) can enhance the potency of immunotherapy in cancer treatment. Nevertheless, it is ambiguous how ICD-related genes (ICDRGs) contribute to hepatocellular carcinoma (HCC). Single-cell RNA sequencing (scRNA-seq) data were used to distinguish malignant cells from normal cells in the HCC tumor microenvironment(TME). Bulk RNA sequencing data was employed to acquire the landscape of the 33 ICDRGs. Unsupervised clustering identified two ICD molecular subtypes. The cellular infiltration characteristics and biological behavior in different subtypes were analyzed by ssGSEA. Subsequently, differentially expressed genes (DEGs) between the two subtypes were determined, based on which patients were classified into three gene clusters. Then, the prognostic model was constructed by Lasso-Cox analysis. Finally, we investigated the expression of risk genes in cancer cell line encyclopedia (CCLE) and validated the function of NKX3-2 in vitro experiments. ICD scores and ICDRGs expression in malignant cells were significantly lower than in normal cells by scRNA-seq analysis. ICD-high subtype was characterized by ICD-related gene overexpression and high levels of immune infiltration abundance and immune checkpoints; Three DEGs-related gene clusters were likewise strongly linked to stromal and immunological activation. In the ICD-related prognostic model consisting of NKX3-2, CHODL, MMP1, NR0B1, and CTSV, the low-risk group patients had a better endpoint and displayed increased susceptibility to immunotherapy and chemotherapeutic drugs like 5-Fluorouracil, afatinib, bortezomib, cediratinib, lapatinib, dasatinib, gefitinib and crizotinib. Moreover, NKX3-2 amplification in HCC samples has been verified by experiments, and its disruption suppressed the proliferation and invasion of tumor cells. Our study highlighted the potential of the ICDRGs risk score as a prognostic indicator to aid in the accurate diagnosis and immunotherapy sensitivity of HCC.

The Value of Chemokine and Chemokine Receptors in Diagnosis, Prognosis, and Immunotherapy of Hepatocellular Carcinoma.

Chemokines and chemokine receptors (CCRs) are involved in a variety of anti-tumour and pro-tumour immune processes in vivo, such as angiogenesis, metastasis, proliferation and invasiveness, and influence patient prognosis and response to therapy. CCRs differentially expressed in HCC and associated with prognosis were extracted from TCGA and GEO databases, and the obtained CCRs were then used to construct signature genes, and the signature gene were selected for expression validation as well as functional experiments to explore the role of CCRs in the treatment and prognosis of HCC. We constructed a prognostic model including five CCRs (CCL20, CCL23, CCR3, CCR10, and CXCR3) and validated the expression of signature genes. The model's risk score is an independent prognostic factor for HCC. We have also developed prognostic model nomograms for clinical use. In addition, we validated that CCR3 expression is associated with poor prognosis in HCC, and the proliferation and migration ability of HCC cells was significantly inhibited after interfering with the expression of CCR3 in MHCC-LM3. We also looked at differences in pathway enrichment, immune infiltration and immune checkpoints. Finally, we found that risk scores were also correlated with drug sensitivity, the high-risk group had a better sensitivity to sorafenib. The CCRs-related gene signature may better assess HCC prognosis and response to immunotherapy and tyrosine kinase inhibitors such as sorafenib in HCC, providing prospective solutions for diagnosis and treatment.

Combined Biomarkers for Early Diagnosis of Hepatocellular Carcinoma

Combined Biomarkers for Early Diagnosis of Hepatocellular Carcinoma

Early-stage biomarkers identification by integrated genomic analysis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC), a type of liver cancer, ranks as the third-leading cause of death due to the lack of definite biomarkers for early-stage detection. HCC progression occurs by the dysregulation of several genes. Though several studies focus on biomarkers for HCC diagnosis, stage-specific marker identification remains elusive. Hence, the present study aims to identify early-stage biomarkers for the detection of HCC through integrated in silico analysis. The differential gene expression was performed using GEO2R for the datasets (GSE14520, GSE63898, GSE121248, GSE124535, GSE94660, and GSE6764) retrieved from Gene Expression Omnibus (GEO) of patients with cirrhotic liver or HCC. The common differentially expressed gene enrichment analysis was performed using Funrich for Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomics (KEGG) gene mapping. The Protein-Protein Interaction (PPI) network was performed using the Search Tool for the Retrieval of Interacting Genes (STRING). The hub genes were identified using the CytoHubba plug-in of Cytoscape software. The identified genes were verified for their prognostic value using the Kaplan-Meier plotter and Immunohistochemistry micrographs obtained from the Human Protein Atlas database. An overall of 243 common differentially expressed genes (DEGs) were identified containing 171 upregulated and 72 downregulated genes. With the help of PPI network construction, ten hub genes were identified as CDK1, AURKA, CCNB1, CCNB2, CENPF, CDC20, TOP2A, BUB1, RRM2, and HMMR, which are dysregulated owing to HCC proliferation, tumorigenesis and poor prognosis in patients. These hub genes are suitable waypoints for the diagnosis and targeted therapy against early-stage HCC.

Ultrasonography of Hepatocellular Carcinoma: From Diagnosis to Prognosis.

Hepatocellular carcinoma (HCC) is a prominent contributor to cancer-related mortality worldwide. Early detection and diagnosis of liver cancer can significantly improve its prognosis and patient survival. Ultrasound technology, serving has undergone substantial advances as the primary method of HCC surveillance and has broadened its scope in recent years for effective management of HCC. This article is a comprehensive overview of ultrasound technology in the treatment of HCC, encompassing early detection, diagnosis, staging, treatment evaluation, and prognostic assessment. In addition, the authors summarized the application of contrast-enhanced ultrasound in the diagnosis of HCC and assessment of prognosis. Finally, the authors discussed further directions in this field by emphasizing overcoming existing obstacles and integrating cutting-edge technologies.

Multifunctional and stimuli-responsive liposomes in hepatocellular carcinoma diagnosis and therapy

Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer, mainly occurring in Asian countries with an increased incidence rate globally. Currently, several kinds of therapies have been deployed for HCC therapy including surgical resection, chemotherapy, radiotherapy and immunotherapy. However, this tumor is still incurable, requiring novel strategies for its treatment. The nanomedicine has provided the new insights regarding the treatment of cancer that liposomes as lipid-based nanoparticles, have been widely applied in cancer therapy due to their biocompaitiblity, high drug loading and ease of synthesis and modification. The current review evaluates the application of liposomes for the HCC therapy. The drugs and genes lack targeting ability into tumor tissues and cells. Therefore, loading drugs or genes on liposomes can increase their accumulation in tumor site for HCC suppression. Moreover, the stimuli-responsive liposomes including pH-, redox- and light-sensitive liposomes are able to deliver drug into tumor microenvironment to improve therapeutic index. Since a number of receptors upregulate on HCC cells, the functionalization of liposomes with lactoferrin and peptides can promote the targeting ability towards HCC cells. Moreover, phototherapy can be induced by liposomes through loading phtoosensitizers to stimulate photothermal- and photodynamic-driven ablation of HCC cells. Overall, the findings are in line with the fact that liposomes are promising nanocarriers for the treatment of HCC.

Artificial Intelligence-Driven Platform: Unveiling Critical Hepatic Molecular Alterations in Hepatocellular Carcinoma Development.

Since most Hepatocellular Carcinoma (HCC) typically arises as a consequence of long-term liver damage, the hepatic molecular characteristics are closely related to the occurrence of HCC. Gaining comprehensive information about the location, morphology, and hepatic molecular alterations related to HCC is essential for accurate diagnosis. However, there is a dearth of technological advancements capable of concurrently providing precise HCC diagnosis and discerning the accompanying hepatic molecular alterations. In this study, an integrated information system is developed for the pathological-level diagnosis of HCC and the revelation of critical molecular alterations in the liver. This system utilizes computed tomography/Surface-enhanced Raman scattering combined with an artificial intelligence strategy to establish connections between the occurrence of HCC and alterations in hepatic biomolecules. Employing artificial intelligence techniques, the SERS spectra from both healthy and HCC groups are successfully classified into two distinct categories with a remarkable accuracy rate of 91.38%. Based on molecular profiling, it is identified that the nucleotide-to-lipid signal ratio holds significant potential as a reliable indicator for the occurrence of HCC, thereby serving as a promising tool for prevention and therapeutic surveillance.

Serum Total Superoxide Dismutase Activity as a Predictive Factor in Patients with Hepatocellular Carcinoma

Objectives: This study aimed to determine the cutoff value for diagnosis and predict mortality in hepatocellular carcinoma (HCC) based on serum total superoxide dismutase (SOD) activity. Methods: A retrospective case-control study of the SOD Model was conducted using data from a single-center dataset at Shandong Provincial Hospital Affiliated with Shandong First Medical University, China. Serum total SOD activity was analyzed in HCC patients (n = 124) and control subjects (n = 117). Receiver operating characteristic (ROC) curves were used to determine cutoff values of serum total SOD activity for HCC diagnosis. Overall survival (OS) was assessed using the Kaplan-Meier method. Results: In the model groups, the cutoff level of total SOD activity for HCC was 169.2 U/mL (sensitivity: 87.23%, specificity: 91.95%), while for HCC [alpha fetoprotein (AFP) &lt; 20 ng/mL], it was 173.4 U/mL (sensitivity: 86.79%, specificity: 88.51%). Additionally, in the validation groups, the true positive rate, true negative rate, and accuracy rate were all above 90%. Based on the cutoff value of SOD, HCC patients were assigned to an H-SOD and L-SOD group depending on their serum total SOD activity at admission before operation. The 5-year OS rate of the H-SOD group was 75.00%, and that of the L-SOD group was 36.59% in HCC patients (P = 0.0245). With a decrease in SOD activity, serum levels of Zn (t = 3.890, P = 0.0003) and Se (t = 7.694, P &lt; 0.0001) were also significantly decreased and positively correlated with SOD activity (both P &lt; 0.05) in HCC patients. Conclusions: Low serum total SOD activity may also be a risk factor for HCC. The decrease of SOD activity in HCC patients was partly related to a lack of Zn and Se.

Realizing the Promise of Artificial Intelligence in Hepatocellular Carcinoma through Opportunities and Recommendations for Responsible Translation

This study aims to provide an overview of the current state-of-the-art applications of artificial intelligence (AI) and machine learning in the management of hepatocellular carcinoma (HCC), and to explore future directions for continued progress in this emerging field. This study is a comprehensive literature review that synthesizes recent findings and advancements in the application of AI and machine learning techniques across various aspects of HCC care, including screening and early detection, diagnosis and staging, prognostic modeling, treatment planning, interventional guidance, and monitoring of treatment response. The review draws upon a wide range of published research studies, focusing on the integration of AI and machine learning with diverse data sources, such as medical imaging, clinical data, genomics, and other multimodal information. The results demonstrate that AI-based systems have shown promise in improving the accuracy and efficiency of HCC screening, diagnosis, and tumor characterization compared to traditional methods. Machine learning models integrating clinical, imaging, and genomic data have outperformed conventional staging systems in predicting survival and recurrence risk. AI-based recommendation systems have the potential to optimize personalized therapy selection, while augmented reality techniques can guide interventional procedures in real-time. Moreover, longitudinal application of AI may enhance the assessment of treatment response and recurrence monitoring. Despite these promising findings, the review highlights the need for rigorous multicenter prospective validation studies, standardized multimodal datasets, and thoughtful consideration of ethical implications before widespread clinical implementation of AI technologies in HCC management.

Radiomic Analysis of Contrast-Enhanced CT Predicts Glypican 3-Positive Hepatocellular Carcinoma.

The Glypican 3 (GPC3)-positive expression in Hepatocellular Carcinoma (HCC) is associated with a worse prognosis. Moreover, GPC3 has emerged as an immunotherapeutic target in advanced unresectable HCC systemic therapy. It is significant to diagnose GPC3-positive HCCs before therapy. Regarding imaging diagnosis of HCC, dynamic contrast-enhanced CT is more common than MRI in many regions. The aim of this study was to construct and validate a radiomics model based on contrast-enhanced CT to predict the GPC3 expression in hepatocellular carcinoma. This retrospective study included 141 (training cohort: n = 100; validation cohort: n = 41) pathologically confirmed HCC patients. Radiomics features were extracted from the Artery Phase (AP) images of contrast-enhanced CT. Logistic regression with the Least Absolute Shrinkage and Selection Operator (LASSO) regularization was used to select features to construct radiomics score (Rad-score). A final combined model, including the Rad-score of the selected features and clinical risk factors, was established. Receiver Operating Characteristic (ROC) curve analysis, Delong test, and Decision Curve Analysis (DCA) were used to assess the predictive performance of the clinical and radiomics models. 5 features were selected to construct the AP radiomics model of contrast-enhanced CT. The radiomics model of AP from contrast-enhanced CT was superior to the clinical model of AFP in training cohorts (P < 0.001), but not superior to the clinical model in validation cohorts (P = 0.151). The combined model (AUC = 0.867 vs. 0.895), including AP Rad-score and serum Alpha-Fetoprotein (AFP) levels, improved the predictive performance more than the AFP model (AUC = 0.651 vs. 0.718) in the training and validation cohorts. The combined model, with a higher decision curve indicating more net benefit, exhibited a better predictive performance than the AP radiomics model. DCA revealed that at a range threshold probability approximately above 60%, the combined model added more net benefit compared to the AP radiomics model of contrastenhanced CT. A combined model including AP Rad-score and serum AFP levels based on contrast-enhanced CT could preoperatively predict GPC3-positive expression in HCC.

Dual-tracer PET/CT in the management of hepatocellular carcinoma

Background & AimsCombined 18F-fluorodeoxyglucose (FDG) and 11C-acetate (dual-tracer) positron emission tomography-computed tomography (PET-CT) is being increasingly performed for the management of hepatocellular carcinoma (HCC), although its role is not well defined. Therefore, we evaluated its effectiveness in (i) staging, (ii) characterization of indeterminate lesions on conventional imaging, and (iii) detection of HCC in patients with unexplained elevations in serum alpha-fetoprotein (AFP) levels. MethodsWe retrospectively assessed 525 consecutive patients from three tertiary centers between 2014 and 2020. For staging, we recorded new lesion detection rates, changes in the Barcelona Clinic Liver Cancer (BCLC) classification, and treatment allocation due to dual-tracer PET-CT. To characterize indeterminate lesions and unexplained elevation of serum AFP levels, the sensitivity and specificity of dual-tracer PET-CT in diagnosing HCC were evaluated. A Multidisciplinary external review and a cost-benefit analysis of patients for metastatic screening were also performed. ResultsDual-tracer PET/CT identified new lesions in 14.3% of 273 staging patients, resulting in BCLC upstaging in 11.7% and treatment modifications in 7.7%. It upstaged 8.1% of 260 patients undergoing metastatic screening, with an estimated savings of US$495 per patient. It had a sensitivity and specificity of 80.7% (95% CI 71.2-88.6%) and 94.8% (95% CI 90.4-98.6%), respectively, for diagnosing HCC in 201 indeterminate lesions. It detected HCC in 45.1% of 51 patients with unexplained elevations in serum AFP concentrations. External review revealed substantial agreement between local and external image interpretation and patient assessment (n=273, κ=0.822; 95% CI 0.803-0.864). ConclusionsDual-tracer PET/CT provides additional value to conventional imaging in HCC patients by improving staging, confirming HCC diagnosis with high accuracy in patients with indeterminate lesions, and detecting HCC in patients with unexplained elevation of serum AFP.• Impact and implications• Dual-tracer PET-CT detected 12% more disease in HCC patients undergoing staging than CT or MRI, resulting in change in treatment in 8% of cases and a cost saving of US$495 per patient.• It is also able to accurately detect HCC in high-risk cases where CT or MRI are equivocal or normal.• Dual-tracer PET/CT provides additional value to conventional imaging in HCC patients by improving staging, confirming HCC diagnosis with high accuracy in patients with indeterminate lesions, and detecting HCC in patients with unexplained elevation of serum AFP.

Cascading Detection of Hydrogen Sulfide and N-Acetyltransferase 2 in Hepatocellular Carcinoma Cells Using a Two-Photon Fluorescent Probe.

Hydrogen sulfide (H2S), a critical gas signaling molecule, and N-acetyltransferase 2 (NAT2), a key enzyme in drug metabolism, are both known active biomarkers for liver function. However, the interactions and effects of H2S and NAT2 in living cells or lesion sites remain unknown due to the lack of imaging tools to achieve simultaneous detection of these two substances, making it challenging to implement real-time imaging and precise tracking. Herein, we report an activity-based two-photon fluorescent probe, TPSP-1, for the cascade detection of H2S and NAT2 in living liver cells. Continuous conversion from TPSP-1 to TPSP-3 was achieved in liver cells and tissues. Significantly, leveraging the outstanding optical properties of this two-photon fluorescent probe, TPSP-1, has been effectively used to identify pathological tissue samples directly from clinical liver cancer patients. This work provides us with this novel sensing and two-photon imaging probe, which can be used as a powerful tool to study the physiological functions of H2S and NAT2 and will help facilitate rapid and accurate diagnosis and therapeutic evaluation of hepatocellular carcinoma.

Comprehensive analysis of INTS family related to expression, prognosis, diagnosis and immune features in hepatocellular carcinoma

PurposeThe integrator subunit (INTS) family, a group exclusive to metazoans, participates in various biologic processes. However, their roles in hepatocellular carcinoma (HCC) remain largely unexplored. MethodsPublic databases were utilized to investigate the transcriptional and protein expression, and clinical relevance of the INTS family in HCC. Meanwhile, the effects of INTS13 knockdown and overexpression on cell proliferation and apoptosis were studied using HCC cell lines. ResultsThe mRNA expression of most INTSs were higher in tumor than normal tissues. Higher expression of INTS1/2/3/4/7/8/9/11/12/13 were correlated with poorer overall survival (OS) in Kaplan-Meier Survival Analysis. Multivariate analysis revealed higher level of INTS13 was an independent prognostic factor for shorter OS. Furthermore, genetic alteration of INTS3/6/7/8/9/10 were found in HCC patients and was associated with shorter disease-free survival and progression-free survival. INTS1/2/3/5/7/11/13/14 were associated with activation of tumor-induced immune response and immune infiltration in HCC. Knockdown of INTS13 inhibited cell proliferation and induced apoptosis in HCC cell lines, while overexpression of INTS13 had the opposite effect. ConclusionOur results indicate that INTS13 is an independent prognostic biomarker in HCC. Furthermore, INTS13 enhances cell proliferation and decreases cell apoptosis in HCC cell lines leading to a poorer OS in HCC patients.