Microvascular Invasion Research Articles

A Systematic Review and Meta-Analysis of MRI Radiomics for Predicting Microvascular Invasion in Patients with Hepatocellular Carcinoma.

The prediction power of MRI radiomics for microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains uncertain. To investigate the prediction performance of MRI radiomics for MVI in HCC. Original studies focusing on preoperative prediction performance of MRI radiomics for MVI in HCC, were systematically searched from databases of PubMed, Embase, Web of Science and Cochrane Library. Radiomics quality score (RQS) and risk of bias of involved studies were evaluated. Meta-analysis was carried out to demonstrate the value of MRI radiomics for MVI prediction in HCC. Influencing factors of the prediction performance of MRI radiomics were identified by subgroup analyses. 13 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement were eligible for this systematic review and meta-analysis. The studies achieved an average RQS of 14 (ranging from 11 to 17), accounting for 38.9% of the total points. MRI radiomics achieved a pooled sensitivity of 0.82 (95%CI: 0.78 - 0.86), specificity of 0.79 (95%CI: 0.76 - 0.83) and area under the summary receiver operator characteristic curve (AUC) of 0.88 (95%CI: 0.84 - 0.91) to predict MVI in HCC. Radiomics models combined with clinical features achieved superior performances compared to models without the combination (AUC: 0.90 vs 0.85, P < 0.05). MRI radiomics has the potential for preoperative prediction of MVI in HCC. Further studies with high methodological quality should be designed to improve the reliability and reproducibility of the radiomics models for clinical application. The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42022333822).

Three-Dimensional Multifrequency MR Elastography for Microvascular Invasion and Prognosis Assessment in Hepatocellular Carcinoma.

Pretreatment identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is important when selecting treatment strategies. To improve models for predicting MVI and recurrence-free survival (RFS) by developing nomograms containing three-dimensional (3D) MR elastography (MRE). Prospective. 188 patients with HCC, divided into a training cohort (n = 150) and a validation cohort (n = 38). In the training cohort, 106/150 patients completed a 2-year follow-up. 1.5T 3D multifrequency MRE with a single-shot spin-echo echo planar imaging sequence, and 3.0T multiparametric MRI (mp-MRI), consisting of diffusion-weighted echo planar imaging, T2-weighted fast spin echo, in-phase out-of-phase T1-weighted fast spoiled gradient-recalled dual-echo and dynamic contrast-enhanced gradient echo sequences. Multivariable analysis was used to identify the independent predictors for MVI and RFS. Nomograms were constructed for visualization. Models for predicting MVI and RFS were built using mp-MRI parameters and a combination of mp-MRI and 3D MRE predictors. Student's t-test, Mann-Whitney U test, chi-squared or Fisher's exact tests, multivariable analysis, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log rank tests. P < 0.05 was considered significant. Tumor c and liver c were independent predictors of MVI and RFS, respectively. Adding tumor c significantly improved the diagnostic performance of mp-MRI (AUC increased from 0.70 to 0.87) for MVI detection. Of the 106 patients in the training cohort who completed the 2-year follow up, 34 experienced recurrence. RFS was shorter for patients with MVI-positive histology than MVI-negative histology (27.1 months vs. >40 months). The MVI predicted by the 3D MRE model yielded similar results (26.9 months vs. >40 months). The MVI and RFS nomograms of the histologic-MVI and model-predicted MVI-positive showed good predictive performance. Biomechanical properties of 3D MRE were biomarkers for MVI and RFS. MVI and RFS nomograms were established. 2 TECHNICAL EFFICACY: Stage 2.

Novel nomograms based on microvascular invasion grade for early-stage hepatocellular carcinoma after curative hepatectomy

Microvascular invasion (MVI) is a critical risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). This study aimed to firstly develop and validate nomograms based on MVI grade for predicting recurrence, especially early recurrence, and overall survival in patients with early-stage HCC after curative resection. We retrospectively reviewed the data of patients with early-stage HCC who underwent curative hepatectomy in the First Affiliated Hospital of Fujian Medical University (FHFU) and Mengchao Hepatobiliary Hospital of Fujian Medical University (MHH). Kaplan–Meier curves and Cox proportional hazards regression models were used to analyse disease-free survival (DFS) and overall survival (OS). Nomogram models were constructed on the datasets from the 70% samples of and FHFU, which were validated using bootstrap resampling with 30% samples as internal validation and data of patients from MHH as external validation. A total of 703 patients with early-stage HCC were included to create a nomogram for predicting recurrence or metastasis (DFS nomogram) and a nomogram for predicting survival (OS nomogram). The concordance indexes and calibration curves in the training and validation cohorts showed optimal agreement between the predicted and observed DFS and OS rates. The predictive accuracy was significantly better than that of the classic HCC staging systems.

Early Onset Intrahepatic Cholangiocarcinoma: Clinical Characteristics, Oncological Outcomes, and Genomic/Transcriptomic Features

IntroductionData on clinical characteristics and disease-specific prognosis among patients with early onset intrahepatic cholangiocarcinoma (ICC) are currently limited.MethodsPatients undergoing hepatectomy for ICC between 2000 and 2020 were identified by using a multi-institutional database. The association of early (≤50 years) versus typical onset (>50 years) ICC with recurrence-free (RFS) and disease-specific survival (DSS) was assessed in the multi-institutional database and validated in an external cohort. The genomic and transcriptomic profiles of early versus late onset ICC were analyzed by using the Total Cancer Genome Atlas (TCGA) and Memorial Sloan Kettering Cancer Center databases.ResultsAmong 971 patients undergoing resection for ICC, 22.7% (n = 220) had early-onset ICC. Patients with early-onset ICC had worse 5-year RFS (24.1% vs. 29.7%, p < 0.05) and DSS (36.5% vs. 48.9%, p = 0.03) compared with patients with typical onset ICC despite having earlier T-stage tumors and lower rates of microvascular invasion. In the validation cohort, patients with early-onset ICC had worse 5-year RFS (7.4% vs. 20.5%, p = 0.002) compared with individuals with typical onset ICC. Using the TCGA cohort, 652 and 266 genes were found to be upregulated (including ATP8A2) and downregulated (including UTY and KDM5D) in early versus typical onset ICC, respectively. Genes frequently implicated as oncogenic drivers, including CDKN2A, IDH1, BRAF, and FGFR2 were infrequently mutated in the early-onset ICC patients.ConclusionsEarly-onset ICC has distinct clinical and genomic/transcriptomic features. Morphologic and clinicopathologic characteristics were unable to fully explain differences in outcomes among early versus typical onset ICC patients. The current study offers a preliminary landscape of the molecular features of early-onset ICC.

Post-transplant hepatitis B virus reactivation impacts the prognosis of patients with hepatitis B-related hepatocellular carcinoma: a dual-centre retrospective cohort study in China.

Highly active hepatitis B virus (HBV) is known to be associated with poor outcomes in patients with hepatocellular carcinoma (HCC). This study aims to investigate the relationship between HBV status and HCC recurrence after liver transplantation. The study retrospectively analyzed HCC patients undergoing liver transplantation in two centres between January 2015 and December 2020. The authors reviewed post-transplant HBV status and its association with outcomes. The prognosis of recipients with hepatitis B surface antigen (HBsAg) reappearance ( n =58) was poorer than those with HBsAg persistent negative ( n =351) and positive ( n =53). In HBsAg persistent positive group, recipients with HBV DNA reappearance or greater than 10-fold increase above baseline had worse outcomes than those without ( P <0.01). HBV reactivation was defined as (a) HBsAg reappearance or (b) HBV DNA reappearance or greater than 10-fold increase above baseline. After propensity score matching, the 5-year overall survival rate and recurrence-free survival rate after liver transplantation in recipients with HBV reactivation were significantly lower than those without (32.0% vs. 62.3%; P <0.01, and 16.4% vs. 63.1%; P <0.01, respectively). Moreover, HBV reactivation was significantly related to post-transplant HCC recurrence, especially lung metastasis. Cox regression analysis revealed that beyond Milan criteria, microvascular invasion and HBsAg-positive graft were independent risk factors for post-transplant HBV reactivation, and a novel nomogram was established accordingly with a good predictive efficacy (area under the time-dependent receiver operating characteristic curve=0.78, C-index =0.73). Recipients with HBV reactivation had worse outcomes and higher tumour recurrence rates than those without. The nomogram could be used to evaluate the risk of post-transplant HBV reactivation effectively.

Prediction of vessels encapsulating tumor clusters pattern and prognosis of hepatocellular carcinoma based on preoperative gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid magnetic resonance imaging

Vessels encapsulating tumor clusters (VETC) is a novel vascular pattern distinct from microvascular invasion that is significantly associated with poor prognosis in patients with hepatocellular carcinoma (HCC). This study aimed to predict the VETC pattern and prognosis of patients with HCC based on preoperative gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) magnetic resonance imaging (MRI). Patients with HCC who underwent surgical resection and preoperative Gd-EOB-DTPA MRI between January 1, 2016 and August 31, 2022 were retrospectively included. The variables associated with VETC were evaluated using logistic regression. A nomogram model was constructed on the basis of independent risk factors. COX regression was used to determine the variables associated with recurrence-free survival (RFS). A total of 98 patients with HCC were retrospectively included. Peritumoral hypointensity on the hepatobiliary phase (HBP) (odd ratio [OR], 2.58; 95% CI, 1.05-6.33; P=.04), tumor-to-liver signal intensity ratio on HBP of ≤0.75 (OR, 27.80; 95% CI, 1.53-502.91; P=.02), and tumor-to-liver apparent diffusion coefficient ratio of ≤1.23 (OR, 4.65; 95% CI, 1.01-21.38; P=.04) were independent predictors of VETC pattern. A nomogram was constructed by combining the aforementioned 3 significant variables. The accuracy, sensitivity, and specificity were 69.79%, 71.74%, and 68.00%, respectively, with an area under the receiver operating characteristic curve of 0.75 (95% CI, 0.65-0.83). The variables significantly associated with RFS of patients with HCC after surgery were Barcelona Clinic Liver Cancer stage (hazard ratio [HR], 2.15; 95% CI, 1.09-4.22; P=.03) and VETC pattern (HR, 2.28; 95% CI, 1.29-4.02; P=.004). The preoperative imaging features based on Gd-EOB-DTPA MRI can be used to predict the VETC pattern, which has prognostic significance for postoperative RFS of patients with HCC.

Microvascular Invasion in Hepatocellular Carcinoma: Some Puzzling Facets.

Hepatocellular carcinoma is the main type of primary liver cancer. Macroscopic vascular invasion is usually identified during imaging, whereas microvascular invasion is usually determined by histopathological evaluation. We aim to identify the association between microvascular invasion and other markers of tumor aggressiveness and to identify the role of microvascular invasion in the prognosis of patients who were treated by liver transplantation for hepatocellular carcinoma. This is a single-center retrospective analysis of prospectively collected data. Patients who received liver transplantation for hepatocellular carcinoma were included in the study. Data were collected regarding sociodemographic variables, criteria of selection for liver transplantation, pretransplant alpha-fetoprotein, presence or absence of microvascular invasion, presence or absence of recurrence, overall survival, and disease-free survival. Data were analyzed using Statistical Package for the Social Sciences. Sociodemographic laboratory values and radiologic tumor characteristics were found to be similar in patients with or without microvascular invasion. Our study revealed that microvascular invasion is associated with increased recurrence, decreased diseasedfree survival, and decreased overall survival, only for patients with hepatocellular carcinoma beyond Milan criteria at the time of liver transplantation. For patients beyond Milan criteria, but not within Milan criteria, microvascular invasion plays a significant role in predicting recurrence and shorter survival after liver transplantation.

The value of varying diffusion curvature MRI for assessing the microvascular invasion of hepatocellular carcinoma.

Varying diffusion curvature (VDC) MRI is an emerging diffusion-weighted imaging (DWI) technique that can capture non-Gaussian diffusion behavior and reflect tissue heterogeneity. However, its clinical utility has hardly been evaluated. We aimed to investigate the value of the VDC technique in noninvasively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC). 74 patients with HCCs, including 39 MVI-positive and 35 MVI-negative HCCs were included into this prospective study. Quantitative metrics between subgroups, clinical risk factors, as well as diagnostic performance were evaluated. The power analysis was also carried out to determine the statistical power. MVI-positive HCCs exhibited significantly higher VDC-derived structural heterogeneity measure, D1 (0.680 ± 0.100 × 10-3 vs 0.572 ± 0.148 × 10-3mm2/s, p = 0.001) and lower apparent diffusion coefficient (ADC) (1.350 ± 0.166 × 10-3 vs 1.471 ± 0.322 × 10-3mm2/s, p = 0.0495) compared to MVI-negative HCCs. No statistical significance was observed for VDC-derived diffusion coefficient, D0 between the subgroups (p = 0.562). Tumor size (odds ratio (OR) = 1.242) and alpha-fetoprotein (AFP) (OR = 2.527) were identified as risk factors for MVI. A predictive nomogram was constructed based on D1, ADC, tumor size, and AFP, which exhibited the highest diagnostic accuracy (AUC = 0.817), followed by D1 (AUC = 0.753) and ADC (AUC = 0.647). The diagnostic performance of the nomogram-based model was also validated by the calibration curve and decision curve. VDC can aid in the noninvasive and preoperative diagnosis of HCC with MVI, which may result in the clinical benefit in terms of prognostic prediction and clinical decision-making.

Multiple Machine-Learning Fusion Model Based on Gd-EOB-DTPA-Enhanced MRI and Aminotransferase-to-Platelet Ratio and Gamma-Glutamyl Transferase-to-Platelet Ratio to Predict Microvascular Invasion in Solitary Hepatocellular Carcinoma: A Multicenter Study.

Currently, it is still confused whether preoperative aminotransferase-to-platelet ratio (APRI) and gamma-glutamyl transferase-to-platelet ratio (GPR) can predict microvascular invasion (MVI) in solitary hepatocellular carcinoma (HCC). We aimed to develop and validate a machine-learning integration model for predicting MVI using APRI, GPR and gadoxetic acid disodium (Gd-EOB-DTPA) enhanced MRI. A total of 314 patients from XinQiao Hospital of Army Medical University were divided chronologically into training set (n = 220) and internal validation set (n = 94), and recurrence-free survival was determined to follow up after surgery. Seventy-three patients from Chongqing University Three Gorges Hospital and Luzhou People's Hospital served as external validation set. Overall, 387 patients with solitary HCC were analyzed as whole dataset set. Least absolute shrinkage and selection operator, tenfold cross-validation and multivariate logistic regression were used to gradually filter features. Six machine-learning models and an ensemble of the all models (ENS) were built. The area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate model's performance. APRI, GPR, HBPratio3 ([liver SI‒tumor SI]/liver SI), PLT, peritumoral enhancement, non-smooth margin and peritumoral hypointensity were independent risk factors for MVI. Six machine-learning models showed good performance for predicting MVI in training set (AUCs range, 0.793-0.875), internal validation set (0.715-0.832), external validation set (0.636-0.746) and whole dataset set (0.756-0.850). The ENS achieved the highest AUCs (0.879 vs 0.858 vs 0.839 vs 0.851) in four cohorts with excellent calibration and more net benefit. Subgroup analysis indicated that ENS obtained excellent AUCs (0.900 vs 0.809 vs 0.865 vs 0.908) in HCC >5cm, ≤5cm, ≤3cm and ≤2cm cohorts. Kaplan‒Meier survival curves indicated that ENS achieved excellent stratification for MVI status. The APRI and GPR may be new potential biomarkers for predicting MVI of HCC. The ENS achieved optimal performance for predicting MVI in different sizes HCC and may aid in the individualized selection of surgical procedures.

The influencing factors for tumor thrombus in patients with hepatocellular carcinoma.

Microvascular invasion (MVI) is considered to be an important factor in the early invasion and metastasis of liver cancer, and the survival rate of patients with MVI is much lower than that of patients without MVI. Therefore, it is crucial to accurately predict the independent predictors of tumor thrombus formation. This study aimed to assess the risk factors for tumor thrombus grades in patients with hepatocellular carcinoma (HCC). Between August 2011 and December 2022, the data of 231 patients diagnosed with HCC were collected and divided into the following three groups: an MVI-negative group, an MVI-positive group, and a portal vein tumor thrombus (PVTT) group. Univariate analysis was used to compare the differences between the three groups in terms of clinical features, pathology, and imaging features. Multiple logistic regression analysis was used to analyze the risk factors associated with tumor thrombus grades, and the cutoff value was finally calculated by using the receiver operating characteristic (ROC) curve. The incidence of MVI and PVTT in the patients with HCC were 10.0% and 6.1%, respectively; univariate analysis revealed statistically significant differences in tumor diameter, alpha fetoprotein level, Ki-67 expression level, gender, tumor quantity, arteriovenous fistula, peritumoral enhancement, and satellite nodules among the three groups (P<0.05). Multiple logistic regression analysis showed that Ki-67 expression level, tumor diameter, and peritumoral enhancement were independent risk factors for tumor thrombus grades (P<0.05). The area under the curve (AUC) of Ki-67 expression level and tumor diameter was 0.713 [95% confidence interval (CI): 0.626-0.800] and 0.753 (95% CI: 0.669-0.837), respectively, and the AUC of the combination analysis was 0.805 (95% CI: 0.723-0.888), with a cutoff value of 17.5% and 4.1 cm, respectively (P<0.05). Tumor diameter, Ki-67 expression level, and peritumoral enhancement can be used as independent predictors of tumor thrombus in patients with HCC. The combination of tumor diameter and Ki-67 expression level can further improve diagnostic efficacy.

Adjuvant hepatic arterial infusion chemotherapy in patients with resected hepatocellular carcinoma with microvascular invasion.

Adjuvant hepatic arterial infusion chemotherapy in patients with resected hepatocellular carcinoma with microvascular invasion.

Association of LI-RADS and Histopathologic Features with Survival in Patients with Solitary Resected Hepatocellular Carcinoma.

Background Both Liver Imaging Reporting and Data System (LI-RADS) and histopathologic features provide prognostic information in patients with hepatocellular carcinoma (HCC), but whether LI-RADS is independently associated with survival is uncertain. Purpose To assess the association of LI-RADS categories and features with survival outcomes in patients with solitary resected HCC. Materials and Methods This retrospective study included patients with solitary resected HCC from three institutions examined with preoperative contrast-enhanced CT and/or MRI between January 2008 and December 2019. Three independent readers evaluated the LI-RADS version 2018 categories and features. Histopathologic features including World Health Organization tumor grade, microvascular and macrovascular invasion, satellite nodules, and tumor capsule were recorded. Overall survival and disease-free survival were assessed with Cox regression models. Marginal effects of nontargetoid features on survival were estimated using propensity score matching. Results A total of 360 patients (median age, 64 years [IQR, 56-70 years]; 280 male patients) were included. At CT and MRI, the LI-RADS LR-M category was associated with increased risk of recurrence (CT: hazard ratio [HR] = 1.83 [95% CI: 1.26, 2.66], P = .001; MRI: HR = 2.22 [95% CI: 1.56, 3.16], P < .001) and death (CT: HR = 2.47 [95% CI: 1.72, 3.55], P < .001; MRI: HR = 1.80 [95% CI: 1.32, 2.46], P < .001) independently of histopathologic features. The presence of at least one nontargetoid feature was associated with an increased risk of recurrence (CT: HR = 1.80 [95% CI: 1.36, 2.38], P < .001; MRI: HR = 1.93 [95% CI: 1.81, 2.06], P < .001) and death (CT: HR = 1.51 [95% CI: 1.10, 2.07], P < .010) independently of histopathologic features. In matched samples, recurrence was associated with the presence of at least one nontargetoid feature at CT (HR = 2.06 [95% CI: 1.15, 3.66]; P = .02) or MRI (HR = 1.79 [95% CI: 1.01, 3.20]; P = .048). Conclusion In patients with solitary resected HCC, LR-M category and nontargetoid features were negatively associated with survival independently of histopathologic characteristics. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kartalis and Grigoriadis in this issue.

Radiomics of Preoperative Multi-Sequence Magnetic Resonance Imaging Can Improve the Predictive Performance of Microvascular Invasion in Hepatocellular Carcinoma.

The aim of the study is to demonstrate that radiomics of preoperative multi-sequence magnetic resonance imaging (MRI) can indeed improve the predictive performance of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). A total of 206 patients with pathologically confirmed HCC who underwent preoperative enhanced MRI were retrospectively recruited. Univariate and multivariate logistic regression analysis identified the independent clinicoradiologic predictors of MVI present and constituted the clinicoradiologic model. Recursive feature elimination (RFE) was applied to select radiomics features (extracted from six sequence images) and constructed the radiomics model. Clinicoradiologic model plus radiomics model formed the clinicoradiomics model. Five-fold cross-validation was used to validate the three models. Discrimination, calibration, and clinical utility were used to evaluate the performance. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the prediction accuracy between models. The clinicoradiologic model contained alpha-fetoprotein (AFP)_lg10, radiological capsule enhancement, enhancement pattern and arterial peritumoral enhancement, which were independent risk factors of MVI. There were 18 radiomics features related to MVI constructed the radiomics model. The mean area under the receiver operating curve (AUC) of clinicoradiologic, radiomics and clinicoradiomics model were 0.849, 0.925 and 0.950 in the training cohort and 0.846, 0.907 and 0.933 in the validation cohort, respectively. The three models' calibration curves fitted well, and decision curve analysis (DCA) confirmed the clinical usefulness. Compared with the clinicoradiologic model, the NRI of radiomics and clinicoradiomics model increased significantly by 0.575 and 0.825, respectively, and the IDI increased significantly by 0.280 and 0.398, respectively. Radiomics of preoperative multi-sequence MRI can improve the predictive performance of MVI in HCC.

A pathologic scoring system for predicting postoperative prognosis in patients with ruptured hepatocellular carcinoma

BackgroundThe accuracy of pathological factors to predict the prognosis of patients with ruptured hepatocellular carcinoma (rHCC) is unclear. We aimed to develop and validate a novel scoring system based on pathological factors to predict the postoperative survival of patients with rHCC. MethodPatients with rHCC who underwent hepatectomy were recruited from three hospitals and allocated to the training (n = 221) and validation (n = 194) cohorts. A new scoring system, namely the MSE (microvascular invasion-satellite foci-Edmondson Steiner) score, was established based on three pathological factors using univariate and multivariate Cox proportional hazards regression analyses, including microvascular invasion, satellite foci, and differentiation grade. Finally, patients were stratified into three groups based on their risk of prognosis (low, intermediate, or high) according to their MSE score. We also constructed MSE score-based nomograms. The performance of the nomograms was assessed by receiver operating characteristic and calibration curve analyses and validated using the validation cohort. ResultsThree pathological factors were significantly correlated with overall survival (OS) and recurrence-free survival (RFS), three of which were included in the MSE score. The score can clearly stratify rHCC patients after hepatectomy (P < 0.05). And we established nomograms based on the MSE score (MSE score, Barcelona Clinic Liver Cancer stage, and alpha-fetoprotein concentration) to predict postoperative OS and RFS in patients with rHCC. The nomograms showed good discrimination, with C-indices over 0.760 for OS and RFS at 1, 3, and 5 years, respectively. The calibration curve showed excellent nomogram calibration, which was also verified in the validation cohort. ConclusionThe clinical MSE score were accurate in predicting OS and RFS in patients with rHCC with resectable lesions after hepatectomy.

Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study

PurposeTo develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre.MethodA total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis. Then, a nomogram prediction model was constructed. External validation was performed on CT (n = 90) and MRI (n = 71) images from another centre.ResultsAmong the 23 radiological and clinical features, size, arterial peritumoral enhancement (APE), tumour margin and alpha-fetoprotein (AFP) were independent influencing factors for MVI in HCC. The nomogram integrating these risk factors had a good predictive effect, with AUC, specificity and sensitivity values of 0.834 (95% CI: 0.774–0.895), 75.0% and 83.5%, respectively. The AUC values of external verification based on CT and MRI image data were 0.794 (95% CI: 0.700–0.888) and 0.883 (95% CI: 0.807–0.959), respectively. No statistical difference in AUC values among training set and testing sets was found.ConclusionThe proposed nomogram prediction model for MVI in HCC has high accuracy, can be used with different imaging techniques, and has good clinical applicability.

Overexpression of tousled-like kinase 2 predicts poor prognosis in HBV-related hepatocellular carcinoma patients after radical resection.

Background: Tousled-like kinase 2 (TLK2) is integral to DNA repair, replication, and cell cycle regulation, crucial for maintaining genome stability and integrity. However, the expression and prognostic value of TLK2 in hepatitis B viral (HBV) -related hepatocellular carcinoma (HCC) remains unclear. Methods: We examined TLK2 expression and prognostic implications in pan-cancer by using diverse databases. Subsequently, TLK2 expression in HBV-related HCC tissues and adjacent tissues was assessed using quantitative real-time PCR and immunohistochemistry. The prognostic value of TLK2 was assessed through ROC curves, time-dependent ROC curves, Cox regression, Kaplan-Meier curve, and decision curve analysis. Additionally, analyses of immune infiltration, protein-protein interactions, key molecules of tumor-related signaling pathways, molecular subtypes, and TLK2-associated differentially expressed genes (DEGs) were conducted, along with GO/KEGG and GSEA enrichment analyses. Results: TLK2 expression was significantly higher in HCC tissues compared to adjacent tissues and correlated with gender, AFP levels, albumin-bilirubin (ALBI) grade, microvascular invasion (MVI), maximum tumor diameter, tumor number, and TNM stage. TLK2 overexpression emerged as an independent risk factor for overall survival (OS) and recurrence-free survival (RFS) in HBV-related HCC patients. An integrated OS nomogram model, incorporating TLK2, age, ALBI grade, MVI, and tumor number, displayed enhanced prognostic capability (C-index: 0.765, 95% CI: 0.732-0.798) in predicting OS and has a higher net benefit than the TNM stage. Moreover, TLK2 expression correlated closely with immune cell infiltration and key molecules of signaling pathways. Functional enrichment analyses highlighted significant associations with DNA duplex unwinding, double-strand break repair, DNA replication, cell cycle, E2F targets, G2M checkpoint, and MYC targets V1. Conclusion: TLK2 is notably overexpressed in HBV-related HCC and emerges as a promising prognostic biomarker, necessitating further validation.

Enhancing preoperative diagnosis of microvascular invasion in hepatocellular carcinoma: domain-adaptation fusion of multi-phase CT images

ObjectivesIn patients with hepatocellular carcinoma (HCC), accurately predicting the preoperative microvascular invasion (MVI) status is crucial for improving survival rates. This study proposes a multi-modal domain-adaptive fusion model based on deep learning methods to predict the preoperative MVI status in HCC.Materials and methodsFrom January 2008 to May 2022, we collected 163 cases of HCC from our institution and 42 cases from another medical facility, with each case including Computed Tomography (CT) images from the pre-contrast phase (PCP), arterial phase (AP), and portal venous phase (PVP). We divided our institution’s dataset (n=163) into training (n=119) and test sets (n=44) in an approximate 7:3 ratio. Additionally, we included cases from another institution (n=42) as an external validation set (test1 set). We constructed three single-modality models, a simple concatenated multi-modal model, two current state-of-the-art image fusion model and a multi-modal domain-adaptive fusion model (M-DAFM) based on deep learning methods. We evaluated and analyzed the performance of these constructed models in predicting preoperative MVI using the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI) methods.ResultsIn comparison with all models, M-DAFM achieved the highest AUC values across the three datasets (0.8013 for the training set, 0.7839 for the test set, and 0.7454 for the test1 set). Notably, in the test set, M-DAFM’s Decision Curve Analysis (DCA) curves consistently demonstrated favorable or optimal net benefits within the 0-0.65 threshold probability range. Additionally, the Net Reclassification Improvement (NRI) values between M-DAFM and the three single-modal models, as well as the simple concatenation model, were all greater than 0 (all p &amp;lt; 0.05). Similarly, the NRI values between M-DAFM and the two current state-of-the-art image fusion models were also greater than 0. These findings collectively indicate that M-DAFM effectively integrates valuable information from multi-phase CT images, thereby enhancing the model’s preoperative predictive performance for MVI.ConclusionThe M-DAFM proposed in this study presents an innovative approach to improve the preoperative predictive performance of MVI.

The role of cold ischemia time and hypothermic perfusion in predicting early hepatocellular carcinoma recurrences after liver transplantation.

The aim of the study was to identify predictors of early tumor recurrence in patients with hepatocellular carcinoma (HCC) after liver transplantation (LT). Retrospective cohort study in 237 consecutive liver recipients with HCC between 2016 and 2021. Multivariate logistic analysis was performed to identify predictors of early HCC recurrences. The impact of hypothermic-oxygenated perfusion (HOPE) on outcome was analyzed after propensity score weighting. Early recurrences were observed in 15 cases. Microvascular invasion (OR 3.737, 95% CI 1.246-11.206, p = 0.019) and cold ischemia time (OR 1.155, 95% CI 1.001-1.333, p = 0.049) were independently associated with a lower risk of HCC recurrences. After balancing for relevant variables, patients in the HOPE group had lower rates of tumor recurrence (weighted OR 0.126, 95% CI 0.016-0.989, p = 0.049) and higher recurrence free survival (weighted HR 0.132, 95% CI 0.017-0.999, p = 0.050). Reducing cold ischemia time and graft perfusion with HOPE can lead to lower rates of early HCC recurrences and higher recurrence-free survival.

CMIR: A Unified Cross-Modality Framework for Preoperative Accurate Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

Microvascular invasion of HCC is an important factor affecting postoperative recurrence and prognosis of patients. Preoperative diagnosis of MVI is greatly significant to improve the prognosis of HCC. Currently, the diagnosis of MVI is mainly based on the histopathological examination after surgery, which is difficult to meet the requirement of preoperative diagnosis. Also, the sensitivity, specificity and accuracy of MVI diagnosis based on a single imaging feature are low. In this paper, a robust, high-precision cross-modality unified framework for clinical diagnosis is proposed for the prediction of microvascular invasion of hepatocellular carcinoma. It can effectively extract, fuse and locate multi-phase MR Images and clinical data, enrich the semantic context, and comprehensively improve the prediction indicators in different hospitals. The state-of-the-art performance of the approach was validated on a dataset of HCC patients with confirmed pathological types. Moreover, CMIR provides a possible solution for related multimodality tasks in the medical field.

The potential of 18F-FDG PET/CT metabolic parameter-based nomogram inpredicting the microvascular invasion of hepatocellular carcinoma before liver transplantation.

Microvascular invasion (MVI) is a critical factor in predicting the recurrence and prognosis of hepatocellular carcinoma (HCC) after liver transplantation (LT). However, there is a lack of reliable preoperative predictors for MVI. The purpose of this study is to evaluate the potential of an 18F-FDG PET/CT-based nomogram in predicting MVI before LT for HCC. 83 HCC patients who obtained 18F-FDG PET/CT before LT were included in this retrospective research. To determine the parameters connected to MVI and to create a nomogram for MVI prediction, respectively, Logistic and Cox regression models were applied. Analyses of the calibration curve and receiver operating characteristic (ROC) curves were used to assess the model's capability to differentiate between clinical factors and metabolic data from PET/CT images. Among the 83 patients analyzed, 41% were diagnosed with histologic MVI. Multivariate logistic regression analysis revealed that Child-Pugh stage, alpha-fetoprotein, number of tumors, CT Dmax, and Tumor-to-normal liver uptake ratio (TLR) were significant predictors of MVI. A nomogram was constructed using these predictors, which demonstrated strong calibration with a close agreement between predicted and actual MVI probabilities. The nomogram also showed excellent differentiation with an AUC of 0.965 (95% CI 0.925-1.000). The nomogram based on 18F-FDG PET/CT metabolic characteristics is a reliable preoperative imaging biomarker for predicting MVI in HCC patients before undergoing LT. It has demonstrated excellent efficacy and high clinical applicability.