MRI-based clinical-radiomics nomogram model for predicting microvascular invasion in hepatocellular carcinoma

BACKGROUND

Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients.

PURPOSE

Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

METHOD

The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf-SVM with the best predictive performance was selected to construct the radiomics score (R-score). Finally, we combined R-score and clinical-pathology-image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC).

RESULT

Alpha-fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920-1.000) constructed by radiometry score (R-score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance.

CONCLUSION

This multi-parameter combined nomogram model had a good performance in predicting MVI of HCC, and had certain auxiliary value for the formulation of surgical plan and evaluation of prognosis.

Hepatic Arterial Infusion Chemotherapy vs Transcatheter Arterial Chemoembolization as Adjuvant Therapy Following Surgery for MVI-Positive Hepatocellular Carcinoma: A Multicenter Propensity Score Matching Analysis

Background

Microvascular invasion (MVI) is a significant pathological feature in hepatocellular carcinoma (HCC), adjuvant hepatic arterial infusion chemotherapy (a-HAIC) and adjuvant transcatheter arterial chemoembolization (a-TACE), are commonly used for HCC patients with MVI. This study aims to evaluate the efficacies of two adjuvant therapies after surgical treatment for HCC, compare them, and identify the significant factors.

Methods

Clinical data from two randomized controlled trials involving HCC patients with MVI after surgical treatment were retrospectively reviewed. Propensity score matching (PSM) analysis was performed to balance baseline differences between patients who received a-HAIC or a-TACE, and control groups who underwent hepatectomy alone. Disease-free survival (DFS) and overall survival (OS) rates were compared.

Results

In total of 549 patients were collected from two randomized controlled trials. Using the PSM and Kaplan-Meier method, the median DFS of the a-HAIC, a-TACE, and control groups was 63.2, 21.7, and 11.2 months (P<0.05). The a-HAIC group show significantly better 1-, 3-, and 5-year OS rates compared to the a-TACE and control groups (96.3%, 80.0%, 72.8% vs 84.4%, 57.0%, 29.8% vs 84.5%, 62.8%, 53.4%, P<0.05). But the OS rates of a-TACE and control groups showed no significant difference (P=0.279). Multivariate analysis identified a-HAIC (HR=0.449, P=0.000) and a-TACE (HR=0.633, P=0.007) as independent protective factors. For OS, a-HAIC (HR=0.388, P=0.003) was identified as an independent protective factor, too.

Conclusion

Compared to a-TACE and the control group, a-HAIC demonstrated greater benefits in preventing tumor recurrence and improving survival in HCC patients with MVI.

Identification of molecular characteristics of hepatocellular carcinoma with microvascular invasion based on deep targeted sequencing

BACKGROUND

As an indicator of tumor invasiveness, microvascular invasion (MVI) is a crucial risk factor for postoperative relapse, metastasis, and unfavorable prognosis in hepatocellular carcinoma (HCC). Nevertheless, the genetic mechanisms underlying MVI, particularly for Chinese patients, remain mostly uncharted.

METHODS

We applied deep targeted sequencing on 66 Chinese HCC samples. Focusing on the telomerase reverse transcriptase (TERT) promoter (TERTp) and TP53 co-mutation (TERTp+/TP53+) group, gene set enrichment analysis (GSEA) was used to explore the potential molecular mechanisms of the TERTp+/TP53+ group on tumor progression and metastasis. Additionally, we evaluated the tumor immune microenvironment of the TERTp+/TP53+ group in HCC using multiplex immunofluorescence (mIF) staining.

RESULTS

Among the 66 HCC samples, the mutated genes that mostly appeared were TERT, TP53, and CTNNB1. Of note, we found 10 cases with TERTp+/TP53+, of which nine were MVI-positive and one was MVI-negative, and there was a co-occurrence of TERTp and TP53 (p < 0.05). Survival analysis demonstrated that patients with the TERTp+/TP53+ group had lower the disease-free survival (DFS) (p = 0.028). GSEA results indicated that telomere organization, telomere maintenance, DNA replication, positive regulation of cell cycle, and negative regulation of immune response were significantly enriched in the TERTp+/TP53+ group (all adjusted p-values (p.adj) < 0.05). mIF revealed that the TERTp+/TP53+ group decreased CD8+ T cells infiltration (p = 0.25) and enhanced PDL1 expression (p = 0.55).

CONCLUSIONS

TERTp+/TP53+ was significantly enriched in MVI-positive patients, leading to poor prognosis for HCC patients by promoting proliferation of HCC cell and inhibiting infiltration of immune cell surrounding HCC. TERTp+/TP53+ can be utilized as a potential indicator for predicting MVI-positive patients and poor prognosis, laying a preliminary foundation for further exploration of co-mutation in HCC with MVI and clinical treatment.

A Transformer-Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation

BACKGROUND & AIMS

We aimed to develop a Transformer-based deep learning (DL) network for prognostic stratification in hepatocellular carcinoma (HCC) patients undergoing RFA.

METHODS

A Swin Transformer DL network was trained to establish associations between magnetic resonance imaging (MRI) datasets and the ground truth of microvascular invasion (MVI) based on 696 surgical resection (SR) patients with solitary HCC ≤3 cm, and was validated in an external cohort (n = 180). The multiphase MRI-based DL risk outputs using an optimal threshold of .5 was employed as a MVI classifier for prognosis stratification in the RFA cohort (n = 180).

RESULTS

Over 90% of all enrolled patients exhibited hepatitis B virus infection. Liver cirrhosis was significantly more prevalent in the RFA cohort compared to the SR cohort (72.2% vs. 44.1%, p < .001). The MVI risk outputs exhibited good performance (area under the curve values = .938 and .883) for predicting MVI in the training and validation cohort, respectively. The RFA patients at high risk of MVI classified by the MVI classifier demonstrated significantly lower recurrence-free survival (RFS) and overall survival rates at 1, 3 and 5 years compared to those classified as low risk (p < .001). Multivariate cox regression modelling of a-fetoprotein > 20 ng/mL [hazard ratio (HR) = 1.53; 95% confidence interval (95% CI): 1.02-2.33, p = .047], high risk of MVI (HR = 3.76; 95% CI: 2.40-5.88, p < .001) and unfavourable tumour location (HR = 2.15; 95% CI: 1.40-3.29, p = .001) yielded a c-index of .731 (bootstrapped 95% CI: .667-.778) for evaluating RFS after RFA. Among the three risk factors, MVI was the most powerful predictor for intrahepatic distance recurrence.

CONCLUSIONS

The proposed MVI classifier can serve as a valuable imaging biomarker for prognostic stratification in early-stage HCC patients undergoing RFA.

Preoperative Contrast-Enhanced Ultrasound Predicts Microvascular Invasion in Hepatocellular Carcinoma as Accurately as Contrast-Enhanced MR

OBJECTIVES

Both contrast-enhanced ultrasound (CEUS) and contrast-enhanced magnetic resonance (CEMR) are important imaging methods for hepatocellular carcinoma (HCC). This study aimed to establish a model using preoperative CEUS parameters to predict microvascular invasion (MVI) in HCC, and compare its predictive efficiency with that of CEMR model.

METHODS

A total of 93 patients with HCC (39 cases in MVI positive group and 54 cases in MVI negative group) who underwent surgery in our hospital from January 2020 to June 2021 were retrospectively analyzed. Their clinical and imaging data were collected to establish CEUS and CEMR models for predicting MVI. The predictive efficiencies of both models were compared.

RESULTS

By the univariate and multivariate regression analyses of patients' clinical information, preoperative CEUS static and dynamic images, we found that serrated edge and time to peak were independent predictors of MVI. The CEUS prediction model achieved a sensitivity of 92.3%, a specificity of 83.3%, and an accuracy of 84.6% (Az: 0.934). By analyzing the clinical and CEMR information, we found that tumor morphology, fast-in and fast-out, peritumoral enhancement, and capsule were independent predictors of MVI. The CEMR prediction model achieved a sensitivity of 97.4%, a specificity of 77.8%, and an accuracy of 83.2% (Az: 0.900). The combination of the two models achieved a sensitivity of 84.6%, a specificity of 87.0%, and an accuracy of 86.2% (Az: 0.884). There was no significant statistical difference in the areas under the ROC curve of the three models.

CONCLUSION

The CEUS model and the CEMR model have similar predictive efficiencies for MVI of HCC. CEUS is also an effective method to predict MVI before operation.

Microvascular invasion is associated with poor survival in patients with dual-phenotype hepatocellular carcinoma

OBJECTIVES

Microvascular invasion (MVI) has previously been reported to be related to cancer prognosis; however, its significance in patients with dual-phenotype hepatocellular carcinoma (DPHCC) remains uncharacterized. We studied the role of MVI in the survival of patients diagnosed with DPHCC in Fujian, China, which has a high incidence of HCC.

METHODS

Patients with DPHCC (n = 84) who had undergone surgical interventions at the 900th Hospital of the Joint Logistic Support Force between 2013 and 2019 were retrospectively analyzed using the log-rank test and Kaplan-Meier method. Univariate and multivariate Cox model analyses were also conducted to further understand the correlation between MVI and patient survival.

RESULTS

Our results indicated that MVI was related to poor survival. According to the univariate analysis, MVI, the number of tumor lesions, necrosis, differentiation, peripheral hepatic fibrosis, the expression of cytokeratin 19 (CK19), and serum levels of both ɑ-fetoprotein (AFP) and cancer antigen-199 showed a strong correlation with overall survival. Necrosis and serum AFP levels were strongly related to an increased risk of death, according to the multivariate analysis. Tumor size; the number of tumor lesions; differentiation; peripheral hepatic fibrosis; liver capsule invasion; and expression of CK19, vascular endothelial growth factor, CK7, and mucin 1 showed a correlation with MVI, per the outcomes of χ2 tests.

CONCLUSIONS

Microvascular invasion may correlate with the survival of patients with DPHCC and could potentially serve as a prognostic predictor of survival.

Accumulation of TOX high mobility group box family member 3 promotes the oncogenesis and development of hepatocellular carcinoma through the MAPK signaling pathway

Microvascular invasion (MVI) has been widely valued in the field of liver surgery because MVI positivity indicates poor prognosis in hepatocellular carcinoma (HCC) patients. However, the potential molecular mechanism underlying the poor prognosis of MVI-positive HCC patients is unclear. Therefore, this study focused on identifying the key genes leading to poor prognosis in patients with a high degree of malignancy of HCC by examining the molecular signaling pathways in MVI-positive HCC patients. Through RNA sequencing, TOX high mobility group box family member 3 (TOX3) was demonstrated to be significantly highly expressed in MVI-positive HCC tissues, which was associated with poor prognosis. The results of in vivo and in vitro showed that TOX3 can promote the oncogenesis and development of HCC by targeting key molecules of the MAPK and EMT signaling pathways. The IP-MS results indicated that proteasome degradation of TOX3 in HCC cells is potentially mediated by a tripartite motif containing 56 (TRIM56, an E3 ligase) in HCC cells. Inhibiting TRIM56 enhances TOX3 protein levels. Overall, our study identified TOX3 as a key gene in the MAPK and EMT signaling pathways in HCC, and its overexpression confers significant proliferation and invasiveness to tumor cells.

Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images

BACKGROUND

Microvascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time-consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep-learning model that could significantly improve the efficiency and accuracy of MVI diagnosis.

MATERIALS AND METHODS

We collected H&E-stained slides from 753 patients with HCC at the First Affiliated Hospital of Zhejiang University. An external validation set with 358 patients was selected from The Cancer Genome Atlas database. The deep-learning model was trained by simulating the method used by pathologists to diagnose MVI. Model performance was evaluated with accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve.

RESULTS

We successfully developed a MVI artificial intelligence diagnostic model (MVI-AIDM) which achieved an accuracy of 94.25% in the independent external validation set. The MVI positive detection rate of MVI-AIDM was significantly higher than the results of pathologists. Visualization results demonstrated the recognition of micro MVIs that were difficult to differentiate by the traditional pathology. Additionally, the model provided automatic quantification of the number of cancer cells and spatial information regarding MVI.

CONCLUSIONS

We developed a deep learning diagnostic model, which performed well and improved the efficiency and accuracy of MVI diagnosis. The model provided spatial information of MVI that was essential to accurately predict HCC recurrence after surgery.

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

Background

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.

Methods

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.

Results

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.

Conclusion

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.

[Prediction of microvascular invasion in hepatocellular carcinoma based on multi-phase dynamic enhanced CT radiomics feature and multi-classifier hierarchical fusion model]

OBJECTIVE

To predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using a model based on multiphase dynamic-enhanced CT (DCE-CT) radiomics feature and hierarchical fusion of multiple classifiers.

METHODS

We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January, 2016 and April, 2020. The volume of interest was outlined in the early arterial phase, late arterial phase, portal venous phase and equilibrium phase, and radiomics features of these 4 phases were extracted. Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase. According to the hierarchical fusion strategy, a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model. The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve (AUC), accuracy, sensitivity, and specificity. The prediction model was also compared with the fusion models using a single phase or multiple phases, models based on a single phase with a single classifier, models with different base classifier diversities, and 8 classifier models based on other ensemble methods.

RESULTS

The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers, with AUC, accuracy, sensitivity, and specificity of 0.828, 0.766, 0.877, and 0.648, respectively. Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models.

CONCLUSION

The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.

Protein profiles reveal MSH6/MSH2 as a potential biomarker for hepatocellular carcinoma with microvascular invasion

AIM

Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence and metastasis in hepatocellular carcinoma (HCC). However, the specific protein expression profiles that differentiate HCC with MVI from those without MVI remain unclear.

METHODS

The profiles of proteins in early-stage HCC tissues and normal liver tissues were characterized by quantitative proteomics techniques. Immunohistochemical (IHC) staining was undertaken on tissue microarrays from 80 HCC patients to assess the expression of MSH2 and MSH6. Cell counting, colony formation, migration, and invasion assays were carried out in vitro.

RESULTS

We identified 5164 proteins in both HCC tissues and adjacent normal liver tissues. Compared to HCC without MVI, 148 upregulated proteins and 97 downregulated proteins were found in HCC with MVI. Particularly noteworthy was the remarkable upregulation of MSH6/MSH2 among these dysregulated proteins in HCC with MVI. Further validation through bioinformatics prediction and IHC confirmed the elevated expression of MSH6/MSH2, which correlated with aggressive disease characteristics and poor prognosis. Receiver operating characteristic curve analyses revealed a substantial area under the curve of 0.761 (specificity 71.79%, sensitivity 73.17%) for the combined use of MSH6/MSH2. Knockdown of MSH6/MSH2 significantly inhibited HCC cell proliferation and invasion in vitro.

CONCLUSIONS

Our study establishes MSH6 or MSH2 as an oncogene that is prominently overexpressed during HCC progression, which provides new targets for HCC with MVI.

Exploring the factors affecting the occurrence of postoperative MVI and the prognosis of hepatocellular carcinoma patients treated with hepatectomy: A multicenter retrospective study

OBJECTIVE

To investigate the influencing factors affecting the occurrence of microvascular invasion (MVI) and the prognosis of hepatocellular carcinoma (HCC) patients treated with hepatectomy, and to explore how MVI affects prognosis in subgroups with different prognostic factors.

METHODS

Clinical data of a total of 1633 patients treated surgically for HCC in four treatment centers were included, including 754 patients with MVI. By using the Cox risk regression model and the Mann-Whitney U-test, the common independent influences on prognosis and MVI were made clear. The incidence of MVI in various subgroups was then examined, as well as the relationship between MVI in various subgroups and prognosis.

RESULTS

The Cox risk regression model showed that MVI, Child-Pugh classification, alpha-fetoprotein (AFP), hepatocirrhosis, tumor diameter, lymphocyte-to-monocyte ratio (LMR), and, Barcelona clinic liver cancer (BCLC) grade were independent determinants of overall survival (OS), and MVI, AFP, hepatocirrhosis, tumor diameter, and LMR were influencing determinants for disease-free survival (DFS). The receiver operating characteristic (ROC) curve showed that MVI was most closely associated with patient prognosis compared to other prognostic factors. AFP, hepatocirrhosis, tumor diameter, and LMR were discovered to be common influences on the prognosis of patients with HCC and MVI when combined with the results of the intergroup comparison of MVI. After grouping, it was showed that patients with hepatocirrhosis, positive AFP (AFP ≥ 20 ng/mL), tumor diameter >50 mm, and LMR ≤3.4 had a significantly higher incidence of MVI than patients in other subgroups, and all four subgroups of MVI-positive patients had higher rates of early recurrence and mortality (p < 0.05).

CONCLUSIONS

MVI was found to be substantially linked with four subgroups of HCC patients with hepatocirrhosis, positive AFP, tumor diameter >50 mm, and LMR ≤3.4, and the prognosis of MVI-positive patients in all four subgroups tended to be worse.

Microvascular Invasion in Hepatocellular Carcinoma: Some Puzzling Facets

BACKGROUND/AIMS

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

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.

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

Objectives

In 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 methods

From 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.

Results

In 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 < 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.

Conclusion

The M-DAFM proposed in this study presents an innovative approach to improve the preoperative predictive performance of MVI.

A Grading System of Microvascular Invasion for Patients with Hepatocellular Carcinoma Undergoing Liver Resection with Curative Intent: A Multicenter Study

Background

Microvascular invasion (MVI) is closely correlated with poor clinical outcomes in patients with hepatocellular carcinoma (HCC). A grading system of MVI is needed to assist in the management of HCC patient.

Methods

Multicenter data of HCC patients who underwent liver resection with curative intent was analyzed. This grading system was established by detected number and distance from tumor boundary of MVI. Survival outcomes were compared among patients in each group. This system was verified by time-receiver operating characteristic curve, time-area under the curve, calibration curve, and decision curve analyses. Cox regression analysis was performed to study the associated factors of prognosis. Logistic analysis was used to study the predictive factors of MVI.

Results

All patients were classified into 4 groups: M0: no MVI; M1: 1~5 proximal MVIs (≤1 cm from tumor boundary); M2a: >5 proximal MVIs (≤1 cm from tumor boundary); M2b: ≥1 distal MVIs (>1 cm from tumor boundary). The recurrence-free survival (RFS), overall survival (OS), and early RFS rates among all the individual groups were significantly different. Based on the number of proximal MVI (0~5 vs >5), patients in the M2b group were further divided into two subgroups which also showed different prognosis. Multiple methods showed this grading system to be significantly better than the MVI two-tiered system in prognostic evaluation. Four multivariate models for RFS, OS, early RFS, late RFS, and a predictive model of MVI were then established and were shown to satisfactorily evaluate prognosis and have a great discriminatory power, respectively.

Conclusion

This MVI grading system could precisely evaluate prognosis of HCC patients after liver resection with curative intent and it could be employed in routine pathological reports. The severity of MVI from both adjacent and distant from tumor boundary should be stated. A hypothesis about two occurrence modes of distal MVI was proposed.

Influence of Lymphatic, Microvascular and Perineural Invasion on Oncological Outcome in Patients with Neuroendocrine Tumors of the Small Intestine

For the histopathological work-up of resected neuroendocrine tumors of the small intestine (siNET), the determination of lymphatic (LI), microvascular (VI) and perineural (PnI) invasion is recommended. Their association with poorer prognosis has already been demonstrated in many tumor entities. However, the influence of LI, VI and PnI in siNET has not been sufficiently described yet. A retrospective analysis of all patients treated for siNET at the ENETS Center of Excellence Charité-Universitätsmedizin Berlin, from 2010 to 2020 was performed (n = 510). Patients who did not undergo primary resection or had G3 tumors were excluded. In the entire cohort (n = 161), patients with LI, VI and PnI status had more distant metastases (48.0% vs. 71.4%, p = 0.005; 47.1% vs. 84.4%, p < 0.001; 34.2% vs. 84.7%, p < 0.001) and had lower rates of curative surgery (58.0% vs. 21.0%, p < 0.001; 48.3% vs. 16.7%, p < 0.001; 68.4% vs. 14.3%, p < 0.001). Progression-free survival was significantly reduced in patients with LI, VI or PnI compared to patients without. This was also demonstrated in patients who underwent curative surgery. Lymphatic, vascular and perineural invasion were associated with disease progression and recurrence in patients with siNET, and these should therefore be included in postoperative treatment considerations.

Effect of transarterial chemoembolization as postoperative adjuvant therapy for intermediate-stage hepatocellular carcinoma with microvascular invasion: a multicenter cohort study

BACKGROUND

Intermediate-stage hepatocellular carcinoma (HCC) with microvascular invasion (MVI) is associated with high recurrence rates and poor survival outcomes after surgery. This study aimed to evaluate the efficacy of postoperative transarterial chemoembolization (TACE) on prognosis of intermediate-stage HCC patients with MVI after curative liver resection (LR).

MATERIALS AND METHODS

Patients who had intermediate-stage HCC with MVI and underwent curative LR between January 2013 and December 2019 at three institutions in China were identified for further analysis. Overall survival (OS) and recurrence-free survival (RFS) were compared between patients treated with and without postoperative TACE by propensity score-matching.

RESULTS

A total of 246 intermediate-stage HCC patients with MVI were enrolled, 137 entered into the LR group and 109 entered into the LR+TACE group. The 1-year, 3-year, and 5-year RFS rates were 42.0, 27.2, and 17.8% in LR+TACE group, and 31.8, 18.2, and 8.7% in LR group. The 1-year, 3-year, and 5-year OS rates were 81.7, 47.2, and 26.1% in the LR+TACE group, and 67.3, 35.6, and 18.5% in the LR group. Compared with LR alone, LR+TACE was associated with significantly better RFS [hazard ratio (HR), 1.443; 95% CI: 1.089-1.914; P =0.009] and OS (HR, 1.438; 95% CI: 1.049-1.972; P =0.023). No difference was observed with RFS and OS in single TACE and multiple TACE in the matched cohort.

CONCLUSION

Postoperative adjuvant TACE could be beneficial for intermediate-stage HCC patients with MVI.

Preoperative CT Radiomics Nomogram for Predicting Microvascular Invasion in Stage I Non-Small Cell Lung Cancer

RATIONALE AND OBJECTIVES: This study aims to develop and validate a nomogram integrating clinical-CT and radiomic features for preoperative prediction of microvascular invasion (MVI) in patients with stage I non‑small cell lung cancer (NSCLC).

MATERIALS AND METHODS

This retrospective study analyzed 188 cases of stage I NSCLC (63 MVI positives and 125 negatives), which were randomly assigned to training (n = 133) and validation cohorts (n = 55) at a ratio of 7:3. Preoperative non-contrast and contrast-enhanced CT (CECT) images were used to analyze computed tomography (CT) features and extract radiomics features. The student's t-test, the Mann-Whitney-U test, the Pearson correlation, the least absolute shrinkage and selection operator, and multivariable logistic analysis were used to select the significant CT and radiomics features. Multivariable logistic regression analysis was performed to build the clinical-CT, radiomics, and integrated models. The predictive performances were evaluated through the receiver operating characteristic curve and compared with the DeLong test. The integrated nomogram was analyzed regarding discrimination, calibration, and clinical significance.

RESULTS

The rad-score was developed with one shape and four textural features. The integrated nomogram incorporating radiomics score, spiculation, and the number of tumor-related vessels (TVN) demonstrated better predictive efficacy than the radiomics and clinical-CT models in the training cohort (area under the curve [AUC], 0.893 vs 0.853 and 0.828, and p = 0.043 and 0.027, respectively) and validation cohort (AUC, 0.887 vs 0.878 and 0.786, and p = 0.761 and 0.043, respectively). The nomogram also demonstrated good calibration and clinical usefulness.

CONCLUSION

The radiomics nomogram integrating the radiomics with clinical-CT features demonstrated good performance in predicting MVI status in stage I NSCLC. The nomogram may be a useful tool for physicians in improving personalized management of stage I NSCLC.

Arachidonic acid metabolism CYP450 pathway is deregulated in hepatocellular carcinoma and associated with microvascular invasion

Arachidonic acid metabolism plays a crucial role in the development and progression of inflammatory and metabolic liver diseases. However, its role in hepatocellular carcinoma (HCC) remains unclear. In this study, we investigated the expression of key genes involved in the arachidonic acid metabolism pathway in HCC using a combination of bioinformatics, proteomics and immunohistochemistry analyses. Through a comprehensive analysis of publicly available datasets, clinical HCC tissues, and tissue microarrays, we compared the expression of hepatic arachidonic acid metabolic genes. We observed significant downregulation of cytochrome P450 (CYP450) pathway genes at both the messenger RNA and protein levels in HCC tissues compared to normal liver tissues. Furthermore, we observed a strong correlation between the deregulation of the arachidonic acid metabolism CYP450 pathway and the pathological features and prognosis of HCC. Specifically, the expression of CYP2C8/9/18/19 was significantly correlated with pathological grade (r = -.484, p < .0001), vascular invasion (r = -.402, p < .0001), aspartate transaminase (r = -.246, p = .025), gamma-glutamyl transpeptidase (r = -.252, p = .022), alkaline phosphatase (r = -.342, p = .002), alpha-fetoprotein (r = -.311, p = .004) and carbohydrate antigen 19-9 (r = -.227, p = .047). Moreover, we discovered a significant association between CYP450 pathway activity and vascular invasion in HCC. Collectively, these data indicate that arachidonic acid CYP450 metabolic pathway deregulation is implicated in HCC progression and may be a potential predictive factor for early recurrence in patients with HCC.

Clinical benefits of intraoperative radiotherapy for the recurrence of centrally located hepatocellular carcinoma with microvascular invasion

BACKGROUND

Although the efficacy and safety of intraoperative radiotherapy (IORT) in the treatment of malignant tumours, such as breast cancer, have been documented, it remains unclear whether this treatment is effective for centrally located hepatocellular carcinoma (HCC) with microvascular invasion (MVI).

AIMS

This study aimed to explore the efficacy and safety of IORT in the treatment of centrally located HCC with MVI.

METHODS AND RESULTS

Patients with centrally located HCC, who underwent surgery between January 2016 and January 2020, were enrolled. The patient cohort was then allocated to two groups: those who underwent IORT combined with liver resection (IORT+LR); or LR alone (LR). Propensity score matching and Cox proportional hazards regression analyses were performed. The Kaplan-Meier method was used to estimate recurrence-free survival (RFS), and the log-rank test was used to determine whether RFS differed between the groups. Subgroup analysis was performed to evaluate differences in RFS and early recurrence rates in patients with different MVI grades. E-values were generated to measure the sensitivity to unmeasured confounding factors. In total, 97 patients were enrolled, 27 of whom underwent IORT+LR and 70 underwent LR alone. The 1-, 3-, and 5-year RFS rates in the IORT+LR group were 66%, 50%, and 32%, respectively, whereas those in the LR group were 54%, 37%, and 26%, respectively. After matching analysis, 23 patients were successfully matched, and RFS was found to be significantly different between the two groups (p = .04). IORT was an independent prognostic factor for RFS (hazard ratio 0.46 [95% confidence interval 0.21-0.99]). In subgroup analysis, RFS between the IORT+LR and LR groups was significantly different in patients with MVI (M1 grade) (p = .0067). The postoperative early recurrence rate was significantly reduced with IORT (p < .05). No serious complications were reported in either group following surgery. Based on E-values, the results appeared to be robust against unmeasured confounding factors.

CONCLUSION

IORT+LR provided safe, feasible treatment for patients with centrally located HCC with MVI, along with an improvement in prognosis and lower early recurrence rates.

Evaluate the Microvascular Invasion of Hepatocellular Carcinoma (≤5 cm) and Recurrence Free Survival with Gadoxetate Disodium-Enhanced MRI-Based Habitat Imaging

BACKGROUND

Tumors are heterogenous and consist of subregions, also known as tumoral habitats, each exhibiting varied biological characteristics. Each habitat corresponds to a cluster of tissue sharing similar structural, metabolic, or functional characteristics. The habitat imaging technique facilitates both the visualization and quantification of these tumoral habitats.

PURPOSE

To evaluate the microvascular invasion (MVI) in hepatocellular carcinoma (HCC) (≤5 cm) and assess the recurrence-free survival (RFS) using gadoxetate disodium-enhanced MRI-based habitat imaging.

STUDY TYPE

Retrospective.

SUBJECTS

180 patients (52.9 years ± 11.7, 156 men) with HCC.

FIELD STRENGTH/SEQUENCE

1.5T/contrast-enhanced T1-weighted gradient-echo sequence.

ASSESSMENT

The enhancement ratio of signal intensity at the arterial phase (AER) and hepatobiliary phase (HBPER) were calculated. The HCC lesions and their peritumoral tissues of 3, 5, and 7 mm were encoded into four habitats. The volume fraction of each habitat was then quantified. The diagnostic performance was assessed using the receiver operating characteristic analysis with 5-fold cross-validation. The RFS was evaluated with Kaplan-Meier curves.

RESULTS

Habitat 2 (with median to high AER and low HBPER) within the peritumoral tissue of 3 mm (f2 -P3 ) and tumor diameter could serve as independent risk factors for MVI and showed the statistical significance (odds ratio (OR) of f2 -P3  = 1.170, 95% CI = 1.099-1.246; OR of tumor diameter: 6.112, 95% CI = 2.162-17.280). A nomogram was developed by incorporating f2 -P3 and tumor diameter, demonstrating high diagnostic accuracy. The area under the curve from 5-fold cross-validation ranged from 0.880 to 1.000. Additionally, the nomogram model demonstrated high efficacy in risk stratification for RFS.

CONCLUSION

Habitat imaging of HCC and its peritumoral microenvironment has the potential for noninvasive and preoperative identification of MVI and prognostic assessment.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Specific gut microbiome signature predicts hepatitis B virus-related hepatocellular carcinoma patients with microvascular invasion

BACKGROUND

We aimed to assess differences in intestinal microflora between patients with operable hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) with microvascular invasion (MVI) and those without MVI. Additionally, we investigated the potential of the microbiome as a non-invasive biomarker for patients with MVI.

METHODS

We analyzed the preoperative gut microbiomes (GMs) of two groups, the MVI (n = 46) and non-MVI (n = 56) groups, using 16S ribosomal RNA gene sequencing data. At the operational taxonomic unit level, we employed random forest models to predict MVI risk and validated the results in independent validation cohorts [MVI group (n = 17) and non-MVI group (n = 15)].

RESULTS

β diversity analysis, utilizing weighted UniFrac distances, revealed a significant difference between the MVI and non-MVI groups, as indicated by non-metric multidimensional scaling and principal coordinate analysis. We also observed a significant correlation between the characteristic intestinal microbial communities at the genus level and their main functions. Nine optimal microbial markers were identified, with an area under the curve of 79.76% between 46 MVI and 56 non-MVI samples and 79.80% in the independent verification group.

CONCLUSION

This pioneering analysis of the GM in patients with operable HBV-HCC with and without MVI opens new avenues for treating HBV-HCC with MVI. We successfully established a diagnostic model and independently verified microbial markers for patients with MVI. As preoperative targeted biomarkers, GM holds potential as a non-invasive tool for patients with HBV-HCC with MVI.

Serum immune biomarker levels combined with hepatitis B virus infection status predict early recurrence of early-stage hepatocellular carcinoma with microvascular invasion after liver resection

INTRODUCTION

The tumor immune response plays a vital role in cancer recurrence in patients with malignancies. We aim to clarify the risk factors for early recurrence and investigate the efficacy of blood-based biomarkers to predict the risk of early recurrence in early-stage hepatocellular carcinoma (HCC) patients with microvascular invasion (MVI) after hepatectomy.

MATERIALS AND METHODS

A total of 101 cases of HCC with MVI who underwent liver resection were enrolled. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors of early recurrence. We calculated the area under the receiver operating characteristic curve to evaluate the performance of the four biomarkers identified as risk factors for early recurrence.

RESULTS

Multiple logistic regression analysis indicated that complement (C)4, cluster of differentiation (CD)4+, immunoglobulin A (IgA), and hepatitis B virus (HBV) DNA of greater than 500 IU/mL were correlated with early recurrence of HCC. The area under the curve was greater for the combination model than for the HBV DNA, CD4+, IgA, or C4 models alone.

CONCLUSION

Preoperative serum CD4+, C4, IgA, and HBV DNA levels were linked with early recurrence of early-stage HCC with MVI and the combination model was of considerable predictive value for the prognosis of HCC with MVI.

The Comparison of Surgical Margins and Type of Hepatic Resection for Hepatocellular Carcinoma With Microvascular Invasion

OBJECTIVE

The objective of this study was to investigate the impact of surgical margin and hepatic resection on prognosis and compare their importance on prognosis in patients with hepatocellular carcinoma (HCC).

METHODS

The clinical data of 906 patients with HCC who underwent hepatic resection in our hospital from January 2013 to January 2015 were collected retrospectively. All patients were divided into anatomical resection (AR) (n = 234) and nonanatomical resection (NAR) group (n = 672) according to type of hepatic resection. The effects of AR and NAR and wide and narrow margins on overall survival (OS) and time to recurrence (TTR) were analyzed.

RESULTS

In all patients, narrow margin (1.560, 1.278-1.904; 1.387, 1.174-1.639) is an independent risk factor for OS and TTR, and NAR is not. Subgroup analysis showed that narrow margins (2.307, 1.699-3.132; 1.884, 1.439-2.468), and NAR (1.481, 1.047-2.095; 1.372, 1.012-1.860) are independent risk factors for OS and TTR in patients with microvascular invasion (MVI)-positive. Further analysis showed that for patients with MVI-positive HCC, NAR with wide margins was a protective factor for OS and TTR compared to AR with narrow margins (0.618, 0.396-0.965; 0.662, 0.448-0.978). The 1, 3, and 5 years OS and TTR rate of the two group were 81%, 49%, 29% versus 89%, 64%, 49% (P = .008) and 42%, 79%, 89% versus 32%, 58%, 74% (P = .024), respectively.

CONCLUSIONS

For patients with MVI-positive HCC, AR and wide margins were protective factors for prognosis. However, wide margins are more important than AR on prognosis. In the clinical setting, if the wide margins and AR cannot be ensured at the same time, the wide margins should be ensured first.

Circulating tumor cells participate in the formation of microvascular invasion and impact on clinical outcomes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. Although the treatment strategies have been improved in recent years, the long-term prognosis of HCC is far from satisfactory mainly due to high postoperative recurrence and metastasis rate. Vascular tumor thrombus, including microvascular invasion (MVI) and portal vein tumor thrombus (PVTT), affects the outcome of hepatectomy and liver transplantation. If vascular invasion could be found preoperatively, especially the risk of MVI, more reasonable surgical selection will be chosen to reduce the risk of postoperative recurrence and metastasis. However, there is a lack of reliable prediction methods, and the formation mechanism of MVI/PVTT is still unclear. At present, there is no study to explore the possibility of tumor thrombus formation from a single circulating tumor cell (CTC) of HCC, nor any related study to describe the possible leading role and molecular mechanism of HCC CTCs as an important component of MVI/PVTT. In this study, we review the current understanding of MVI and possible mechanisms, discuss the function of CTCs in the formation of MVI and interaction with immune cells in the circulation. In conclusion, we discuss implications for potential therapeutic targets and the prospect of clinical treatment of HCC.

Characterization of Microvascular Invasion in Hepatocellular Carcinoma Using Computational Modeling of Interstitial Fluid Pressure and Velocity

BACKGROUND

Most solid tumors show increased interstitial fluid pressure (IFP), and this increased IFP is an obstacle to treatment. A noninvasive model for measuring IFP in hepatocellular carcinoma (HCC) is an unresolved issue.

PURPOSE

To develop a noninvasive model to measure IFP and interstitial fluid velocity (IFV) in HCC and to characterize the microvascular invasion (MVI) status by using this model.

STUDY TYPE

Retrospective.

POPULATION

A total of 97 HCC patients (mean age 57.6 ± 10.9 years, 77.3% males), 53 of them with MVI and 44 of them without MVI.

FIELD STRENGTH/SEQUENCE

A 3-T, three-dimensional spoiled gradient-recalled echo.

ASSESSMENT

MVI was defined as microscopic vascular invasion of small vessels within the peritumoral liver tissue. The volumes of interest (VOIs) were manually delineated and enclosed the tumor lesion and healthy liver parenchyma, respectively. The extended Tofts model (ETM) was used to estimate permeability parameters from all the VOIs. Subsequently, the continuity partial differential equation (PDE) was implemented and IFP and IFV were acquired.

STATISTICAL TESTS

Wilcoxon signed-ranks tests, histogram analysis, Mann-Whitney U test, Fisher's exact test, least absolute shrinkage and selection operator (LASSO) logistic regression, receiver operating characteristic (ROC) curve analysis with the area under the curve (AUC), Youden index, DeLong test, and Benjamini-Hochberg correction. A P value <0.05 was considered statistically significant.

RESULTS

The HCC lesions exhibited elevated IFP and reduced IFV. There were no significant differences in any measured demographic and clinical features between the MVI-positive and MVI-negative groups, except for tumor size. Nine IFP histogram analysis-derived parameters and seven IFV histogram analysis-derived parameters could be used to characterize the MVI status. LASSO regression selected five features: IFP maximum, IFP 10th percentile, IFP 90th percentile, IFV SD, and IFV 10th percentile. The combination of these features showed the highest AUC (0.781) and specificity (77.3%).

DATA CONCLUSION

A noninvasive IFP and IFV measurement model for HCC was developed. Specific IFP- and IFV-derived parameters exhibited significant association with the MVI status.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

Neoadjuvant intensity modulated radiotherapy for a single and small (≤5 cm) hepatitis B virus-related hepatocellular carcinoma predicted to have high risks of microvascular invasion: a randomized clinical trial

BACKGROUND

The presence of microvascular invasion (MVI) significantly impairs postoperative long-term survival of patients with hepatocellular carcinoma (HCC). The role of neoadjuvant radiotherapy (RT) in treating patients with an early-stage HCC predicted to have high risks of MVI remains to be explored.

MATERIALS AND METHODS

Consecutive patients with a resectable single and small (≤5 cm) hepatitis B virus-related HCC predicted to have high risks of MVI were randomized 1:1 to receive either neoadjuvant intensity modulated radiation therapy (18 Gy with fractionated doses of 3 Gy) followed by surgery 4 weeks later or upfront surgery. The primary endpoint was disease-free survival (DFS). The secondary outcomes included overall survival (OS), objective response rate, RT-related toxicity and surgical complications.

RESULTS

There were 30 patients randomized to each of the two groups. In the neoadjuvant RT group, three patients violated the study protocol, with two having upfront hepatectomy and one radiofrequency ablation after RT. The objective response rate after RT was 25.0% (7/28), but 2 patients suffered from grade 3 liver toxicity. The median follow-up was 68 months (interquartile range, 58-70 months) in the neoadjuvant RT group, and 68 months (interquartile range, 62-75 months) in the upfront surgery group. On intention-to-treat analysis, the median DFS and median OS were not reached in both the 2 arms. The 1-year, 2-year, 3-year and 5-year DFS rates for the neoadjuvant RT group were 86.7%, 76.7%, 60.0% and 56.3%, versus 90.0%, 66.7%, 52.8% and 45.7% in the upfront surgery group ( P =0.448), respectively. The corresponding OS rates were 96.7%, 86.7%, 83.3% and 72.7%, versus 100.0%, 93.3%, 79.6% and 60.7% ( P = 0.399).

CONCLUSION AND RELEVANCE

For patients with a resectable single and small hepatitis B virus-related HCC predicted to have high risks of MVI, neoadjuvant RT gave a promising response rate with a mild toxicity. Nevertheless, the neoadjuvant RT yielded similar long-term DFS and OS rates compared with patients who underwent upfront surgery.

Predictive value of radiomics analysis of enhanced CT for three-tiered microvascular invasion grading in hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) is a major risk factor, for recurrence and metastasis of hepatocellular carcinoma (HCC) after radical surgery and liver transplantation. However, its diagnosis depends on the pathological examination of the resected specimen after surgery; therefore, predicting MVI before surgery is necessary to provide reference value for clinical treatment. Meanwhile, predicting only the existence of MVI is not enough, as it ignores the degree, quantity, and distribution of MVI and may lead to MVI-positive patients suffering due to inappropriate treatment. Although some studies have involved M2 (high risk of MVI), majority have adopted the binary classification method or have not included radiomics.

PURPOSE

To develop three-class classification models for predicting the grade of MVI of HCC by combining enhanced computed tomography radiomics features with clinical risk factors.

METHODS

The data of 166 patients with HCC confirmed by surgery and pathology were analyzed retrospectively. The patients were divided into the training (116 cases) and test (50 cases) groups at a ratio of 7:3. Of them, 69 cases were MVI positive in the training group, including 45 cases in the low-risk group (M1) and 24 cases in the high-risk group (M2), and 47 cases were MVI negative (M0). In the training group, the optimal subset features were obtained through feature selection, and the arterial phase radiomics model, portal venous phase radiomics model, delayed phase radiomics model, three-phase radiomics model, clinical imaging model, and combined model were developed using Linear Support Vector Classification. The test group was used for validation, and the efficacy of each model was evaluated through the receiver operating characteristic curve (ROC).

RESULTS

The clinical imaging features of MVI included alpha-fetoprotein, tumor size, tumor margin, peritumoral enhancement, intratumoral artery, and low-density halo. The area under the curve (AUC) of the ROC values of the clinical imaging model for M0, M1, and M2 were 0.831, 0.701, and 0.847, respectively, in the training group and 0.782, 0.534, and 0.785, respectively, in the test group. After combined radiomics analyis, the AUC values for M0, M1, and M2 in the test group were 0.818, 0.688, and 0.867, respectively. The difference between the clinical imaging model and the combined model was statistically significant (p = 0.029).

CONCLUSION

The clinical imaging model and radiomics model developed in this study had a specific predictive value for HCC MVI grading, which can provide precise reference value for preoperative clinical diagnosis and treatment. The combined application of the two models had a high predictive efficacy.

The Feasibility of Using Tri-Exponential Intra-Voxel Incoherent Motion DWI for Identifying the Microvascular Invasion in Hepatocellular Carcinoma

Purpose

To assess the effectiveness of tri-exponential Intra-Voxel Incoherent Motion (tri-IVIM) MRI in preoperatively identifying microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Patients and Methods

In this prospective study, 67 patients with HCC were included. Metrics from bi-exponential IVIM (bi-IVIM) and tri-IVIM were calculated. Subgroup comparisons were analyzed using the independent Student's t-test or Mann-Whitney U-test. Logistic regression was performed to explore clinical risk factors. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis.

Results

MVI-positive HCCs exhibited significantly lower true diffusion coefficient (Dt) from bi-IVIM, as well as fast-diffusion coefficients (Df) and slow-diffusion coefficients (Ds) from tri-IVIM, compared to MVI-negative HCCs (p < 0.05). Tumor size and alpha-fetoprotein (AFP) were identified as risk factors. The combination of tri-IVIM-derived metrics (Ds and Df) yielded higher diagnostic accuracy (AUC = 0.808) compared to bi-IVIM (AUC = 0.741). A predictive model based on a nomogram was constructed using Ds, Df, tumor size, and AFP, resulting in the highest diagnostic accuracy (AUC = 0.859). Decision curve analysis indicated that the constructed model, provided the highest net benefit by accurately stratifying the risk of MVI, followed by tri-IVIM and bi-IVIM.

Conclusion

Tri-IVIM can provide information on perfusion and diffusion for evaluating MVI in HCC. Additionally, tri-IVIM outperformed bi-IVIM in identifying MVI-positive HCC. By integrating clinical risk factors and metrics from tri-IVIM, a predictive nomogram exhibited the highest diagnostic accuracy, potentially aiding in the noninvasive and preoperative assessment of MVI.

MRI Features for Predicting Microvascular Invasion and Postoperative Recurrence in Hepatocellular Carcinoma Without Peritumoral Hypointensity

Purpose

To identify MRI features of hepatocellular carcinoma (HCC) that predict microvascular invasion (MVI) and postoperative intrahepatic recurrence in patients without peritumoral hepatobiliary phase (HBP) hypointensity.

Patients and Methods

One hundred and thirty patients with HCC who underwent preoperative gadoxetate-enhanced MRI and curative hepatic resection were retrospectively reviewed. Two radiologists reviewed all preoperative MR images and assessed the radiological features of HCCs. The ability of peritumoral HBP hypointensity to identify MVI and intrahepatic recurrence was analyzed. We then assessed the MRI features of HCC that predicted the MVI and intrahepatic recurrence-free survival (RFS) in the subgroup without peritumoral HBP hypointensity. Finally, a two-step flowchart was constructed to assist in clinical decision-making.

Results

Peritumoral HBP hypointensity (odds ratio, 3.019; 95% confidence interval: 1.071-8.512; P=0.037) was an independent predictor of MVI. The sensitivity, specificity, positive predictive value, negative predictive value, and AUROC of peritumoral HBP hypointensity in predicting MVI were 23.80%, 91.04%, 71.23%, 55.96%, and 0.574, respectively. Intrahepatic RFS was significantly shorter in patients with peritumoral HBP hypointensity (P<0.001). In patients without peritumoral HBP hypointensity, the only significant difference between MVI-positive and MVI-negative HCCs was the presence of a radiological capsule (P=0.038). Satellite nodule was an independent risk factor for intrahepatic RFS (hazard ratio,3.324; 95% CI: 1.733-6.378; P<0.001). The high-risk HCC detection rate was significantly higher when using the two-step flowchart that incorporated peritumoral HBP hypointensity and satellite nodule than when using peritumoral HBP hypointensity alone (P<0.001).

Conclusion

In patients without peritumoral HBP hypointensity, a radiological capsule is useful for identifying MVI and satellite nodule is an independent risk factor for intrahepatic RFS.

Effect of PIVKA-II and AFP secretion status on early recurrence of hepatocellular carcinoma after open and laparoscopic surgery

BACKGROUND

Prothrombin induced by vitamin K absence-II (PIVKA-II) and Alpha-fetoprotein (AFP) have been widely used as diagnostic markers in hepatocellular carcinoma (HCC), but the prognostic values of the two serum markers and their clinical usefulness in patient selection for different surgical approaches remain largely unclear.

METHODS

HCC patients received surgical treatment between 2015 and 2019 were included. Patients were divided into four statuses according to the serum PIVKA-II and AFP secretion status: PIVKA-II (-) AFP (-) (status 1); PIVKA-II (+) AFP (-) (status 2); PIVKA-II (-) AFP (+) (status 3); PIVKA-II (+) AFP (+) (status 4). Kaplan-Meier analyses were conducted to compare the survivals of the four groups and the HCC patients received different surgical interventions; time-dependent AUC curves were introduced to evaluate the prognostic value of the PIV-AFP status; Cox regression model was used to identify prognostic indexes for overall survival (OS) and recurrence-free survival (RFS).

RESULTS

A total of 518 patients were included. Patients with PIVKA-II (+) and APF (+) presented significantly decreased OS and RFS comparing to the other statuses. The areas under ROC curves of PIV-AFP status in predicting OS and RFS were superior to the PIVKA-II or the AFP alone. The HCC patients in early stages with PIVKA-II (+) and APF (+) had worse RFS when received laparoscopic hepatectomy than those who received open hepatectomy, whereas there was no difference in other secretion statuses. The PIVKA-II (+) and AFP (+) secretion status was an independent risk factor for OS, RFS.

CONCLUSIONS

The PIV-AFP secretion status is of favorable clinical utility in predicting the OS and RFS of the HCC patients; extra caution is needed when applicated the laparoscopic approach in the HCC patients with PIVKA-II (+) and AFP (+).

Prediction of Microvascular Invasion in Solitary AFP-Negative Hepatocellular Carcinoma ≤ 5 cm Using a Combination of Imaging Features and Quantitative Dual-Layer Spectral-Detector CT Parameters

RATIONALE AND OBJECTIVES

AFP-negative hepatocellular carcinoma (AFPN-HCC) within 5 cm is a special subgroup of HCC. This study aimed to investigate the value of dual-layer spectral-detector CT (DLCT) and construct a scoring model based on imaging features as well as DLCT for predicting microvascular invasion (MVI) in AFPN-HCC within 5 cm.

METHODS

This retrospective study enrolled 104 HCC patients who underwent multiphase contrast-enhanced DLCT studies preoperatively. Combined radiological features (CR) and combined DLCT quantitative parameter (CDLCT) were constructed to predict MVI. Multivariable logistic regression was applied to identify potential predictors of MVI. Based on the coefficient of the regression model, a scoring model was developed. The predictive efficacy was assessed through ROC analysis.

RESULTS

Microvascular invasion (MVI) was found in 28 (26.9%) AFPN-HCC patients. Among single parameters, the effective atomic number in arterial phase demonstrated the best predictive efficiency for MVI with an area under the curve (AUC) of 0.792. CR and CDLCT showed predictive performance with AUCs of 0.848 and 0.849, respectively. A risk score (RS) was calculated using the independent predictors of MVI as follows: RS = 2 × (mosaic architecture) + 2 × (corona enhancement) + 2 × (incomplete tumor capsule) + 2 × (2-trait predictor of venous invasion [TTPVI]) + 3 × (CDLCT > -1.229). Delong's test demonstrated this scoring system could significantly improve the AUC to 0.929 compared with CR (p = 0.016) and CDLCT (p = 0.034).

CONCLUSION

The scoring model combining radiological features with DLCT provides a promising tool for predicting MVI in solitary AFPN-HCC within 5 cm preoperatively.

Radiomic Analysis of Intrahepatic Cholangiocarcinoma: Non-Invasive Prediction of Pathology Data: A Multicenter Study to Develop a Clinical-Radiomic Model

Standard imaging cannot assess the pathology details of intrahepatic cholangiocarcinoma (ICC). We investigated whether CT-based radiomics may improve the prediction of tumor characteristics. All consecutive patients undergoing liver resection for ICC (2009-2019) in six high-volume centers were evaluated for inclusion. On the preoperative CT, we segmented the ICC (Tumor-VOI, i.e., volume-of-interest) and a 5-mm parenchyma rim around the tumor (Margin-VOI). We considered two types of pathology data: tumor grading (G) and microvascular invasion (MVI). The predictive models were internally validated. Overall, 244 patients were analyzed: 82 (34%) had G3 tumors and 139 (57%) had MVI. For G3 prediction, the clinical model had an AUC = 0.69 and an Accuracy = 0.68 at internal cross-validation. The addition of radiomic features extracted from the portal phase of CT improved the model performance (Clinical data+Tumor-VOI: AUC = 0.73/Accuracy = 0.72; +Tumor-/Margin-VOI: AUC = 0.77/Accuracy = 0.77). Also for MVI prediction, the addition of portal phase radiomics improved the model performance (Clinical data: AUC = 0.75/Accuracy = 0.70; +Tumor-VOI: AUC = 0.82/Accuracy = 0.73; +Tumor-/Margin-VOI: AUC = 0.82/Accuracy = 0.75). The permutation tests confirmed that a combined clinical-radiomic model outperforms a purely clinical one (p < 0.05). The addition of the textural features extracted from the arterial phase had no impact. In conclusion, the radiomic features of the tumor and peritumoral tissue extracted from the portal phase of preoperative CT improve the prediction of ICC grading and MVI.

Clinical Interpretability of Deep Learning for Predicting Microvascular Invasion in Hepatocellular Carcinoma by Using Attention Mechanism

Preoperative prediction of microvascular invasion (MVI) is essential for management decision in hepatocellular carcinoma (HCC). Deep learning-based prediction models of MVI are numerous but lack clinical interpretation due to their "black-box" nature. Consequently, we aimed to use an attention-guided feature fusion network, including intra- and inter-attention modules, to solve this problem. This retrospective study recruited 210 HCC patients who underwent gadoxetate-enhanced MRI examination before surgery. The MRIs on pre-contrast, arterial, portal, and hepatobiliary phases (hepatobiliary phase: HBP) were used to develop single-phase and multi-phase models. Attention weights provided by attention modules were used to obtain visual explanations of predictive decisions. The four-phase fusion model achieved the highest area under the curve (AUC) of 0.92 (95% CI: 0.84-1.00), and the other models proposed AUCs of 0.75-0.91. Attention heatmaps of collaborative-attention layers revealed that tumor margins in all phases and peritumoral areas in the arterial phase and HBP were salient regions for MVI prediction. Heatmaps of weights in fully connected layers showed that the HBP contributed the most to MVI prediction. Our study firstly implemented self-attention and collaborative-attention to reveal the relationship between deep features and MVI, improving the clinical interpretation of prediction models. The clinical interpretability offers radiologists and clinicians more confidence to apply deep learning models in clinical practice, helping HCC patients formulate personalized therapies.

Validation of quantitative prognostic prediction using ADV score for resection of hepatocellular carcinoma: A Korea-Japan collaborative study with 9200 patients

BACKGROUND

A score derived from the concentrations of α-fetoprotein (AFP) and des-γ-carboxy prothrombin (DCP) and tumor volume (TV), called ADV score, has been shown to be prognostic of hepatocellular carcinoma (HCC) recurrence following hepatic resection (HR) or liver transplantation.

METHODS

This multicenter, multinational validation study included 9200 patients who underwent HR from 2010 to 2017 at 10 Korean and 73 Japanese centers, and were followed up until 2020.

RESULTS

AFP, DCP, and TV showed weak correlations (ρ ≤ .463, r ≤ .189, p < .001). Disease-free survival (DFS), overall survival (OS), and post-recurrence survival rates were dependent on 1.0 log and 2.0 log intervals of ADV scores (p < .001). Receiver operating characteristic (ROC) curve analysis showed that ADV score cutoffs of 5.0 log for DFS and OS yielded the areas under the curve ≥ .577, with both being significantly prognostic of tumor recurrence and patient mortality at 3 years. ADV score cutoffs of ADV 4.0 log and 8.0 log, derived through K-adaptive partitioning method, showed higher prognostic contrasts in DFS and OS. ROC curve analysis showed that an ADV score cutoff of 4.2 log was suggestive of microvascular invasion, with both microvascular invasion and an ADV score cutoff of 4.2 log showing similar DFS rates.

CONCLUSIONS

This international validation study demonstrated that ADV score is an integrated surrogate biomarker for post-resection prognosis of HCC. Prognostic prediction using ADV score can provide reliable information that can assist in planning treatment of patients with different stages of HCC and guide individualized post-resection follow-up based on the relative risk of HCC recurrence.

Modality-based attention and dual-stream multiple instance convolutional neural network for predicting microvascular invasion of hepatocellular carcinoma

Background and purpose

The presence of microvascular invasion (MVI) is a crucial indicator of postoperative recurrence in patients with hepatocellular carcinoma (HCC). Detecting MVI before surgery can improve personalized surgical planning and enhance patient survival. However, existing automatic diagnosis methods for MVI have certain limitations. Some methods only analyze information from a single slice and overlook the context of the entire lesion, while others require high computational resources to process the entire tumor with a three-dimension (3D) convolutional neural network (CNN), which could be challenging to train. To address these limitations, this paper proposes a modality-based attention and dual-stream multiple instance learning(MIL) CNN.

Materials and methods

In this retrospective study, 283 patients with histologically confirmed HCC who underwent surgical resection between April 2017 and September 2019 were included. Five magnetic resonance (MR) modalities including T2-weighted, arterial phase, venous phase, delay phase and apparent diffusion coefficient images were used in image acquisition of each patient. Firstly, Each two-dimension (2D) slice of HCC magnetic resonance image (MRI) was converted into an instance embedding. Secondly, modality attention module was designed to emulates the decision-making process of doctors and helped the model to focus on the important MRI sequences. Thirdly, instance embeddings of 3D scans were aggregated into a bag embedding by a dual-stream MIL aggregator, in which the critical slices were given greater consideration. The dataset was split into a training set and a testing set in a 4:1 ratio, and model performance was evaluated using five-fold cross-validation.

Results

Using the proposed method, the prediction of MVI achieved an accuracy of 76.43% and an AUC of 74.22%, significantly surpassing the performance of the baseline methods.

Conclusion

Our modality-based attention and dual-stream MIL CNN can achieve outstanding results for MVI prediction.

Liver resection versus liver transplantation for hepatocellular carcinoma within the Milan criteria based on estimated microvascular invasion risks

Background

Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) may optimize individualized treatment decision-making. This study aimed to investigate the prognostic differences between HCC patients undergoing liver resection (LR) and liver transplantation (LT) based on predicted MVI risks.

Methods

We analysed 905 patients who underwent LR, including 524 who underwent anatomical resection (AR) and 117 who underwent LT for HCC within the Milan criteria using propensity score matching. A nomogram model was used to predict preoperative MVI risk.

Results

The concordance indices of the nomogram for predicting MVI were 0.809 and 0.838 in patients undergoing LR and LT, respectively. Based on an optimal cut-off value of 200 points, the nomogram defined patients as high- or low-risk MVI groups. LT resulted in a lower 5-year recurrence rate and higher 5-year overall survival (OS) rate than LR among the high-risk patients (23.6% vs 73.2%, P < 0.001; 87.8% vs 48.1%, P < 0.001) and low-risk patients (19.0% vs 45.7%, P < 0.001; 86.5% vs 70.0%, P = 0.002). The hazard ratios (HRs) of LT vs LR for recurrence and OS were 0.18 (95% confidence interval [CI], 0.09-0.37) and 0.12 (95% CI, 0.04-0.37) among the high-risk patients and 0.37 (95% CI, 0.21-0.66) and 0.36 (95% CI, 0.17-0.78) among the low-risk patients. LT also provided a lower 5-year recurrence rate and higher 5-year OS rate than AR among the high-risk patients (24.8% vs 63.5%, P = 0.001; 86.7% vs 65.7%, P = 0.004), with HRs of LT vs AR for recurrence and OS being 0.24 (95% CI, 0.11-0.53) and 0.17 (95% CI, 0.06-0.52), respectively. The 5-year recurrence and OS rates between patients undergoing LT and AR were not significantly different in the low-risk patients (19.4% vs 28.3%, P = 0.129; 85.7% vs 77.8%, P = 0.161).

Conclusions

LT was superior to LR for patients with HCC within the Milan criteria with a predicted high or low risk of MVI. No significant differences in prognosis were found between LT and AR in patients with a low risk of MVI.

[Prediction of microvascular invasion in hepatocellular carcinoma with magnetic resonance imaging using models combining deep attention mechanism with clinical features]

OBJECTIVE

To investigate the consistency and diagnostic performance of magnetic resonance imaging (MRI) for detecting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and the validity of deep learning attention mechanisms and clinical features for MVI grade prediction.

METHODS

This retrospective study was conducted among 158 patients with HCC treated in Shunde Hospital Affiliated to Southern Medical University between January, 2017 and February, 2020. The imaging data and clinical data of the patients were collected to establish single sequence deep learning models and fusion models based on the EfficientNetB0 and attention modules. The imaging data included conventional MRI sequences (T₁WI, T₂WI, and DWI), enhanced MRI sequences (AP, PP, EP, and HBP) and synthesized MRI sequences (T1mapping-pre and T1mapping-20 min), and the high-risk areas of MVI were visualized using deep learning visualization techniques.

RESULTS

The fusion model based on T1mapping-20min sequence and clinical features outperformed other fusion models with an accuracy of 0.8376, a sensitivity of 0.8378, a specificity of 0.8702, and an AUC of 0.8501 for detecting MVI. The deep fusion models were also capable of displaying the high-risk areas of MVI.

CONCLUSION

The fusion models based on multiple MRI sequences can effectively detect MVI in patients with HCC, demonstrating the validity of deep learning algorithm that combines attention mechanism and clinical features for MVI grade prediction.

Prediction microvascular invasion of hepatocellular carcinoma based on tumour margin enhancing pattern in multiphase computed tomography images

Purpose

The presence of microvascular invasion of hepatocellular carcinoma has a significantly decreased outcome following hepatectomy or liver transplantation. Currently, it is still based on histological examination. Identification of microvascular invasion by using pre-operative imaging is important for the decision-making of surgeons and interventional radiologists. Aim of the study was to predict the microvascular invasion of hepatocellular carcinoma based on tumour margin enhancement of pre-operative multiphase computed tomography (CT) images.

Material and methods

Fifty-three patients with hepatocellular carcinoma, who underwent pre-operative multiphase CT scans, were included in this study. Tumour margin enhancing patterns were analysed in the late arterial phase, portovenous phase, and delay phase. The CT features including peritumoral enhancement, arterial rim-enhancement, presence of daughter nodules, complete capsule enhancement in portovenous/delay phase, and nodular capsule enhancement in portovenous/delay phase were reviewed with calculations for sensitivity and specificity. Univariate analysis and multivariate analysis were used to identify predictive features for microvascular invasion (MVI).

Results

In the late arterial phase, peritumoral enhancement or the presence of daughter nodules were not predictors for MVI. Nodular capsule enhancement in the portovenous phase and delay phase were independent predictors for MVI with odds ratios of 29.25 and 33.09, respectively. The sensitivity and specificity for incomplete/nodular capsule enhancement in the portovenous phase were 69.23% and 96.86%, respectively. The sensitivity and specificity for incomplete/nodular capsule enhancement in the delay phase were 71.79% and 96.86%, respectively.

Conclusion

Nodular capsule enhancement in the portovenous phase or delay phase was a good predictor for MVI.

Association of severity in the grading of microvascular invasion with long-term oncological prognosis after liver resection for early-stage hepatocellular carcinoma: a multicenter retrospective cohort study from a hepatitis B virus-endemic area

BACKGROUND

The presence of microvascular invasion (MVI) is a significant malignant pathological feature related to recurrence and survival after liver resection for hepatocellular carcinoma (HCC). This study aimed to investigate the relationship between the severity in the grading of MVI and long-term oncological outcomes in patients with early-stage HCC.

METHODS

A retrospective study was conducted on a prospectively maintained multicenter database on patients who underwent curative resection for Barcelona Clinic Liver Cancer stage 0/A HCC between 2017 and 2020. Patients were classified into three groups according to the severity in the grading of MVI: M0 (no MVI), M1 (1-5 sites of MVI occurring ≤1 cm away from the tumor), and M2 (>5 sites occurring ≤1 cm and/or any site occurring >1 cm away from the tumor). Recurrence-free survival (RFS) and overall survival (OS) were compared among the groups.

RESULTS

Of 388 patients, M0, M1, and M2 of the MVI gradings were present in 223 (57.5%), 118 (30.4%), and 47 (12.1%) patients, respectively. The median OS and RFS in patients with M0, M1, and M2 were 61.1, 52.7, and 27.4 months; and 43.0, 29.1, and 13.1 months (both P <0.001), respectively. Multivariable analyses identified both M1 and M2 to be independent risk factors for OS [hazard ratio (HR): 1.682, P =0.003; and HR: 3.570, P <0.001] and RFS (HR: 1.550, P =0.037; and HR: 2.256, P <0.001).

CONCLUSION

The severity in the grading of MVI was independently associated with recurrence and survival after HCC resection. Patients with the presence of MVI, especially those with a more severe MVI grading (M2), require more stringent recurrence surveillance and/or active adjuvant therapy against recurrence.

Significance of anatomical resection and resection margin status in patients with HBV-related hepatocellular carcinoma and microvascular invasion: a multicenter propensity score-matched study

BACKGROUND

Microvascular invasion (MVI) is a risk factor for postoperative survival outcomes for patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to evaluate the impact of anatomical resection (AR) versus nonanatomical resection (NAR) combined with resection margin (RM) (narrow RM <1 cm vs. wide RM ≥1 cm) on long-term prognosis in hepatitis B virus-related HCC patients with MVI.

MATERIALS AND METHODS

Data from multicenters on HCC patients with MVI who underwent hepatectomy was analyzed retrospectively. Propensity score matching analysis was performed in these patients.

RESULTS

The 1965 enrolled patients were divided into four groups: AR with wide RM ( n =715), AR with narrow RM ( n =387), NAR with wide RM ( n =568), and NAR with narrow RM ( n =295). Narrow RM ( P <0.001) and NAR ( P <0.001) were independent risk factors for both overall survival and recurrence-free survival in these patients based on multivariate analyses. For patients in both the AR and NAR groups, wide RM resulted in significantly lower operative margin recurrence rates than those patients in the narrow RM groups after propensity score matching ( P =0.002 and 0.001). Patients in the AR with wide RM group had significantly the best median overall survival (78.9 vs. 51.5 vs. 48.0 vs. 36.7 months, P <0.001) and recurrence-free survival (23.6 vs. 14.8 vs. 17.8 vs. 9.0 months, P <0.001) than those in the AR with narrow RM, NAR with wide RM or with narrow RM groups, respectively.

CONCLUSIONS

If technically feasible and safe, AR combined with wide RM should be the recommended therapeutic strategy for HCC patients who are estimated preoperatively with a high risk of MVI.

Postoperative adjuvant transarterial chemoembolisation improves survival of hepatocellular carcinoma patients with microvascular invasion: A multicenter retrospective cohort

BACKGROUND

We aimed to investigate the efficacy of postoperative adjuvant transarterial chemoembolisation (PA-TACE) in patients with hepatocellular carcinoma (HCC) complicated by microvascular invasion (MVI).

METHODS

A retrospective analysis of 1505 patients with HCC who underwent hepatectomy at four medical centers, including 782 patients who received PA-TACE and 723 patients who did not receive adjuvant PA-TACE, has been conducted. Propensity score matching (PSM) (1:1) was performed on the data to minimise selection bias, which resulted in a balanced clinical profile between groups.

RESULTS

After PSM, 620 patients who received PA-TACE and 620 patients who did not receive PA-TACE were included. Disease-free survival (DFS, 1-, 2-, and 3-year: 88%-68%-61% vs. 70%-58%-51%, p < 0.001) and overall survival (OS, 1-, 2-, and 3-year: 96%-89%-82% vs. 89%-77%-67%, p < 0.001) were significantly higher in patients who received PA-TACE than in those who did not. Patients with MVI who received PA-TACE had significantly higher DFS (1-, 2-, and 3-year: 68%-57%-48% vs. 46%-31%-27%, p < 0.001) and OS (1-, 2-, and 3-year: 96%-84%-77% vs. 79%-58%-40%, p < 0.001) than those who did not receive PA-TACE. Among the six different liver cancer stages, MVI-negative patients did not have significant survival outcomes from PA-TACE (p > 0.05), whereas MVI-positive patients achieved higher DFS and OS from it (p < 0.05). Liver dysfunction, fever, and nausea/vomiting were the most common adverse events in patients receiving PA-TACE. There was no significant difference in grade 3 or 4 adverse events between the groups (p > 0.05).

CONCLUSIONS

Postoperative adjuvant transarterial chemoembolisation has a good safety profile and may be a potentially beneficial treatment modality for survival outcomes in patients with HCC, especially those with concomitant MVI.

Combined serum CXCL8, CXCL9 and CXCL13 tests for the prediction of microvascular invasion in hepatocellular carcinoma

Aim: This work is to explore the predictive and diagnostic value of chemokine C-X-C motif ligand 8 (CXCL8), CXCL9 and CXCL13 combined detections for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Materials & methods: A total of 82 HCC patients with MVI were recruited as the MVI group and 154 patients with non MVI were recruited as the non MVI group. Results: In HCC patients with MVI, CXCL8, CXCL9, CXCL13 levels were significantly elevated. Child-Pugh scores and serum α-fetoprotein level had positive correlation with CXCL8, CXCL9 and CXCL13 levels. The serum levels of CXCL8, 9 and 13 were effective in predicting MVI in HCC patients. Conclusion: CXCL8, CXCL9 and CXCL13 levels in HCC patients are valuable parameters in the prediction of MVI.

Prognostic significance of three-tiered pathological classification for microvascular invasion in patients with combined hepatocellular-cholangiocarcinoma following hepatic resection

BACKGROUND AND OBJECTIVES

Previous studies have reported that the microvascular invasion three-tiered grading (MiVI-TTG) scheme is a better prognostic predictor than the two-tiered microvascular invasion (MiVI) grading scheme in hepatocellular carcinoma. This study aims to explore the prognostic significance of MiVI-TTG in patients undergoing liver resection for combined hepatocellular-cholangiocarcinoma (cHCC) and to explore the risk factors for MiVI in cHCC.

METHODS

This research included 208 patients graded as M0, M1, or M2 using the MiVI-TTG scheme. Predictive performance was assessed by Cox regression analysis, Kaplan-Meier curve with Log rank test, Harrell's c-index, and time-dependent areas under the receiver operating characteristic curve (tdAUC). The clinical utility of the two schemes was evaluated by decision cure analysis (DCA). The risk factors for MiVI were evaluated using logistic regression analysis.

RESULTS

Among 208 cHCC patients, the proportions of M0, M1 and M2 were 38.9%, 36.5%, and 24.5%, respectively. Patients with severe MiVI status had worse recurrence-free survival and overall survival (OS) based on Kaplan-Meier analysis. M1, M2, and MiVI-positive were independent risk factors for early recurrence, while M2 and MiVI-positive were associated with overall survival (OS). MiVI-TTG had a larger c-index, tdAUC, and net benefit rate than the two-tiered MiVI grading scheme for predicting recurrence free survival and OS. AFP≥400 ng/ml was the independent risk factor for MiVI, and satellite nodules were independent risk factors for M2.

CONCLUSIONS

MiVI-TTG has a greater prognostic value than the two-tiered MiVI grading scheme in patients undergoing hepatic resection for cHCC.

MVI-TR: A Transformer-Based Deep Learning Model with Contrast-Enhanced CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma

In this study, we considered preoperative prediction of microvascular invasion (MVI) status with deep learning (DL) models for patients with early-stage hepatocellular carcinoma (HCC) (tumor size ≤ 5 cm). Two types of DL models based only on venous phase (VP) of contrast-enhanced computed tomography (CECT) were constructed and validated. From our hospital (First Affiliated Hospital of Zhejiang University, Zhejiang, P.R. China), 559 patients, who had histopathological confirmed MVI status, participated in this study. All preoperative CECT were collected, and the patients were randomly divided into training and validation cohorts at a ratio of 4:1. We proposed a novel transformer-based end-to-end DL model, named MVI-TR, which is a supervised learning method. MVI-TR can capture features automatically from radiomics and perform MVI preoperative assessments. In addition, a popular self-supervised learning method, the contrastive learning model, and the widely used residual networks (ResNets family) were constructed for fair comparisons. With an accuracy of 99.1%, a precision of 99.3%, an area under the curve (AUC) of 0.98, a recalling rate of 98.8%, and an F1-score of 99.1% in the training cohort, MVI-TR achieved superior outcomes. Additionally, the validation cohort's MVI status prediction had the best accuracy (97.2%), precision (97.3%), AUC (0.935), recalling rate (93.1%), and F1-score (95.2%). MVI-TR outperformed other models for predicting MVI status, and showed great preoperative predictive value for early-stage HCC patients.

Prediction of microvascular invasion in hepatocellular carcinoma based on preoperative Gd-EOB-DTPA-enhanced MRI: Comparison of predictive performance among 2D, 2D-expansion and 3D deep learning models

Purpose

To evaluate and compare the predictive performance of different deep learning models using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma.

Methods

The data of 233 patients with pathologically confirmed hepatocellular carcinoma (HCC) treated at our hospital from June 2016 to June 2021 were retrospectively analyzed. Three deep learning models were constructed based on three different delineate methods of the region of interest (ROI) using the Darwin Scientific Research Platform (Beijing Yizhun Intelligent Technology Co., Ltd., China). Manual segmentation of ROI was performed on the T1-weighted axial Hepatobiliary phase images. According to the ratio of 7:3, the samples were divided into a training set (N=163) and a validation set (N=70). The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of three models, and their sensitivity, specificity and accuracy were assessed.

Results

Among 233 HCC patients, 109 were pathologically MVI positive, including 91 men and 18 women, with an average age of 58.20 ± 10.17 years; 124 patients were MVI negative, including 93 men and 31 women, with an average age of 58.26 ± 10.20 years. Among three deep learning models, 2D-expansion-DL model and 3D-DL model showed relatively good performance, the AUC value were 0.70 (P=0.003) (95% CI 0.57-0.82) and 0.72 (P<0.001) (95% CI 0.60-0.84), respectively. In the 2D-expansion-DL model, the accuracy, sensitivity and specificity were 0.7143, 0.739 and 0.688. In the 3D-DL model, the accuracy, sensitivity and specificity were 0.6714, 0.800 and 0.575, respectively. Compared with the 3D-DL model (based on 3D-ResNet), the 2D-DL model is smaller in scale and runs faster. The frames per second (FPS) for the 2D-DL model is 244.7566, which is much larger than that of the 3D-DL model (73.3374).

Conclusion

The deep learning model based on Gd-EOB-DTPA-enhanced MRI could preoperatively evaluate MVI in HCC. Considering that the predictive performance of 2D-expansion-DL model was almost the same as the 3D-DL model and the former was relatively easy to implement, we prefer the 2D-expansion-DL model in practical research.

The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis

Objective

The aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Method

The databases of PubMed, Cochrane library, Embase, Web of Science, Ovid MEDLINE, Springer, and Science Direct were searched for original studies from their inception to 20 August 2022. The quality of each study included was assessed according to the Quality Assessment of Diagnostic Accuracy Studies 2 and the radiomics quality score. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was plotted and the area under the curve (AUC) was calculated to evaluate the diagnostic accuracy. Sensitivity analysis and subgroup analysis were performed to explore the source of the heterogeneity. Deeks' test was used to assess publication bias.

Results

A total of 15 studies involving 981 patients were included. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.79 (95%CI: 0.72-0.85), 0.81 (95%CI: 0.73-0.87), 4.1 (95%CI:2.9-5.9), 0.26 (95%CI: 0.19-0.35), 16 (95%CI: 9-28), and 0.87 (95%CI: 0.84-0.89), respectively. The results showed great heterogeneity among the included studies. Sensitivity analysis indicated that the results of this study were statistically reliable. The results of subgroup analysis showed that hepatocyte-specific contrast media (HSCM) had equivalent sensitivity and equivalent specificity compared to the other set. The least absolute shrinkage and selection operator method had high sensitivity and specificity than other methods, respectively. The investigated area of the region of interest had high specificity compared to the volume of interest. The imaging-to-surgery interval of 15 days had higher sensitivity and slightly low specificity than the others. Deeks' test indicates that there was no publication bias (P=0.71).

Conclusion

Radiomics-based MRI has high accuracy in predicting MVI in HCC, and it can be considered as a non-invasive method for assessing MVI in HCC.

Combining Preoperative Clinical and Imaging Characteristics to Predict MVI in Hepatitis B Virus-Related Combined Hepatocellular Carcinoma and Cholangiocarcinoma

BACKGROUND

Combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA) is a rare form of primary liver malignancy. Microvascular invasion (MVI) indicates poor postsurgical prognosis in cHCC-CCA. The objective of this study was to investigate preoperative predictors of MVI in hepatitis B virus (HBV) -related cHCC-CCA patients.

METHODS

A total of 69 HBV-infected patients with pathologically confirmed cHCC-CCA who underwent hepatectomy were included. Univariate and multivariate analyses were conducted to determine independent risk factors that were then incorporated into the predictive model associated with MVI. Receiver operating characteristic analysis was used to assess the predictive performance of the new model.

RESULTS

For the multivariate analysis, γ-glutamyl transpeptidase (OR, 3.69; p = 0.034), multiple nodules (OR, 4.41; p = 0.042) and peritumoral enhancement (OR, 6.16; p = 0.004) were independently associated with MVI. Active replication of HBV indicated by positive HBeAg showed no differences between MVI-positive and MVI-negative patients. The prediction score using the independent predictors achieved an area under the curve of 0.813 (95% CI 0.717-0.908). A significantly lower recurrence-free survival was observed in the high-risk group with a score of ≥1 (p < 0.001).

CONCLUSION

γ-glutamyl transpeptidase, peritumoral enhancement and multiple nodules were independent preoperative predictors of MVI in HBV-related cHCC-CCA patients. The established prediction score demonstrated satisfactory performance in predicting MVI pre-operatively and may facilitate prognostic stratification.

New insights into a microvascular invasion prediction model in hepatocellular carcinoma: A retrospective study from the SEER database and China

Background and Aims

The prognosis of liver cancer is strongly influenced by microvascular infiltration (MVI). Accurate preoperative MVI prediction can aid clinicians in the selection of suitable treatment options. In this study, we constructed a novel, reliable, and adaptable nomogram for predicting MVI.

Methods

Using the Surveillance, Epidemiology, and End Results (SEER) database, we extracted the clinical data of 1,063 patients diagnosed with hepatocellular carcinoma (HCC) and divided it into either a training (n = 739) or an internal validation cohort (n = 326). Based on multivariate analysis, the training cohort data were analyzed and a nomogram was generated for MVI prediction. This was further verified using an internal validation cohort and an external validation cohort involving 293 Chinese patients. Furthermore, to evaluate the efficacy, accuracy, and clinical use of the nomogram, we used concordance index (C-index), calibration curve, and decision curve analysis (DCA) techniques.

Results

In accordance with the multivariate analysis, tumor size, tumor number, alpha-fetoprotein (AFP), and histological grade were independently associated with MVI. The established model exhibited satisfactory performance in predicting MVI. The C-indices were 0.719, 0.704, and 0.718 in the training, internal validation, and external validation cohorts, respectively. The calibration curves showed an excellent consistency between the predictions and actual observations. Finally, DCA demonstrated that the newly developed nomogram had favorable clinical utility.

Conclusions

We established and verified a novel preoperative MVI prediction model in HCC patients. This model can be a beneficial tool for clinicians in selecting an optimal treatment plan for HCC patients.

A Clinical Tool to Predict the Microvascular Invasion Risk in Patients with Hepatocellular Carcinoma

BACKGROUND

Microvascular invasion (MVI) plays an important role in tumor progression. The aim of this study is to establish and validate an effective hematological nomogram for MVI prediction in hepatocellular carcinoma (HCC).

METHODS

A retrospective study was performed in a primary cohort that includes 1306 patients clinicopathologically diagnosed with HCC, and a validation cohort contained 563 continuous patients. Univariate logistic regression was used to assess the association between variables included both clinicopathologic factors and coagulation parameters (prothrombin time, activated partial thromboplastin time, fibrinogen, and thrombin time [TT]) and MVI. Multiple logistic regression was used to construct a prediction nomogram. We tested the accuracy of the nomogram by discrimination and calibration, and then plotted decision curves to assess the benefits of the nomogram-assisted decisions in a clinical context.

RESULTS

In the two cohorts, patients without MVI had the longest overall survival (OS), compared the OS with MVI. The multivariate analysis indicated that age, sex, tumor node metastasis (TNM) stage, aspartate aminotransferase, alpha fetoprotein, C-reactive protein, and TT were identified as significant independent predictors of MVI of HCC patients. The Hosmer-Lemeshow test showed good point estimate associated P value between predicted risk and observed risk across the deciles. Moreover, the calibration performance of the nomogram risk scores in each decile of the primary cohort was within 5 percentage points of the mean predicted risk score, and in the validation cohort, the observed risk in 90% decile was within 5 percentage points of the mean predicted risk score.

CONCLUSIONS

A noninvasive and easy-to-use nomogram was established and may be used to predict preoperative MVI in HCC.