Preoperative prediction of microvascular invasion: new insights into personalized therapy for early-stage hepatocellular carcinoma.

Owing to advances in diagnosis and treatment methods over past decades, a growing number of early-stage hepatocellular carcinoma (HCC) diagnoses has enabled a greater of proportion of patients to receive curative treatment. However, a high risk of early recurrence and poor prognosis remain major challenges in HCC therapy. Microvascular invasion (MVI) has been demonstrated to be an essential independent predictor of early recurrence after curative therapy. Currently, biopsy is not generally recommended before treatment to evaluate MVI in HCC according clinical guidelines due to sampling error and the high risk of tumor cell seeding following biopsy. Therefore, the postoperative histopathological examination is recognized as the gold standard of MVI diagnosis, but this lagging indicator greatly impedes clinicians in selecting the optimal effective treatment for prognosis. As imaging can now noninvasively and completely assess the whole tumor and host situation, it is playing an increasingly important role in the preoperative assessment of MVI. Therefore, imaging criteria for MVI diagnosis would be highly desirable for optimizing individualized therapeutic decision-making and achieving a better prognosis. In this review, we summarize the emerging image characteristics of different imaging modalities for predicting MVI. We also discuss whether advances in imaging technique have generated evidence that could be practice-changing and whether advanced imaging techniques will revolutionize therapeutic decision-making of early-stage HCC.

Radiogenomics nomogram based on MRI and microRNAs to predict microvascular invasion of hepatocellular carcinoma.

Purpose: This study aimed to develop and validate a radiogenomics nomogram for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) on the basis of MRI and microRNAs (miRNAs). Materials and methods: This cohort study included 168 patients (training cohort: n = 116; validation cohort: n = 52) with pathologically confirmed HCC, who underwent preoperative MRI and plasma miRNA examination. Univariate and multivariate logistic regressions were used to identify independent risk factors associated with MVI. These risk factors were used to produce a nomogram. The performance of the nomogram was evaluated by receiver operating characteristic curve (ROC) analysis, sensitivity, specificity, accuracy, and F1-score. Decision curve analysis was performed to determine whether the nomogram was clinically useful. Results: The independent risk factors for MVI were maximum tumor length, rad-score, and miRNA-21 (all P < 0.001). The sensitivity, specificity, accuracy, and F1-score of the nomogram in the validation cohort were 0.970, 0.722, 0.884, and 0.916, respectively. The AUC of the nomogram was 0.900 (95% CI: 0.808-0.992) in the validation cohort, higher than that of any other single factor model (maximum tumor length, rad-score, and miRNA-21). Conclusion: The radiogenomics nomogram shows satisfactory predictive performance in predicting MVI in HCC and provides a feasible and practical reference for tumor treatment decisions.

Prognosis comparison between hepatocellular carcinoma patients with microvascular invasion who received hepatectomy alone and those who underwent early PA-TACE: a retrospective cohort study.

Background: Postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE) can achieve longer overall survival (OS) and disease-free survival (DFS) in hepatocellular carcinoma (HCC) patients with microvascular invasion (MVI). We investigated whether this treatment strategy could benefit these patients by mediating the dysfunctional immunological status. Therefore, a retrospective cohort study was conducted to investigate the effect of early PA-TACE in HCC patients with MVI by measuring the levels of T helper cell 17 (Th17) and regulatory T cell (Treg). Methods: This study retrospectively included 472 patients with HCC undergoing hepatectomy between December 2015 and December 2018, and 115 patients with MVI confirmed by postoperative pathology were enrolled and divided into two groups of TACE group and non-TACE group according to whether TACE was performed. HCC patients with MVI. The proportion of Treg and Th17 cells in peripheral blood was measured one day before and four weeks after TACE. All patients in the two groups were followed up until death or until the study ended in December 2023. The rates of OS and progression-free survival (PFS) in patients with MVI were compared between those who received hepatectomy alone and those who underwent early PA-TACE. Results: Among 115 HCC patients with MVI from 472 patients, the study enrolled 51 patients with PA-TACE into the TACE group and 42 patients without TACE into the non-TACE group. There were no statistical differences in baseline data between the two groups (all P>0.05). The frequency of Treg among CD4+ T cells in HCC patients with PA-TACE was significantly lower than baseline (7.34%±3.61% vs. 5.82%±2.76%, P<0.001), and the frequency of Th17 among CD4+ T cells in these patients was significantly higher than baseline (0.49%±0.28% vs. 0.50%±0.25%, P<0.001). Among all the patients, the median OS was 61.8 months. The OS rate and PFS rate at 12, 36, and 60 months in the TACE group were significantly higher than those in the non-TACE group (all P<0.05). Conclusions: PA-TACE may have roles in improving survival outcomes, and restoring immune homeostasis in HCC patients with MVI after hepatectomy.

The value of contrast-enhanced ultrasound combined with microflow imaging in predicting microvascular invasion of hepatocellular carcinoma before operation.

OBJECTIVE: To evaluate the preoperative predictive value of contrast-enhanced ultrasound (CEUS) combined with microflow imaging (MFI) in microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHODS: In our study, 80 patients with HCC were analyzed retrospectively. According to the gold standard of postoperative pathology, the patients were divided into MVI positive group (n = 39) and MVI negative group (n = 41). we were to analyze the correlation between CEUS and MVI in combination with MFI, to identify independent risk factors for the occurrence of MVI positive, and to analyze the predictive efficacy of every independent risk factor and their combination in preoperative prediction of MVI. RESULTS: In our study, 80 patients were enrolled, including 39 patients in the MVI-positive group and 41 patients in the MVI-negative group, with a MVI-positive rate of 48.8%. By univariate analysis and multivariate analysis, it was found that there were statistically significant differences in enhancement range extension, start time of wash out and CEUS-MFI between the two groups, which were independent risk factors for MVI-positive. The combination of three independent risk factors is more effective than single one in predicting MVI of HCC. CONCLUSIONS: CEUS combined with MFI is feasible for the preoperative prediction of MVI in HCC, and can provides meaningful help for individualized clinical treatment.

Corrigendum: Fat fraction quantification with MRI estimates tumor proliferation of hepatocellular carcinoma.

[This corrects the article DOI: 10.3389/fonc.2024.1367907.].

Identification of prognostic biomarkers for cholangiocarcinoma by combined analysis of molecular characteristics of clinical MVI subtypes and molecular subtypes.

Cholangiocarcinoma (CCA) is widely noted for its high degree of malignancy, rapid progression, and limited therapeutic options. This study was carried out on transcriptome data of 417 CCA samples from different anatomical locations. The effects of lipid metabolism related genes and immune related genes as CCA classifiers were compared. Key genes were derived from MVI subtypes and better molecular subtypes. Pathways such as epithelial mesenchymal transition (EMT) and cell cycle were significantly activated in MVI-positive group. CCA patients were classified into three (four) subtypes based on lipid metabolism (immune) related genes, with better prognosis observed in lipid metabolism-C1, immune-C2, and immune-C4. IPTW analysis found that the prognosis of lipid metabolism-C1 was significantly better than that of lipid metabolism-C2 + C3 before and after correction. KRT16 was finally selected as the key gene. And knockdown of KRT16 inhibited proliferation, migration and invasion of CCA cells.

Fat fraction quantification with MRI estimates tumor proliferation of hepatocellular carcinoma.

Purpose: To assess the utility of fat fraction quantification using quantitative multi-echo Dixon for evaluating tumor proliferation and microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods: A total of 66 patients with resection and histopathologic confirmed HCC were enrolled. Preoperative MRI with proton density fat fraction and R2* mapping was analyzed. Intratumoral and peritumoral regions were delineated with manually placed regions of interest at the maximum level of intratumoral fat. Correlation analysis explored the relationship between fat fraction and Ki67. The fat fraction and R2* were compared between high Ki67(>30%) and low Ki67 nodules, and between MVI negative and positive groups. Receiver operating characteristic (ROC) analysis was used for further analysis if statistically different. Results: The median fat fraction of tumor (tFF) was higher than peritumor liver (5.24% vs 3.51%, P=0.012). The tFF was negatively correlated with Ki67 (r=-0.306, P=0.012), and tFF of high Ki67 nodules was lower than that of low Ki67 nodules (2.10% vs 4.90%, P=0.001). The tFF was a good estimator for low proliferation nodules (AUC 0.747, cut-off 3.39%, sensitivity 0.778, specificity 0.692). There was no significant difference in tFF and R2* between MVI positive and negative nodules (3.00% vs 2.90%, P=0.784; 55.80s-1 vs 49.15s-1, P=0.227). Conclusion: We infer that intratumor fat can be identified in HCC and fat fraction quantification using quantitative multi-echo Dixon can distinguish low proliferative HCCs.

Role of microvascular invasion in early recurrence of hepatocellular carcinoma after liver resection: A literature review.

Hepatectomy is widely considered a potential treatment for hepatocellular carcinoma (HCC). Unfortunately, one-third of HCC patients have tumor recurrence within 2 years after surgery (early recurrence), accounting for more than 60% of all recurrence patients. Early recurrence is associated with a worse prognosis. Previous studies have shown that microvascular invasion (MVI) is one of the key factors for early recurrence and poor prognosis in patients with HCC after surgery. This paper reviews the latest literature and summarizes the predictors of MVI, the correlation between MVI and early recurrence, the identification of suspicious nodules or subclinical lesions, and the treatment strategies for MVI-positive HCC. The aim is to explore the management of patients with MVI-positive HCC.

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

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

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.

PURPOSE: Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS: A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS: Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION: The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

Noninvasive urinary protein signatures combined clinical information associated with microvascular invasion risk in HCC patients.

BACKGROUND: Microvascular invasion (MVI) is the main factor affecting the prognosis of patients with hepatocellular carcinoma (HCC). The aim of this study was to identify accurate diagnostic biomarkers from urinary protein signatures for preoperative prediction. METHODS: We conducted label-free quantitative proteomic studies on urine samples of 91 HCC patients and 22 healthy controls. We identified candidate biomarkers capable of predicting MVI status and combined them with patient clinical information to perform a preoperative nomogram for predicting MVI status in the training cohort. Then, the nomogram was validated in the testing cohort (n = 23). Expression levels of biomarkers were further confirmed by enzyme-linked immunosorbent assay (ELISA) in an independent validation HCC cohort (n = 57). RESULTS: Urinary proteomic features of healthy controls are mainly characterized by active metabolic processes. Cell adhesion and cell proliferation-related pathways were highly defined in the HCC group, such as extracellular matrix organization, cell-cell adhesion, and cell-cell junction organization, which confirms the malignant phenotype of HCC patients. Based on the expression levels of four proteins: CETP, HGFL, L1CAM, and LAIR2, combined with tumor diameter, serum AFP, and GGT concentrations to establish a preoperative MVI status prediction model for HCC patients. The nomogram achieved good concordance indexes of 0.809 and 0.783 in predicting MVI in the training and testing cohorts. CONCLUSIONS: The four-protein-related nomogram in urine samples is a promising preoperative prediction model for the MVI status of HCC patients. Using the model, the risk for an individual patient to harbor MVI can be determined.

Precise localization of microvascular invasion in hepatocellular carcinoma based on three-dimensional histology-MR image fusion: an ex vivo experimental study.

Background: Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). However, MVI cannot be detected by conventional imaging. To localize MVI precisely on magnetic resonance (MR) images, we evaluated the feasibility and accuracy of 3-dimensional (3D) histology-MR image fusion of the liver. Methods: Animal models of VX2 liver tumors were established in 10 New Zealand white rabbits under ultrasonographic guidance. The whole liver lobe containing the VX2 tumor was extracted and divided into 4 specimens, for a total of 40 specimens. MR images were obtained with a T2-weighted sequence for each specimen, and then histological images were obtained by intermittent, serial pathological sections. 3D histology-MR image fusion was performed via landmark registration in 3D Slicer software. We calculated the success rate and registration errors of image fusion, and then we located the MVI on MR images. Regarding influencing factors, we evaluated the uniformity of tissue thickness after sampling and the uniformity of tissue shrinkage after dehydration. Results: The VX2 liver tumor model was successfully established in the 10 rabbits. The incidence of MVI was 80% (8/10). 3D histology-MR image fusion was successfully performed in the 39 specimens, and the success rate was 97.5% (39/40). The average registration error was 0.44±0.15 mm. MVI was detected in 20 of the 39 successfully registered specimens, resulting in a total of 166 MVI lesions. The specific location of all MVI lesions was accurately identified on MR images using 3D histology-MR image fusion. All MVI lesions showed as slightly hyperintense on the high-resolution MR T2-weighted images. The results of the influencing factor assessment showed that the tissue thickness was uniform after sampling (P=0.38), but the rates of the tissue shrinkage was inconsistent after dehydration (P<0.001). Conclusions: 3D histology-MR image fusion of the isolated liver tumor model is feasible and accurate and allows for the successful identification of the specific location of MVI on MR images.

Preoperative MRI features to predict vessels that encapsulate tumor clusters and microvascular invasion in hepatocellular carcinoma.

OBJECTIVE: To estimate the potential of preoperative MRI features in the prediction of the integration patterns of vessels that encapsulate tumor clusters (VETC) and microvascular invasion (MVI) (VM) patterns in hepatocellular carcinoma (HCC) patients after resection and to assess the prognostic value of VM patterns. MATERIALS AND METHODS: Patients who underwent surgical resection for HCC between July 2019 and July 2020 were retrospectively included in the training cohort and validation cohort. In the training cohort, patients were classified into VM-positive HCC (VM-HCC) and VM-negative HCC (non-VM HCC). Predictors associated with VM-HCC were determined by using logistic regression analyses and used to build a prediction model of VM-HCC. The model was tested in the validation cohort by area under the receiver operating characteristic curve (AUC) analysis. Prognostic factors associated with early recurrence of HCC were evaluated by use of Cox logistic regression analyses. RESULTS: Alpha-fetoprotein (AFP) level higher than 400 ng/mL (odds ratio [OR] = 8.0; 95% CI: 2.6-25.2; P < 0.001), non-smooth tumor margin (OR = 3.1; 95% CI: 1.4-6.0; P < 0.001) and peritumoral arterial enhancement (OR = 2.9; 95% CI: 1.4-6.2; P = 0.004) were independent predictors of VM-HCC. The AUCs of the prediction model for VM-HCC were 0.81 for the training cohort and 0.79 for the validation cohort. The high risk of VM-HCC predicted by the three preoperative predictors derived from the prediction model (hazard ratio [HR] 2.0; 95% CI: 1.3, 3.2; P = 0.003) were independently associated with early recurrence, while pathologically confirmed VM-HCC (HR 2.8; 95% CI: 1.6, 3.8; P < 0.001) and satellite nodules (HR 1.8; 95% CI: 1.1, 3.1; P = 0.025) were independently associated with early recurrence after surgical resection. CONCLUSION: The predictive model can be used to predict VM patterns. VM-HCC is associated with increased risk of early recurrence after surgical resection in HCC.

ProJect: a powerful mixed-model missing value imputation method.

Missing values (MVs) can adversely impact data analysis and machine-learning model development. We propose a novel mixed-model method for missing value imputation (MVI). This method, ProJect (short for Protein inJection), is a powerful and meaningful improvement over existing MVI methods such as Bayesian principal component analysis (PCA), probabilistic PCA, local least squares and quantile regression imputation of left-censored data. We rigorously tested ProJect on various high-throughput data types, including genomics and mass spectrometry (MS)-based proteomics. Specifically, we utilized renal cancer (RC) data acquired using DIA-SWATH, ovarian cancer (OC) data acquired using DIA-MS, bladder (BladderBatch) and glioblastoma (GBM) microarray gene expression dataset. Our results demonstrate that ProJect consistently performs better than other referenced MVI methods. It achieves the lowest normalized root mean square error (on average, scoring 45.92% less error in RC_C, 27.37% in RC_full, 29.22% in OC, 23.65% in BladderBatch and 20.20% in GBM relative to the closest competing method) and the Procrustes sum of squared error (Procrustes SS) (exhibits 79.71% less error in RC_C, 38.36% in RC full, 18.13% in OC, 74.74% in BladderBatch and 30.79% in GBM compared to the next best method). ProJect also leads with the highest correlation coefficient among all types of MV combinations (0.64% higher in RC_C, 0.24% in RC full, 0.55% in OC, 0.39% in BladderBatch and 0.27% in GBM versus the second-best performing method). ProJect's key strength is its ability to handle different types of MVs commonly found in real-world data. Unlike most MVI methods that are designed to handle only one type of MV, ProJect employs a decision-making algorithm that first determines if an MV is missing at random or missing not at random. It then employs targeted imputation strategies for each MV type, resulting in more accurate and reliable imputation outcomes. An R implementation of ProJect is available at https://github.com/miaomiao6606/ProJect.

Development of a magnetic resonance imaging-derived radiomics model to predict microvascular invasion in patients with hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) with microvascular invasion (MVI) has a poor prognosis, is prone to recurrence and metastasis, and requires more complex surgical techniques. Radiomics is expected to enhance the discriminative performance for identifying HCC, but the current radiomics models are becoming increasingly complex, tedious, and difficult to integrate into clinical practice. The purpose of this study was to investigate whether a simple prediction model using noncontrast-enhanced T2-weighted magnetic resonance imaging (MRI) could preoperatively predict MVI in HCC. Methods: A total of 104 patients with pathologically confirmed HCC (training cohort, n=72; test cohort, n=32; ratio, about 7:3) who underwent liver MRI within 2 months prior to surgery were retrospectively included. A total of 851 tumor-specific radiomic features were extracted on T2-weighted imaging (T2WI) for each patient using AK software (Artificial Intelligence Kit Version; V. 3.2.0R, GE Healthcare). Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used in the training cohort for feature selection. The selected features were incorporated into a multivariate logistic regression model to predict MVI, which was validated in the test cohort. The model's effectiveness was evaluated using the receiver operating characteristic and calibration curves in the test cohort. Results: Eight radiomic features were identified to establish a prediction model. In the training cohort, the area under the curve, accuracy, specificity, sensitivity, and positive and negative predictive values of the model for predicting MVI were 0.867, 72.7%, 84.2%, 64.7%, 72.7%, and 78.6%, respectively; while in the test cohort, they were 0.820, 75%, 70.6%, 73.3%, 75%, and 68.8%, respectively. The calibration curves displayed good consistency between the prediction of MVI by the model and actual pathological results in both the training and validation cohorts. Conclusions: A prediction model using radiomic features from single T2WI can predict MVI in HCC. This model has the potential to be a simple and fast method to provide objective information for decision-making during clinical treatment.

Microvascular invasion of hepatocellular carcinoma predicts microvascular invasion of its recurrence: potential implications for salvage liver transplantation?

Background: Microvascular invasion (MVI) can only be assessed on a full surgical specimen. We aimed at evaluating, whether the histology of the primary tumor is predictive of MVI in a hepatocellular carcinoma (HCC) recurrence. Methods: Patients, who underwent liver resection or orthotopic liver transplantation (OLT) for recurrent HCC from January 2001 until June 2018 were eligible for this retrospective analysis. Resected specimens were evaluated for HCC subtype/morphology, vessels encapsulating tumor clusters (VETC)-pattern and MVI. Dichotomous parameters were analyzed using χ2-test and ϕ-values, with P values <0.05 being considered significant. Results: Of 230 HCC recurrences, 37 (16.1%) underwent repeated liver resection (n=22) or OLT (n=15). Of these, 67.6% initially exceeded the Milan criteria. MVI correlated Milan criteria (P=0.005), tumor size (P=0.015) and VETC-pattern (P=0.034) in the primary specimen. The recurrences shared many features of the primary HCC such as tumor grade (P=0.002), VETC-pattern (P=0.035), and MVI (P=0.046). In recurrences, however, only the concordance with the Milan criteria correlated with MVI (P=0.018). No patient without MVI in the primary HCC revealed MVI on early recurrence (<2 years) (P=0.035). Conclusions: HCC recurrences share many biological features of the primary tumor. Moreover, early recurrences of MVI-negative HCC never revealed MVI. This finding offers novel concepts, e.g., patient selection for salvage OLT.