Microvascular Invasion Risk Research Articles

Assessing microvascular invasion in HBV-related hepatocellular carcinoma: an online interactive nomogram integrating inflammatory markers, radiomics, and convolutional neural networks.

The early recurrence of hepatocellular carcinoma (HCC) correlates with decreased overall survival. Microvascular invasion (MVI) stands out as a prominent hazard influencing post-resection survival status and metastasis in patients with HBV-related HCC. The study focused on developing a web-based nomogram for preoperative prediction of MVI in HBV-HCC. 173 HBV-HCC patients from 2017 to 2022 with complete preoperative clinical data and Gadopentetate dimeglumine-enhanced magnetic resonance images were randomly divided into two groups for the purpose of model training and validation, using a ratio of 7:3. MRI signatures were extracted by pyradiomics and the deep neural network, 3D ResNet. Clinical factors, blood-cell-inflammation markers, and MRI signatures selected by LASSO were incorporated into the predictive nomogram. The evaluation of the predictive accuracy involved assessing the area under the receiver operating characteristic (ROC) curve (AUC), the concordance index (C-index), along with analyses of calibration and decision curves. Inflammation marker, neutrophil-to-lymphocyte ratio (NLR), was positively correlated with independent MRI radiomics risk factors for MVI. The performance of prediction model combined serum AFP, AST, NLR, 15 radiomics features and 7 deep features was better than clinical and radiomics models. The combined model achieved C-index values of 0.926 and 0.917, with AUCs of 0.911 and 0.907, respectively. NLR showed a positive correlation with MRI radiomics and deep learning features. The nomogram, incorporating NLR and MRI features, accurately predicted individualized MVI risk preoperatively.

Nomogram for preoperative estimation of microvascular invasion risk in hepatocellular carcinoma

Microvascular invasion (MVI) is an adverse prognostic indicator of tumor recurrence after surgery for hepatocellular carcinoma (HCC). Therefore, developing a nomogram for estimating the presence of MVI before liver resection is necessary. We retrospectively included 260 patients with pathologically confirmed HCC at the Fifth Medical Center of Chinese PLA General Hospital between January 2021 and April 2024. The patients were randomly divided into a training cohort (n = 182) for nomogram development, and a validation cohort (n = 78) to confirm the performance of the model (7:3 ratio). Significant clinical variables associated with MVI were then incorporated into the predictive nomogram using both univariate and multivariate logistic analyses. The predictive performance of the nomogram was assessed based on its discrimination, calibration, and clinical utility. Serum carnosine dipeptidase 1 ([CNDP1] OR 2.973; 95 % CI 1.167–7.575; p = 0.022), cirrhosis (OR 8.911; 95 % CI 1.922–41.318; p = 0.005), multiple tumors (OR 4.095; 95 % CI 1.374–12.205; p = 0.011), and tumor diameter ≥3 cm (OR 4.408; 95 % CI 1.780–10.919; p = 0.001) were independent predictors of MVI. Performance of the nomogram based on serum CNDP1, cirrhosis, number of tumors and tumor diameter was achieved with a concordance index of 0.833 (95 % CI 0.771–0.894) and 0.821 (95 % CI 0.720–0.922) in the training and validation cohorts, respectively. It fitted well in the calibration curves, and the decision curve analysis further confirmed its clinical usefulness. The nomogram, incorporating significant clinical variables and imaging features, successfully predicted the personalized risk of MVI in HCC preoperatively.

Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images.

Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI and RFS using preoperative MRI scans. Utilising a retrospective dataset of 725 HCC patients from seven institutions, we developed and validated a multitask deep learning model focused on predicting MVI and RFS. The model employs a transformer architecture to extract critical features from preoperative MRI scans. It was trained on a set of 234 patients and internally validated on a set of 58 patients. External validation was performed using three independent sets (n = 212, 111, 110). The multitask deep learning model yielded high MVI prediction accuracy, with AUC values of 0.918 for the training set and 0.800 for the internal test set. In external test sets, AUC values were 0.837, 0.815 and 0.800. Radiologists' sensitivity and inter-rater agreement for MVI prediction improved significantly when integrated with the model. For RFS, the model achieved C-index values of 0.763 in the training set and ranged between 0.628 and 0.728 in external test sets. Notably, PA-TACE improved RFS only in patients predicted to have high MVI risk and low survival scores (p < .001). Our deep learning model allows accurate MVI and survival prediction in HCC patients. Prospective studies are warranted to assess the clinical utility of this model in guiding personalised treatment in conjunction with clinical criteria.

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

We aimed to develop a Transformer-based deep learning (DL) network for prognostic stratification in hepatocellular carcinoma (HCC) patients undergoing RFA. 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). 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. The proposed MVI classifier can serve as a valuable imaging biomarker for prognostic stratification in early-stage HCC patients undergoing RFA.

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.

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

BackgroundMicrovascular 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.MethodsWe 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).ResultsUrinary 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.ConclusionsThe 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.

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.

A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients

Background and aimThe presence of microvascular invasion (MVI) will impair the surgical outcome of hepatocellular carcinoma (HCC). Adipose and muscle tissues have been confirmed to be associated with the prognosis of HCC. We aimed to develop and validate a nomogram based on adipose and muscle related-variables for preoperative prediction of MVI in HCC.MethodsOne hundred fifty-eight HCC patients from institution A (training cohort) and 53 HCC patients from institution B (validation cohort) were included, all of whom underwent preoperative CT scan and curative resection with confirmed pathological diagnoses. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to data dimensionality reduction and screening. Nomogram was constructed based on the independent variables, and evaluated by external validation, calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA).ResultsHistopathologically identified MVI was found in 101 of 211 patients (47.9%). The preoperative imaging and clinical variables associated with MVI were visceral adipose tissue (VAT) density, intramuscular adipose tissue index (IMATI), skeletal muscle (SM) area, age, tumor size and cirrhosis. Incorporating these 6 factors, the nomogram achieved good concordance index of 0.79 (95%CI: 0.72–0.86) and 0.75 (95%CI: 0.62–0.89) in training and validation cohorts, respectively. In addition, calibration curve exhibited good consistency between predicted and actual MVI probabilities. ROC curve and DCA of the nomogram showed superior performance than that of models only depended on clinical or imaging variables. Based on the nomogram score, patients were divided into high (> 273.8) and low (< = 273.8) risk of MVI presence groups. For patients with high MVI risk, wide-margin resection or anatomical resection could significantly improve the 2-year recurrence free survival.ConclusionBy combining 6 preoperative independently predictive factors of MVI, a nomogram was constructed. This model provides an optimal preoperative estimation of MVI risk in HCC patients, and may help to stratify high-risk individuals and optimize clinical decision making.

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

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

Prognostic analysis of postoperative pathological microvascular invasion in hepatocellular carcinoma and the effect of surgical margin on prognosis.

52 Background: To analyze the impact of different MVI grades and surgical margins on patient survival through analyzing the presence of microvascular invasion (MVI) after liver resection of hepatocellular carcinoma (HCC). Methods: The clinical and follow-up data of 513 HCC patients who underwent hepatectomy in our department from January 2016 to December 2020 were analyzed retrospectively. All the patients signed the informed consent form, complying with the medical ethics regulations. Risk factors of MVI in patients with HCC were analyzed by univariate and multivariate analysis, and effect of different MVI grades on postoperative complications was explored. The effects of different MVI grades and different surgical margins on tumor-free survival and overall survival were also analyzed. Results: There were 332 cases in M0 group, 123 cases in M1 group and 58 cases in M2 group. Univariate analysis showed that tumor diameter, tumor number, CNCL stage and alpha-fetoprotein were risk factors for MVI in patients with HCC, and multivariate analysis showed that tumor maximum diameter, tumor number and CNLC stage were independent influencing factors for MVI. There was no significant difference in the incidence of postoperative complications among different groups. The 1-year, 2-year, 3-year disease-free survival rate of M0 group was 79.6%, 71.0%, 63.4%, which was significantly higher than that in M1 group (59.6%, 48.0%，43.3%) and M2 group (31.0%, 27.5%, 25.2%). The 1-year, 2-year, 3-year overall survival rate of M0 group was 97.3%, 88.2%, 84.6%, which was significantly higher than that in M1 group (87.0%, 71.5%, 66.1%), and M2 group (79.3%, 61.9%, 52.6%),respectively. In the aspect of surgical margin, in MVI negative group, the disease-free survival rate of wide margin resection for 1-year, 2-year, 3-year was 82.6%, 70.7%, 65.4%, while that of narrow margin resection was 79.4%, 64.7% and 60.5% (p=0.322).The 1-year, 2-year, 3-year overall survival rate of patients who received wide margin resection was 97.5%, 89.6%, 85.2%, while that of patients who received narrow margin resection was 97.1%, 86.0%，82.8%. (p= 0.822). In the MVI positive group, the 1-year, 2-year and 3-year disease-free survival rate was 58.1%, 45.4%, 40.3% in the wide margin group and 42.9%, 37.3%, 36.1% in the narrow margin group (p=0. 044). The 1-year, 2-year, 3-year overall survival rate of the wide margin group was 88.8%, 71.9%, 64.3%, which was higher than that of the narrow margin group, 82.6%, 64.8%, 61.0% (p = 0.032). Conclusions: Tumor size, number and stage are independent risk factors for MVI positive patients.The higher the MVI grade, the worse the prognosis.In patients with MVI, the long-term prognosis of patients with wide margin is better than that of patients with narrow margin. Therefore, for patients with high risk of MVI before operation, wide margin should be selected as far as possible.

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

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. 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. 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). 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). 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.

Preoperative assessment of microvascular invasion risk using gadoxetate-enhanced MRI for predicting outcomes after liver transplantation for single hepatocellular carcinoma within the Milan criteria.

To compare therapeutic outcomes after liver transplantation (LT) between hepatocellular carcinomas (HCC) with low and high risk for microvascular invasion (MVI) within the Milan criteria evaluated preoperatively. Eighty patients with a single HCC who underwent LT as the initial therapy between 2008 and 2017 were included from two tertiary referral medical centers in a HBV-predominant population. A preoperative MVI-risk model was used to identify low- and high-risk patients. Recurrence-free survival (RFS) after LT between the two risk groups was compared using Kaplan-Meier curves with the log-rank test. Prognostic factors for RFS were identified using a multivariable Cox hazard regression analysis. Eighty patients were included (mean age, 51.8 years +/- 7.5 [standard deviation], 65 men). Patients were divided into low-risk (n = 64) and high-risk (n = 16) groups for MVI. The RFS rates after LT were significantly lower in the MVI high-risk group compared to the low-risk group at 1 year (75.0% [95% CI: 56.5-99.5%] vs. 96.9% [92.7-100%], p = 0.048), 3 years (62.5% [42.8-91.4%] vs. 95.3% [90.3-100%], p = 0.008), and 5 years (62.5% [42.8-91.4%] vs. and 95.3% [90.3-100%], p = 0.008). In addition, multivariable analysis showed that MVI high risk was the only significant factor for poor RFS (p = 0.016). HCC patients with a high risk of MVI showed significantly lower RFS after LT than those without. This model could aid in selecting optimal candidates in addition to the Milan criteria when considering upfront LT for patients with HCC if alternative treatment options are available. High risk for microvascular invasion (MVI) in hepatocellular carcinoma patients lowered recurrence-free survival after liver transplantation, despite meeting the Milan criteria. Identifying MVI risk could aid candidate selection for upfront liver transplantation, particularly if alternative treatments are available. • A predictive model-derived microvascular invasion (MVI) high- and low-risk groups had a significant difference in the incidence of MVI on pathology. • Recurrence-free survival after liver transplantation (LT) for single hepatocellular carcinoma (HCC) within the Milan criteria was significantly different between the MVI high- and low-risk groups. • The peak incidence of tumor recurrence was 20 months after liver transplantation, probably indicating that HCC with high risk for MVI had a high risk of early (≤ 2 years) tumor recurrence.

Nomogram for Predicting Microvascular Invasion in Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced MRI and Intravoxel Incoherent Motion Imaging

Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but it can only be determined through histopathological results. The aim of this study was to develop and validate a nomogram for preoperative prediction MVI in HCC using gadoxetic acid-enhanced magnetic resonance imaging (MRI) and intravoxel incoherent motion imaging (IVIM). From July 2017 to September 2022, 148 patients with surgically resected HCC who underwent preoperative gadoxetic acid-enhanced MRI and IVIM were included in this retrospective study. Clinical indicators, imaging features, and diffusion parameters were compared between the MVI-positive and MVI-negative groups using the chi-square test, Mann-Whitney U test, and independent sample t test. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance in predicting MVI. Univariate and multivariate analyses were conducted to identify the significant clinical-radiological variables associated with MVI. Subsequently, a predictive nomogram that integrates clinical-radiological risk factors and diffusion parameters was developed and validated. Serum alpha-fetoprotein level, tumor size, nonsmooth tumor margin, peritumoral hypo-intensity on hepatobiliary phase (HBP), apparent diffusion coefficient value and D value were statistically significant different between MVI-positive group and MVI-negative group. The results of multivariate analysis identified tumor size (odds ratio [OR], 0.786; 95% confidence interval [CI], 0.675-0.915; P<.01), nonsmooth tumor margin (OR, 2.299; 95% CI, 1.005-5.257; P<.05), peritumoral hypo-intensity on HBP (OR, 2.786; 95% CI, 1.141-6.802; P<.05) and D (OR, 0.293; 95% CI,0.089-0.964; P<.05) was the independent risk factor for the status of MVI. In ROC analysis, the combination of peritumoral hypo-intensity on HBP and D demonstrated the highest area under the curve value (0.902) in prediction MVI status, with sensitivity 92.8% and specificity 87.7%. The nomogram exhibited excellent predictive performance with C-index of 0.936 (95% CI 0.895-0.976) in the patient cohort, and had well-fitted calibration curve. The nomogram incorporating clinical-radiological risk factors and diffusion parameters achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

A novel nomogram for predicting microvascular invasion in hepatocellular carcinoma

Introduction and ObjectivesIn hepatocellular carcinoma (HCC), the prognosis of patients with microvascular invasion (MVI) is poor. Therefore, in this study, we established and evaluated the performance of a novel nomogram to predict MVI in patients with HCC. Materials and MethodsWe retrospectively obtained clinical data of 497 patients with HCC who underwent hepatectomy at Liaoning Cancer Hospital from November 1, 2018, to November 4, 2021. The patients (n = 497) were randomized in a 7:3 ratio into the training cohort (TC, n = 349) and the validation cohort (VC, n = 148). We performed Least Absolute Shrinkage and Selection Operator (LASSO) and univariate as well as multivariate logistic regression analyses (ULRA, MRLA) on patients in the TC to identify factors independently predicting MVI. ResultsPreoperative FIB-4, AFU, AFP levels, liver cirrhosis, and non-smooth tumor margin were independent risk factors for preoperative MVI prediction. The C-index of the TC, VC, and the entire cohort was 0.846, 0.786, and 0.829, respectively. The calibration curves demonstrated the outstanding agreement between predicted MVI incidences by our model and the actual MVI risk. Decision curve analysis (DCA) confirmed the significance of our predictive model in clinical settings. The Kaplan−Meier (KM) survival curve showed that the recurrence-free survival (RFS) and overall survival (OS) of patients in the high-MVI risk group were poor compared to those in the low-MVI risk group. ConclusionsWe constructed and evaluated the performance of the novel nomogram for predicting MVI risk. Our predictive model could adequately predict MVI risk and aid clinicians in selecting appropriate therapeutic strategies for patients.

Preoperative Prediction of Microvascular Invasion with Gadoxetic Acid-Enhanced Magnetic Resonance Imaging in Patients with Single Hepatocellular Carcinoma: The Implication of Surgical Decision on the Extent of Liver Resection

Introduction: Microvascular invasion (MVI) is one of the most important prognostic factors for hepatocellular carcinoma (HCC) recurrence, but its application in preoperative clinical decisions is limited. This study aimed to identify preoperative predictive factors for MVI in HCC and further evaluate oncologic outcomes of different types and extents of hepatectomy according to stratified risk of MVI. Methods: Patients with surgically resected single HCC (≤5 cm) who underwent preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI) were included in a single-center retrospective study. Two radiologists reviewed the images with no clinical, pathological, or prognostic information. Significant predictive factors for MVI were identified using logistic regression analysis against pathologic MVI and used to stratify patients. In the subgroup analysis, long-term outcomes of the stratified patients were analyzed using the Kaplan-Meier method with log-rank test and compared between anatomical and nonanatomical or major and minor resection. Results: A total of 408 patients, 318 men and 90 women, with a mean age of 56.7 years were included. Elevated levels of tumor markers (alpha-fetoprotein [α-FP] ≥25 ng/mL and PIVKA-II ≥40 mAU/mL) and three MRI features (tumor size ≥3 cm, non-smooth tumor margin, and arterial peritumoral enhancement) were independent predictive factors for MVI. As the MVI risk increased from low (no predictive factor) and intermediate (1–2 factors) to high-risk (3–4 factors), recurrence-free and overall survival of each group significantly decreased (p = 0.001). In the high MVI risk group, 5-year cumulative recurrence rate was significantly lower in patients who underwent major compared to minor hepatectomy (26.6 vs. 59.8%, p = 0.027). Conclusion: Tumor markers and MRI features can predict the risk of MVI and prognosis after hepatectomy. Patients with high MVI risk had the worst prognosis among the three groups, and major hepatectomy improved long-term outcomes in these high-risk patients.

Predicting post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma

Distinct vascular patterns, including microvascular invasion (MVI) and vessels encapsulating tumour clusters (VETC), are associated with poor outcomes of hepatocellular carcinoma (HCC). Imaging surrogates of these vascular patterns potentially help to predict post-resection recurrence. Herein, a prognostic model integrating imaging-based surrogates of these distinct vascular patterns was developed to predict postoperative recurrence-free survival (RFS) in patients with HCC. Clinico-radiological data of 1,285 patients with HCC from China undergoing surgical resection were retrospectively enrolled from seven medical centres between 2014 and 2020. A prognostic model using clinical data and imaging-based surrogates of MVI and VETC patterns was developed (n= 297) and externally validated (n= 373) to predict RFS. The surrogates (i.e. MVI and VETC scores) were individually built from preoperative computed tomography using two independent cohorts (n= 360 and 255). Whether the model's stratification was associated with postoperative recurrence following anatomic resection was also evaluated. The MVI and VETC scores demonstrated effective performance in their respective training and validation cohorts (AUC: 0.851-0.883 for MVI and 0.834-0.844 for VETC). The prognostic model incorporating serum alpha-foetoprotein, tumour multiplicity, MVI score, and VETC score achieved a C-index of 0.748-0.764 for the developing and external validation cohorts and generated three prognostically distinct strata. For patients at model-predicted medium risk, anatomic resection was associated with improved RFS (p <0.05). By contrast, anatomic resection had no impact on RFS in patients at model-predicted low or high risk (both p >0.05). The proposed model integrating imaging-based surrogates of distinct vascular patterns enabled accurate prediction for RFS. It can potentially be used to identify HCC surgical candidates who may benefit from anatomic resection. MVI and VETC are distinct vascular patterns of HCC associated with aggressive biological behaviour and poor outcomes. Our multicentre study provided a model incorporating imaging-based surrogates of these patterns for preoperatively predicting RFS. The proposed model, which uses imaging detection to estimate the risk of MVI and VETC, offers an opportunity to help shed light on the association between tumour aggressiveness and prognosis and to support the selection of the appropriate type of surgical resection.

Prognostic significance of controlling nutritional status score for patients with gastric cancer: A systematic review and meta‑analysis.

There is a need to identify potentially useful biomarker(s) for the prediction of prognostic outcomes in patients diagnosed with gastric cancer. This meta-analysis provided updated evidence on the association of controlling nutritional status (CONUT) score with survival and other clinicopathological outcomes in patients with gastric cancer. PubMed and Scopus databases were systematically searched. The review included studies, observational in design, that were conducted among patients with gastric cancer and had documented the association of CONUT score with outcomes of interest. The primary outcomes of interest were overall survival (OS), cancer-specific survival (CSS) and recurrence-free survival (RFS) along with tumour size and extent (T status), nodal status (N status) and tumour staging (TNM staging). STATA was used for statistical analysis. The meta-analysis was conducted with 17 studies. The 5-year OS [hazard ratio (HR), 1.75; 95% confidence interval (CI): 1.55, 1.96], RFS (HR, 1.58; 95% CI: 1.30, 1.91) and CSS (HR, 1.89; 95% CI: 1.01, 3.52) were comparatively poorer in the high CONUT group, than in low CONUT group. High CONUT score was associated with increased risk of having T3/T4 tumour [odds ratio (OR), 1.64; 95% CI: 1.16, 2.34], N2/N3 nodal status (OR, 1.44; 95% CI: 1.17, 1.77) and stage III/IV tumour (OR, 1.64; 95% CI: 1.43, 1.88). The risk of microvascular invasion (OR, 1.46; 95% CI: 1.20, 1.77) and post-operative complications (OR, 1.64; 95% CI: 1.31, 2.06) was higher in those with high CONUT. There were no differences in the risk of poorly differentiated tumour and need for adjuvant chemotherapy between the two groups. Findings suggested that preoperative assessment of CONUT score may be included in the routine assessment of patients with gastric cancer due to its association with survival and other clinical as well as pathological outcomes.

Wide surgical margins improve prognosis for HCC with microvascular invasion.

Hepatocellular carcinoma (HCC) is the sixth leading cause of malignant tumors worldwide. Liver resection is a pivotal treatment modality for HCC. Surgical margin plays an important role in decreasing recurrence and improving prognosis for HCC patients. This paper aimed to perform a systematic review of the literature in regard to surgical margin in HCC patients with microvascular invasion (MVI). Residual MVI due to insufficient surgical margins is the main origin of postoperative recurrence and metastasis in HCC patients. A wide surgical margin (WSM) significantly improves oncological outcomes and long-term survival in HCC patients with MVI. Progress in the preoperative prediction of MVI may contribute to precise surgical decision-making in the future. WSM was associated with better outcomes in HCC patients with MVI. WSM is recommended for well-preserved liver function HCC patients who are predicted to have a high risk of MVI preoperatively.

Establishment and validation of a preoperative nomogram model for predicting the risk of hepatocellular carcinoma with microvascular invasion

Objective: To establish and validate a nomogram model for predicting the risk of microvascular invasion(MVI) in hepatocellular carcinoma. Methods: The clinical data of 210 patients with hepatocellular carcinoma who underwent hepatectomy at Department of Hepatobiliary and Pancreatic Surgery,the Affiliated Hospital of Qingdao University from January 2013 to October 2021 were retrospectively analyzed. There were 169 males and 41 females, aged(M(IQR)) 57(12)years(range:30 to 80 years). The patients were divided into model group(the first 170 cases) and validation group(the last 40 cases) according to visit time. Based on the clinical data of the model group,rank-sum test and multivariate Logistic regression analysis were used to screen out the independent related factors of MVI. R software was used to establish a nomogram model to predict the preoperative MVI risk of hepatocellular carcinoma,and the validation group data were used for external validation. Results: Based on the modeling group data,the receiver operating characteristic curve was used to determine that cut-off value of DeRitis ratio,γ-glutamyltransferase(GGT) concentration,the inverse number of activated peripheral blood T cell ratio (-aPBTLR) and the maximum tumor diameter for predicting MVI, which was 0.95((area under curve, AUC)=0.634, 95%CI: 0.549 to 0.719), 38.2 U/L(AUC=0.604, 95%CI: 0.518 to 0.689),-6.05%(AUC=0.660, 95%CI: 0.578 to 0.742),4 cm(AUC=0.618, 95%CI: 0.533 to 0.703), respectively. Univariate and multivariate Logistic regression analysis showed that DeRitis≥0.95,GGT concentration ≥38.2 U/L,-aPBTLR>-6.05% and the maximum tumor diameter ≥4 cm were independent related factors for MVI in hepatocellular carcinoma patients(all P<0.05). The nomogram prediction model based on the above four factors established by R software has good prediction efficiency. The C-index was 0.758 and 0.751 in the model group and the validation group,respectively. Decision curve analysis and clinical impact curve showed that the nomogram model had good clinical benefits. Conclusions: DeRitis ratio,serum GGT concentration,-aPBTLR and the maximum tumor diameter are valuable factors for preoperative prediction of hepatocellular carcinoma with MVI. A relatively reliable nomogram prediction model could be established on them.

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

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. 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. 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). 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.