We aimed to systematically assess the value of radiomics/machine learning (ML) models for diagnosing microvascular invasion (MVI) in patients with cholangiocarcinoma (CCA) using various radiologic modalities.

A systematic search of was conducted on Web of Sciences, PubMed, Scopus, and Embase. All the studies that assessed the value of radiomics models or ML models along with the use of imaging features were included. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria and METhodological RadiomICs Score (METRICS) were used for quality assessment. Pooled estimates for the diagnostic performance of radiomics/ML models were calculated. I-squared was used to assess heterogeneity, and sensitivity and subgroup analyses were performed to find the sources of heterogeneity. Deeks' funnel plots were used to assess publication bias.

11 studies were included in the systematic review with only one study being about extrahepatic CCA. According to the METRICS, the mean score was 62.99 %. Meta-analyses were performed on intrahepatic CCA studies. The meta-analysis of the best ML models revealed an AUC of 0.93 in the training cohort and an AUC of 0.85 in the validation cohort. Regarding the best radiomics model, the AUC was 0.85 in the training cohort and 0.81 in the validation cohort.

Radiomics/ML models showed very good diagnostic performance regarding MVI diagnosis in patients with intrahepatic CCA and may provide a non-invasive method for this purpose. However, given the high heterogeneity and low number of the included studies, further multi-center studies with prospective design and robust external validation are essential.

This study aims to develop a machine learning model that accurately diagnoses microvascular invasion (MVI) in hepatocellular carcinoma by using radiomic features from MVI-positive regions of interest (ROIs). Unlike previous studies, which do not account for the location and distribution of MVI, this research focuses on correlating preoperative imaging with postoperative pathological MVI. This study involves obtaining ex vivo 3D ultrasound images of 36 hepatic specimens from nine rabbits. These images are fused with whole-slide images to localize MVI regions precisely. The identified MVI regions are segmented into MVI-positive ROIs, with a 1:3 ratio of positive to negative ROIs. Radiomic features are extracted from each ROI, and 30 features highly associated with MVI are selected for model development. The performance of several machine learning models is evaluated using metrics such as sensitivity, specificity, accuracy, the area under the curve (AUC), and F1 score. The GBDT model achieves the best results, with an AUC of 0.91, an F1 score of 0.85, a sensitivity of 0.76, a specificity of 0.92, and an accuracy of 0.86. The high diagnostic accuracy of these models highlights the potential for future clinical application in the precise diagnosis of MVI using radiomic features from MVI-positive ROIs.

Accurate prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for guiding treatment. This study evaluates and compares the performance of clinicoradiologic, traditional radiomics, deep-learning radiomics, feature fusion, and decision fusion models based on multi-region MR habitat imaging using six machine-learning classifiers.

We retrospectively included 300 HCC patients. The intratumoral and peritumoral regions were segmented into distinct habitats, from which radiomics and deep-learning features were extracted using arterial phase MR images. To reduce feature dimensionality, we applied intra-class correlation coefficient (ICC) analysis, Pearson correlation coefficient (PCC) filtering, and recursive feature elimination (RFE). Based on the selected optimal features, prediction models were constructed using decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and XGBoost (XGB) classifiers. Additionally, fusion models were developed utilizing both feature fusion and decision fusion strategies. The performance of these models was validated using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis.

The decision fusion model (VOI-Peri10-1) using LR and integrating clinicoradiologic, radiomics, and deep-learning features achieved the highest AUC of 0.808 (95% confidence interval [CI]: 0.807-0.912) in the test cohort, with good calibration (Hosmer-Lemeshow test, P > 0.050) and clinical net benefit.

The LR-based decision fusion model integrating clinicoradiologic, radiomics, and deep-learning features shows promise for preoperative prediction of MVI in HCC, aiding in patient outcome predictions and personalized treatment planning.

The high recurrent rate after surgery hinders the survival of patients with hepatocellular carcinoma (HCC). This prospective cohort study aimed to evaluate the efficacy and safety of lenvatinib plus transarterial chemoembolization (TACE) as an adjuvant therapy in HCC patients with high risk of recurrence.

Patients were enrolled from eight hepatobiliary centers in China. The primary endpoint was disease-free survival (DFS). The secondary endpoints were overall survival (OS) and safety. Additionally, propensity score matching (PSM) and other three propensity score analyses were performed to balance the potential baseline bias to validate the conclusion. The adverse events (AEs) were recorded throughout the study. The study was registered at ClinicalTrials.gov (NCT03838796).

A total of 297 patients were enrolled, with 147 in the LEN + TACE group and 150 in the TACE group. Before PSM, the LEN + TACE group achieved significantly better DFS than the TACE group (19.0 vs. 10.0 months, P = 0.011). PSM analysis identified 111 matched pairs. After PSM, the LEN + TACE group also showed better DFS (19.0 vs. 9.0 months, P = 0.018). Other three propensity score analyses yielded similar DFS benefit tendency. Furthermore, favorable OS was also obtained in the LEN + TACE group before PSM. Lenvatinib related AEs of grade 3 or 4 occurred in 28.6 % of the patients in the LEN + TACE group.

Adjuvant lenvatinib plus TACE might be a promising adjuvant approach for HCC patients with high risk of recurrence, which could significantly prolong DFS and potentially OS with a manageable safety profile.

This study aimed to develop a multiregional radiomics-based model using multisequence MRI to predict microvascular invasion in hepatocellular carcinoma.

We enrolled 141 patients with hepatocellular carcinoma, including 61 with microvascular invasion, who were diagnosed between March 2017 and July 2022. Clinical data were compared using the Wilcoxon rank-sum test or χ2 test. Patients were randomly divided into training (n=112, 80%) and test (n=29, 20%) data sets. Four MRI sequences-including T2-weighted imaging, T2-weighted imaging with fat suppression, arterial phase-contrast enhancement, and portal venous phase contrast enhancement-were used to build the radiomics model. The tumor volumes of interest were manually delineated, and the expand-5 mm and expand-10 mm volumes of interest were automatically generated. A total of 1409 radiomic features were extracted from each volume of interest. Feature selection was performed using the least absolute shrinkage and selection operator and Spearman correlation analysis. Three logistic regression models (Tumor, Tumor-Expand5, and Tumor-Expand10) were established based on the radiomic features. Model performance was assessed using receiver operating characteristic analysis and Delong's test.

Maximum tumor diameter, hepatitis B virus DNA, and aspartate aminotransferase levels were significantly different between the groups. The Tumor-Expand5mm model exhibited the best performance among the 3 models, with areas under the curve of 0.90 and 0.84 in the training and test data sets.

The Tumor-Expand5 model based on multisequence MRI shows great potential for predicting microvascular invasion in patients with hepatocellular carcinoma, and may further contribute to personal clinical decision-making.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer. HCC treatment is challenging; surgical resection is the primary curative treatment for early-stage disease, but recurrence rates are high. Immune checkpoint inhibitors (ICIs) are a promising neoadjuvant treatment that can reduce recurrence rates and mortality after surgery and achieve complete/partial responses. Clinical trials provide strong evidence for the efficacy and safety of ICI monotherapy for neoadjuvant HCC treatment.

This study aims to evaluate the value of Kupffer phase imaging with Sonazoid-Contrast-Enhanced Ultrasound (CEUS) in differentiating atypical Hepatocellular Carcinoma (HCC) from Focal Nodular Hyperplasia (FNH), compared to the efficacy of Gadodiamide-Enhanced Magnetic Resonance Imaging (CEMRI).

We retrospectively reviewed a total of 56 focal liver lesions, comprising 36 atypical HCC and 20 FNH lesions, which were examined pre-operatively using both CEUS and CEMRI. Features were extracted from the images obtained from both modalities. The diagnostic performance of these features was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC).

In the multivariate analysis, HBsAg-positive status (OR = 8.395, p = 0.021) and hypo-enhancement in the Kupffer phase (OR = 5.276, p = 0.042) were identified as independent predictors of atypical HCC. The specificity and sensitivity of HBsAg-positive for diagnosing atypical HCC are 65.0 % and 91.7 %, respectively. The specificity and sensitivity of hypo-enhancement in the Kupffer phase for diagnosing atypical HCC are 95.0 % and 63.9 %, respectively. Significantly, by integrating CEUS diagnostic characteristics to distinguish atypical HCC from FNH, we attained a specificity of 100.0 %, a sensitivity of 63.9 %, and a diagnostic accuracy of 76.8 %. Similarly, the combination of CEMRI diagnostic features resulted in a specificity of 100.0 %, a sensitivity of 47.2 %, and an overall diagnostic accuracy of 66.1 %. CEUS's diagnostic accuracy is superior to CEMRI and this difference is statistically significant (P < 0.05).

Sonazoid-CEUS demonstrates significant clinical potential and is a viable tool for the differential diagnosis of atypical HCC and FNH.

Accurate identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for treatment and prognosis. Single-modality and feature fusion models using manual segmentation fail to provide insights into MVI. This study aims to develop a DeepLab V3+ model for automated segmentation of HCC magnetic resonance (MR) images and a decision fusion model to predict MVI and early recurrence (ER).

This retrospective study included 209 HCC patients (146 in the training and 63 in the test cohorts). The performance of DeepLab V3+ for HCC MR image segmentation was evaluated using Dice Loss and F1 score. Intraclass correlation coefficients (ICCs) assessed feature extraction reliability. Spearman's correlation analyzed the relationship between tumor volumes from automated and manual segmentation, with agreement evaluated using Bland-Altman plots. Model performance was assessed using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis. A nomogram predicted ER of HCC after surgery, with Kaplan-Meier analysis for 2-year recurrence-free survival (RFS).

The DeepLab V3+ model demonstrated high segmentation accuracy, with strong agreement in feature extraction (ICC: 0.802-0.999). The decision fusion model achieved AUCs of 0.968 and 0.878 for MVI prediction, and the nomogram for predicting ER yielded AUCs of 0.782 and 0.690 in the training and test cohorts, respectively, with significant RFS differences between the risk groups.

The DeepLab V3+ model accurately segmented HCC. The decision fusion model significantly improved MVI prediction, and the nomogram offered valuable insights into recurrence risk for clinical decision-making.

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

This investigation aimed to create a new nomogram based on complement C3 to forecast 1-, 3-, and 5-year overall survival (OS) rates in patients with early-stage hepatocellular carcinoma (HCC) exhibiting microvascular invasion (MVI) post-curative surgery.

This study encompassed 1234 patients treated with resection at the Affiliated Cancer Hospital of Guangxi Medical University. The cohort for primary included 865 patients from December 2015 to December 2019, while the validation cohort comprised 369 patients. Follow-ups were conducted regularly until December 2024. Variables predicting survival were identified using Cox regression analyses, and based on these, a nomogram was constructed. This nomogram's accuracy was assessed via time-dependent ROC curves, calibration curves and KM curve analyses.

Investigations identified complement C3, PT, the presence of cirrhosis, tumor capsule, and MVI-M2 as distinct predictors of survival in HCC patients. Based on these findings, a predictive nomogram was constructed and validated, aimed at estimating the 1-, 3-, and 5-year OS. The efficacy of the nomogram was validated through analyses with ROC curves, calibration curves, each demonstrating positive outcomes. Additionally, KM curve analysis effectively separated the patient populations into two prognostic risk categories within both the primary and validation cohorts.

In conclusion, a new nomogram has been developed and corroborated through multivariate Cox regression analysis, aimed at estimating overall survival for patients in early stages of microvascular invasion following surgical resection. This tool has proven to be more effective in forecasting survival outcomes for such patients post-curative surgery.

We aimed to develop a convenient nomogram to predict preoperative MVI in patients with hepatocellular carcinoma (HCC). Patients who underwent surgical resection due to HCC from June 2018 to June 2023 at the Third Affiliated Hospital of Sun Yat-sen University were retrospectively reviewed. Univariate and multivariable logistic linear regression analyses were used to investigate potential risk factors for MVI. A nomogram was plotted based on these risk factors. The tumor diameter (≥ 5 cm), BCLC stage, PLT (>127.50 × 109/L), AST (>29.50 U/L) and AFP (>10.07 ng/ml) were identified as independent preoperative risk factors for MVI by univariate and multivariable logistic analysis. The nomogram demonstrated decent accuracy in estimating the presence of MVI, with an AUC of 0.69 (95%CI: 0.64-0.73). The calibration curves exhibited a close match between the predicted probabilities and the actual estimates of MVI in the nomogram (p = 0.947). Decision curve analysis (DCA) revealed that the prediction model had a high net benefit if the threshold probability>20%. High platelet counts were strongly associated with the presence of MVI in HCC patients. Our convenient nomogram demonstrated decent accuracy in estimating the presence of MVI and had notable clinical application.

Microvascular invasion (MVI) is a critical prognostic factor for postoperative hepatocellular carcinoma recurrence, but the reliability of its current pathological diagnosis remains uncertain.

To evaluate the accuracy of current 7-point sampling methods and propose an optimal pathological protocol using whole-mount slide imaging (WSI) for better MVI detection.

We utilized 40 New Zealand white rabbits to establish VX2 liver tumor models. The entire tumor-containing liver lobe was subsequently obtained, following which five different sampling protocols (A-E) were employed to evaluate the detection rate, accuracy, quantity, and distribution of MVI, with the aim of identifying the optimal sampling method.

VX2 liver tumor models were successfully established in 37 rabbits, with an incidence of MVI of 81.1% (30/37). The detection rates [27% (10/37), 43% (16/37), 62% (23/37), 68% (25/37), and 93% (14/15)] and quantity (15, 36, 107, 125, and 395) of MVI increased significantly from protocols A to E. The distribution of MVI showed fewer MVIs farther away from the tumor, but the percentage of MVI detected quantity gradually increased from 6.7% to 48.3% in the distant nonneoplastic liver tissue from protocols A to E. Protocol C was identified as the optimal sampling method by comparing them in sequence. The sampling protocol of three consecutive interval WSIs at the tumor center (WSI3) was further screened to determine the optimal number of WSIs. Protocol A (7-point sampling method) exhibited only 46% accuracy and a high false-negative rate of 67%. Notably, the WSI3 protocol improved the accuracy to 78% and decreased the false-negative rate to 27%.

The current 7-point sampling method has a high false-negative rate in MVI detection. In contrast, the WSI3 protocol provides a practical and effective approach to improve MVI diagnostic accuracy, which is crucial for hepatocellular carcinoma diagnosis and treatment planning.

To investigate clinical effects of hepatic artery interventional embolization chemotherapy (TACE) for primary hepatocellular carcinoma (PHC).

73 patients with PHC in our hospital from January 2017 to January 2018 were selected and divided into 37 cases in study group and 36 cases in control group by random number table method. The control group received only ultrasound-guided microwave ablation treatment, and the study group received TACE treatment again before surgery based on control group. The expression levels of cancer antigen 125 (CA125), alpha-fetoprotein (AFP), multiple tumor suppressors 1 (P16) proteins, and cancer antigen 19-9 (CA19-9) were compared between the two groups at different time periods after treatment, and the remission rate (ORR), control rate (DCR), complication rate at 3 months after treatment and survival rate at 3 years after treatment were compared.

After 1 year of treatment, ORR, DCR, and P16 protein levels in the study group were higher than those in the control group (P < 0.05), and differences were statistically significant; CA125, CA19-9, and AFP levels in study group were lower than those in the control group (P < 0.05), and differences were statistically significant. The regression equation showed that long-term survival rate of both groups showed decreasing trend over time, while long-term survival rate of study group was always higher than that of the control group.

Comprehensive intervention for hepatic artery interventional chemoembolization in patients with primary hepatocellular carcinoma is more effective, which can effectively reduce incidence of complications and adverse effects in patients and help shorten treatment time of hepatic artery interventional chemoembolization in patients.

Microvascular invasion (MVI) serves as a significant predictor of poor prognosis in intrahepatic cholangiocarcinoma (ICC). This study aims to establish a comprehensive model utilizing MR radiomics for preoperative MVI status stratification and outcome prediction in ICC patients.

A total of 249 ICC patients were randomly assigned to training and validation cohorts (174:75), along with a time-independent test cohort consisting of 47 ICC patients. Independent clinical and imaging predictors were identified by univariate and multivariate logistic regression analyses. The radiomic model was developed based on robust radiomic features extracted using a logistic regression classifier. The predictive efficacy of the models was evaluated by receiver operating characteristic curves, calibration curves and decision curves. Multivariate Cox analysis identified the independent risk factors for recurrence-free survival and overall survival, Kaplan-Meier curves were plotted, and a nomogram was used to visualize the predictive model.

The imaging model included tumor size and intrahepatic duct dilatation. The radiomics model comprised 25 stable radiomics features. The Imaging-Radiomics (IR) model, which integrates independent predictors and robust radiomics features, demonstrates desirable performance for MVI (AUCtraining= 0.890, AUCvalidation= 0.885 and AUCtest= 0.815). The calibration curve and decision curve validate the clinical utility. Preoperative MVI prediction based on IR model demonstrated comparable accuracy in MVI stratification and outcome prediction when compared to histological MVI.

The IR model and the nomogram based on IR model-predicted MVI status may serve as potential tools for MVI status stratification and outcome prediction in ICC patients preoperatively.

Regional lymph node metastasis (LNM) assessment is crucial for predicting intrahepatic cholangiocarcinoma (iCCA) prognosis. However, imaging assessment has limitations for identifying LNM.

To investigate the association between MRI radiomics features, regional LNM status, and prognosis in iCCA.

Retrospective.

Two hundred ninety-six patients (male = 197) with surgically confirmed iCCA.

1.5 T and 3.0 T. DWI, T2WI, and contrast-enhanced T1WI.

Clinical information, radiologic, and MRI-based radiomics features associated with LNM status were collected to establish models. Performance of MRI, PET/CT, and the combined LNM models were compared in training (N = 207) and test (N = 89) datasets. Overall survival (OS) was compared based on LNM status.

The independent features were selected by 5-fold cross-validation. The performance of MRI, PET/CT, and the models was evaluated using the area under receiver operating characteristic curve (AUC). Univariable and multivariable Cox regression were used to identify independent variables for OS. Kaplan-Meier curves were compared with the log-rank test between LNM positive and negative groups. P < 0.05 was considered statistically significant.

Intrahepatic duct dilatation, enhancement pattern, and CA19-9 were independent clinicoradiologic features. The radiomics model was constructed by the independent radiomics features extracted from T2WI and delay phase T1WI. The combined LNM model showed AUC of 0.888, 0.884, and 0.811 in training, validation, and test cohorts with a positive net benefit. PET/CT exhibited similar sensitivity to the combined LNM model (0.750 vs. 0.733, P > 0.999) while the combined LNM model showed higher specificity (0.703 vs. 0.630, P = 0.039) in the test cohort. High risk of regional LNM was significantly associated with worse OS (median: 24 months) than low risk (median: 30 months, P < 0.0001).

The combined LNM model has the strongest correlation with LNM status for mass-forming iCCA patients.

3 TECHNICAL EFFICACY: Stage 2.

To determine the diagnostic value of ultrasound, multi-phase enhanced computed tomography, and magnetic resonance imaging of small hepatocellular carcinoma.

Experimental studies on diagnosing small hepatocellular carcinoma in four databases: PubMed, Cochrane Library, Web of Science, and Embase, were comprehensively searched from October 2007 to October 2024. Relevant diagnostic accuracy data were extracted and a Bayesian model that combined direct and indirect evidence was used for analysis.

16 original studies were included and data from 2,447 patients were collated to assess the diagnostic value of 10 different methods. The methodological quality of the included studies was good and there was no obvious publication bias. The pooled DOR of all diagnostic methods was 19.61, which was statistically significant (I2 = 76.0%, P < 0.01, 95% CI:13.30 - 28.92). Normal US + CEUS + ultrasonic elastic imaging had the highest specificity (92.9), accuracy (93.6), and positive predictive value (94.4). Unenhanced MRI + Contrast-enhanced MRI had the highest sensitivity (96.6) and negative predictive value (96.6), but specificity (12.5) and positive predictive value (34.4) were extremely poor. Contrast-enhanced MRI had the highest diagnostic value in individual imaging methods (sensitivity: 66, specificity: 55.5, accuracy: 67.9, positive predictive value: 64.4, negative predictive value: 66.5). There was significant inconsistency and high heterogeneity in this study.

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024507883.

This study aimed to investigate how dynamic contrast-enhanced CT imaging signs correlate with the differentiation grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to assess their predictive value for MVI when combined with clinical characteristics.

We conducted a retrospective analysis of clinical data from 232 patients diagnosed with HCC at our hospital between 2021 and 2022. All patients underwent preoperative enhanced CT scans, laboratory tests, and postoperative pathological examinations. Among the 232 patients, 89 were identified as MVI-positive and 143 as MVI-negative. Regarding tumor differentiation, 56 patients were well-differentiated, 145 moderately, and 31 poorly. Multivariate logistic regression analysis was employed to establish a prediction model for variables showing significant differences. Additionally, the diagnostic performance of various indicators were evaluated using ROC analysis.

Among the qualitative data, significant differences (P<0.05) were observed between the MVI-positive and MVI-negative groups in 5 items such as peritumoral enhancement. In terms of quantitative data, the MVI-positive group exhibited higher maximum tumor length, AST, ALT, AFP levels and the ALBI score (P<0.05). Conversely, CT values in the arterial phase (AP), portal venous phase (PVP), and PT levels were lower in the MVI-positive group (P<0.05). Multivariate Logistic regression analysis identified ALBI score, PT level, CT value in PVP, and tumor capsule as independent risk factors for MVI occurrence (AUC: 0.71, 0.58, 0.66, and 0.60). The combined diagnostic AUC value was 0.82 (95% CI: 0.76-0.87). Significant differences were found among different differentiation grade groups in 10 items such as non-smooth tumor margin (P<0.05).

Preoperative dynamic contrast-enhanced CT examination in patients with HCC can be utilized to predict the presence of MVI. When combined with clinical characteristics, these imaging signs demonstrate good predictive performance for MVI status. Furthermore, this approach has significant implications for determining the differentiation grade of tumors.

Personalized precision medicine can be facilitated by clinically available preoperative microvascular invasion (MVI) prediction models that are reliable and postoperative MVI pathological grade-related recurrence prediction models that are accurate. In this study, we aimed to compare different mathematical models to derive the best preoperative prediction and postoperative recurrence prediction models for MVI.

A total of 143 patients with hepatocellular carcinoma (HCC) whose clinical, laboratory, imaging, and pathological data were available were included in the analysis. Logistic regression, Cox proportional hazards regression, LASSO regression with 10-fold cross-validation, stepwise regression, and random forest methods were used for variable screening and predictive modeling. The accuracy and validity of seven preoperative MVI prediction models and five postoperative recurrence prediction models were compared in terms of C-index, net reclassification improvement, and integrated discrimination improvement.

Multivariate logistic regression analysis revealed that a preoperative nomogram model with the variables cirrhosis diagnosis, alpha-fetoprotein > 400, and diameter, shape, and number of lesions can predict MVI in patients with HCC reliably. Postoperatively, a nomogram model with MVI grade, number of lesions, capsule involvement status, macrovascular invasion, and shape as the variables was selected after LASSO regression and 10-fold cross-validation analysis to accurately predict the prognosis for different MVI grades. The number and shape of the lesions were the most common predictors of MVI preoperatively and recurrence postoperatively.

Our study identified the best statistical approach for the prediction of preoperative MVI as well as postoperative recurrence in patients with HCC based on clinical, imaging, and laboratory tests results. This could expedite preoperative treatment decisions and facilitate postoperative management.

To investigate the impact of surgical resection margin and hepatic resection type on prognosis and compare their prognostic significance on patients with hepatocellular carcinoma (HCC) with or without microvascular invasion (MVI) who underwent laparoscopic liver resection (LLR).

A retrospective analysis was conducted on 320 patients with HCC who underwent LLR. According to the grading of MVI, patients were classified as M0, M1 and M2. Patients were divided into the anatomical resection (AR) and nonanatomical resection (NAR) groups according to the hepatic resection type. Survival and Cox regression analyses were performed to explore the effects of AR and NAR, wide and narrow resection margin on overall survival (OS) and time to recurrence (TTR).

In the whole cohort, narrow resection margin was an independent risk factor for OS and TTR, whereas NAR was not. Subgroup analysis showed that narrow resection margin and NAR were both independent risk factors for OS and TTR in HCC patients with MVI. The 5-year OS and TTR rates of the two groups (NAR-wide resection margin and AR-narrow resection margin) with M1 were 85.3 % versus 62 % and 34.4 % versus 60.2 %. Similarly, the 5-year OS and TTR rates of the two groups (NAR-wide resection margin and AR-narrow resection margin) with M2 were 80.2 % versus 47.9 % and 30.8 % versus 64.8 %.

Anatomical hepatectomy and wide resection margin were independent protective factors for HCC patients with MVI receiving LLR. Nonetheless, wide resection margin had a greater impact on prognosis than anatomical hepatectomy.

Microvascular invasion (MVI) is a significant indicator of the aggressive behavior of hepatocellular carcinoma (HCC). Expanding the surgical resection margin and performing anatomical liver resection may improve outcomes in patients with MVI. However, no reliable preoperative method currently exists to predict MVI status or to identify patients at high-risk group (M2).

To develop and validate models based on contrast-enhanced computed tomography (CECT) radiomics and clinicoradiological factors to predict MVI and identify M2 among patients with hepatitis B virus-related HCC (HBV-HCC). The ultimate goal of the study was to guide surgical decision-making.

A total of 270 patients who underwent surgical resection were retrospectively analyzed. The cohort was divided into a training dataset (189 patients) and a validation dataset (81) with a 7:3 ratio. Radiomics features were selected using intra-class correlation coefficient analysis, Pearson or Spearman's correlation analysis, and the least absolute shrinkage and selection operator algorithm, leading to the construction of radscores from CECT images. Univariate and multivariate analyses identified significant clinicoradiological factors and radscores associated with MVI and M2, which were subsequently incorporated into predictive models. The models' performance was evaluated using calibration, discrimination, and clinical utility analysis.

Independent risk factors for MVI included non-smooth tumor margins, absence of a peritumoral hypointensity ring, and a high radscore based on delayed-phase CECT images. The MVI prediction model incorporating these factors achieved an area under the curve (AUC) of 0.841 in the training dataset and 0.768 in the validation dataset. The M2 prediction model, which integrated the radscore from the 5 mm peritumoral area in the CECT arterial phase, α-fetoprotein level, enhancing capsule, and aspartate aminotransferase level achieved an AUC of 0.865 in the training dataset and 0.798 in the validation dataset. Calibration and decision curve analyses confirmed the models' good fit and clinical utility.

Multivariable models were constructed by combining clinicoradiological risk factors and radscores to preoperatively predict MVI and identify M2 among patients with HBV-HCC. Further studies are needed to evaluate the practical application of these models in clinical settings.

Pretreatment identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is important when selecting treatment strategies.

To improve models for predicting MVI and recurrence-free survival (RFS) by developing nomograms containing three-dimensional (3D) MR elastography (MRE).

Prospective.

188 patients with HCC, divided into a training cohort (n = 150) and a validation cohort (n = 38). In the training cohort, 106/150 patients completed a 2-year follow-up.

1.5T 3D multifrequency MRE with a single-shot spin-echo echo planar imaging sequence, and 3.0T multiparametric MRI (mp-MRI), consisting of diffusion-weighted echo planar imaging, T2-weighted fast spin echo, in-phase out-of-phase T1-weighted fast spoiled gradient-recalled dual-echo and dynamic contrast-enhanced gradient echo sequences.

Multivariable analysis was used to identify the independent predictors for MVI and RFS. Nomograms were constructed for visualization. Models for predicting MVI and RFS were built using mp-MRI parameters and a combination of mp-MRI and 3D MRE predictors.

Student's t-test, Mann-Whitney U test, chi-squared or Fisher's exact tests, multivariable analysis, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log rank tests. P < 0.05 was considered significant.

Tumor c and liver c were independent predictors of MVI and RFS, respectively. Adding tumor c significantly improved the diagnostic performance of mp-MRI (AUC increased from 0.70 to 0.87) for MVI detection. Of the 106 patients in the training cohort who completed the 2-year follow up, 34 experienced recurrence. RFS was shorter for patients with MVI-positive histology than MVI-negative histology (27.1 months vs. >40 months). The MVI predicted by the 3D MRE model yielded similar results (26.9 months vs. >40 months). The MVI and RFS nomograms of the histologic-MVI and model-predicted MVI-positive showed good predictive performance.

Biomechanical properties of 3D MRE were biomarkers for MVI and RFS. MVI and RFS nomograms were established.

2 TECHNICAL EFFICACY: Stage 2.

To examine the relationship between microvascular invasion (MVI) grading severity and long-term outcomes in early-stage hepatocellular carcinoma (HCC) patients undergoing laparoscopic liver resection (LLR).

Patients who had LLR for early-stage HCC were enrolled. According to the grading severity of MVI, patients were classified into M0, M1 and M2. Recurrence-free survival (RFS) and overall survival (OS) among the groups were compared. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors of OS and RFS.

Among 233 patients, MVI grading as M0, M1, and M2 accounts for 122 (52.4 ​%), 84 (36 ​%), and 27 (11.6 ​%) patients, respectively. The median OS and RFS in patients with M0, M1, and M2 were 84.9, 40.1, and 25.2 months; and 76.9, 27.0, and 18.8 months, respectively. Multivariable analyses identified both M1 and M2 to be independent risk factors for OS and RFS.

Grading severity of MVI was independently associated with RFS and OS after LLR for early-stage HCC. Patients with MVI, especially those with M2, should receive stringent recurrence surveillance and active adjuvant therapy.

To use Sonazoid contrast-enhanced ultrasound (S-CEUS) and Gadolinium-Ethoxybenzyl-Diethylenetriamine Penta-Acetic Acid magnetic-resonance imaging (EOB-MRI), exploring a non-invasive preoperative diagnostic strategy for microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

111 newly developed HCC cases were retrospectively collected. Both S-CEUS and EOB-MRI examinations were performed within one month of hepatectomy. The following indicators were investigated: size; vascularity in three phases of S-CEUS; margin, signal intensity, and peritumoral wedge shape in EOB-MRI; tumoral homogeneity, presence and integrity of the tumoral capsule in S-CEUS or EOB-MRI; presence of branching enhancement in S-CEUS; baseline clinical and serological data. The least absolute shrinkage and selection operator regression and multivariate logistic regression analysis were applied to optimize feature selection for the model. A nomogram for MVI was developed and verified by bootstrap resampling.

Of the 16 variables we included, wedge and margin in HBP of EOB-MRI, capsule integrity in AP or HBP/PVP images of EOB-MRI/S-CEUS, and branching enhancement in AP of S-CEUS were identified as independent risk factors for MVI and incorporated into construction of the nomogram. The nomogram achieved an excellent diagnostic efficiency with an area under the curve of 0.8434 for full data training set and 0.7925 for bootstrapping validation set for 500 repetitions. In evaluating the nomogram, Hosmer-Lemeshow test for training set exhibited a good model fit with P > 0.05. Decision curve analysis of nomogram model yielded excellent clinical net benefit with a wide range (5-80 % and 85-94 %) of risk threshold.

The MVI Nomogram established in this study may provide a strategy for optimizing the preoperative diagnosis of MVI, which in turn may improve the treatment and prognosis of MVI-related HCC.

To investigate associations between tissue diffusion, stiffness, and different tumor microenvironment features in resected hepatocellular carcinoma (HCC).

Seventy-two patients were prospectively included for preoperative magnetic resonance (MR) diffusion-weighted imaging and MR elastography examination. The mean apparent diffusion coefficient (ADC) and stiffness value were measured on the central three slices of the tumor and peri-tumor area. Cell density, tumor-stroma ratio (TSR), lymphocyte-rich HCC (LR-HCC), and CD8 + T cell infiltration were estimated in resected tumors. The interobserver agreement of MRI measurements and subjective pathological evaluation was assessed. Variables influencing ADC and stiffness were screened with univariate analyses, and then identified with multivariable linear regression. The potential relationship between explored imaging biomarkers and histopathological features was assessed with linear regression after adjustment for other influencing factors.

Seventy-two patients (male/female: 59/13, mean age: 56 ± 10.2 years) were included for analysis. Inter-reader agreement was good or excellent regarding MRI measurements and histopathological evaluation. No correlation between tumor ADC and tumor stiffness was found. Multivariable linear regression confirmed that cell density was the only factor associated with tumor ADC (Estimate = -0.03, p = 0.006), and tumor-stroma ratio was the only factor associated with tumor stiffness (Estimate = -0.18, p = 0.03). After adjustment for fibrosis stage (Estimate = 0.43, p < 0.001) and age (Estimate = 0.04, p < 0.001) in the multivariate linear regression, intra-tumoral CD8 + T cell infiltration remained a significant factor associated with peri-tumor stiffness (Estimate = 0.63, p = 0.02).

Tumor ADC surpasses tumor stiffness as a biomarker of cellularity. Tumor stiffness is associated with tumor-stroma ratio and peri-tumor stiffness might be an imaging biomarker of intra-tumoral immune microenvironment.

Tissue stiffness could potentially serve as an imaging biomarker of the intra-tumoral immune microenvironment of hepatocellular carcinoma and aid in patient selection for immunotherapy.

Apparent diffusion coefficient reflects cellularity of hepatocellular carcinoma. Tumor stiffness reflects tumor-stroma ratio of hepatocellular carcinoma and is associated with tumor-infiltrating lymphocytes. Tumor and peri-tumor stiffness might serve as imaging biomarkers of intra-tumoral immune microenvironment.

To develop and validate a preoperative prediction model based on multimodal ultrasound and biochemical indicator for identifying microvascular invasion (MVI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm.

From May 2022 to November 2023, a total of 318 patients with pathologically confirmed single HCC ≤ 5 cm from three institutions were enrolled. All of them underwent preoperative biochemical, conventional ultrasound (US), and contrast-enhanced ultrasound (CEUS) (Sonazoid, 0.6 mL, bolus injection) examinations. Univariate and multivariate logistic regression analyses on clinical information, biochemical indicator, and US imaging features were performed in the training set to seek independent predictors for MVI-positive. The models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis in both validation and test sets. Subgroup analyses in patients with different liver background and tumor sizes were conducted to further investigate the model's performance.

Logistic regression analyses showed that obscure tumor boundary in B-mode US, intra-tumoral artery in pulsed-wave Doppler US, complete Kupffer-phase agent clearance in Sonazoid-CEUS, and biomedical indicator PIVKA-II were independently correlated with MVI-positive. The combined model comprising all predictors showed the highest AUC, which were 0.937 and 0.893 in the validation and test sets. Good calibration and prominent net benefit were achieved in both sets. No significant difference was found in subgroup analyses.

The combination of biochemical indicator, conventional US, and Sonazoid-CEUS features could help preoperative MVI prediction in patients with a single HCC ≤ 5 cm.

Investigation of imaging features in conventional US, Sonazoid-CEUS, and biochemical indicators showed a significant relation with MVI-positivity in patients with a single HCC ≤ 5 cm, allowing the construction of a model for preoperative prediction of MVI status to help treatment decision making.

MVI status is important for patients with a single HCC ≤ 5 cm. The model based on conventional US, Sonazoid-CEUS and PIVKA-II performs best for MVI prediction. The combined model has potential for preoperative prediction of MVI status.

To explore clinical features and prognosis of hepatocellular carcinoma (HCC) in hepatitis B virus surface antigen (HBsAg)-serocleared patients and identify risk factors associated with postoperative recurrence after curative hepatectomy.

Patients who had undergone initial hepatectomy for HCC from January 2010 through December 2022. Clinicopathological data were compared between HBsAg-seropositive and HBsAg-serocleared patients. Furthermore, risk factors associated with early and late postoperative HCC recurrence (early and late recurrences (ER and LR), respectively) were analyzed for HBsAg-serocleared HCC patients treated by curative hepatectomy.

A total of 2184 consecutive patients undergoing initial hepatectomy for HCC were enrolled, including 339 (15.5%) HBsAg-serocleared and 1845 (84.5%) HBsAg-seropositive cases. Tumor characteristics were comparable between the two groups. After curative hepatectomy, the ER rate was lower in the HBsAg-serocleared group than in the HBsAg-seropositive group (16.2% vs 26.3%; p = 0.000). LR rates in the HBsAg-seropositive and HBsAg-serocleared groups were similar (8.3% vs 6.9%, respectively, p = 0.418). Multivariate analysis showed that among HBsAg-serocleared patients, Hong Kong Liver Cancer stage and microvascular invasion were risk factors associated with postoperative ER, while γ-glutamyl transferase level and neutrophil-to-lymphocyte ratio were associated with LR.

HBsAg-serocleared and HBsAg-seropositive HCC patients exhibited similar tumor characteristics. Curative hepatectomy-treated HBsAg-serocleared HCC patients experienced a lower ER rate and better short-term (⩽3 years) overall survival (OS) rates than their HBsAg-seropositive counterparts. LR, very late recurrence, and long-term (4-, and 5-year) OS rates were similar between the two groups.

Vessels Encapsulating Tumor Clusters (VETC) are now recognized as independent indicators of recurrence and overall survival in hepatocellular carcinoma (HCC) patients. However, there has been limited investigation into predicting the VETC pattern using hepatobiliary phase (HBP) features from preoperative gadobenate-enhanced MRI.

This study involved 252 HCC patients with confirmed VETC status from three different hospitals (Hospital 1: training set with 142 patients; Hospital 2: test set with 64 patients; Hospital 3: validation set with 46 patients). Independent predictive factors for VETC status were determined through univariate and multivariate logistic analyses. Subsequently, these factors were used to construct two distinct VETC prediction models. Model 1 included all independent predictive factors, while Model 2 excluded HBP features. The performance of both models was assessed using the Area Under the Curve (AUC), Decision Curve Analysis, and Calibration Curve. Prediction accuracy between the two models was compared using Net Reclassification Improvement (NRI) and Integrated Discriminant Improvement (IDI).

CA199, IBIL, shape, peritumoral hyperintensity on HBP, and arterial peritumoral enhancement were independent predictors of VETC. Model 1 showed robust predictive performance, with AUCs of 0.836 (training), 0.811 (test), and 0.802 (validation). Model 2 exhibited moderate performance, with AUCs of 0.813, 0.773, and 0.783 in the respective sets. Calibration and decision curves for both models indicated consistent predictions between predicted and actual VETC, benefiting HCC patients. NRI showed Model 1 increased by 0.326, 0.389, and 0.478 in the training, test, and validation sets compared to Model 2. IDI indicated Model 1 increased by 0.036, 0.028, and 0.025 in the training, test, and validation sets compared to Model 2.

HBP features from preoperative gadobenate-enhanced MRI can enhance the predictive performance of VETC in HCC.

Chemokines play a crucial role in the pathogenesis of patients with hepatocellular carcinoma (HCC). The expression levels of interferon-γ-induced protein-10 (CXCL10) and macrophage inflammatory protein-3α (MIP-3a) were investigated to clarify their clinical significance in HCC. The protein levels of CXCL10 and MIP-3a in the serum of 105 HBV-associated HCC patients, 50 patients with liver cirrhosis (LC), 50 patients with chronic hepatitis B (CHB) and 50 healthy donors (HC) were detected by liquid chip technology (Luminex) or ELISA. In addition, their mRNA levels were also determined in liver cancer and adjacent cancer tissue (paracancer; ParaCa) from 65 HCC patients. The online database UALCAN was used to analyze the association between CXCL10 and pathological manifestations of liver cancer. In addition, the diagnostic value of CXCL10/MIP-3a and AFP in HCC patients was determined by analyzing the Receiver Operating Characteristic Curve (ROC). The protein concentrations of CXCL10 and MIP-3a were significantly higher in the HCC group than in the LC, CHB and HC groups. CXCL10 in sera and liver cancer tissues is significantly positively correlated with ALT, but no significance between CXCL10 in ParaCa tissues and sera-ALT. Their mRNA is significantly higher in cancer tissues than in ParaCa tissues. The areas under the ROC curve of CXCL10, MIP-3a, CXCL10 and MIP-3a combined and AFP were 0.9169, 0.9261, 0.9299 and 0.7880, respectively. Elevated chemokines CXCL10 and MIP-3a in HCC patients may be associated with the clinical manifestation of HCC and could be a potential molecular marker for prognostic evaluation or a therapeutic target for HCC.

This study aims to evaluate and compare the predictive accuracy of Sonazoid-contrast-enhanced ultrasound (CEUS) and Gd-EOB-DTPA-enhanced MRI for detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

In this single-center prospective study, we included 64 patients with histopathologically confirmed single HCC lesions. Based on post-operative pathologic data, patients were categorized into two groups: those with MVI (n = 21) and those without MVI (n = 43). The diagnostic efficacy of CEUS was compared with that of MRI in predicting MVI.

Multifactorial analysis revealed that US features (tumor size > 4.35 cm, peritumoral enhancement, post-vascular ring enhancement, peak energy in the arterial phase of the difference between the margin area of HCC and distal liver parenchyma <-1.0 × 106 a.u), MRI features (rim enhancement, irregular tumor margin, and the halo sign) were all independent predictors of MVI (p < 0.05). The sensitivity and specificity of CEUS features in predicting MVI ranged from 61.9% to 86.4% and from 42.9% to 71.4%, respectively. For MRI features, the sensitivity and specificity ranged from 33.3% to 76.3% and from 54.7% to 90.5%, respectively. No statistically significant differences were observed in the area under the curve between CEUS and MRI (p > 0.05). Notably, peak energy of the difference showed the highest sensitivity at 86.4%, while the halo sign in MRI exhibited the highest specificity at 90.5%.

Sonazoid-CEUS and Gd-EOB-DTPA-enhanced MRI demonstrate potential in predicting MVI in HCC lesions. Notably, CEUS showed higher sensitivity, whereas MRI displayed greater specificity in predicting MVI.

Microvascular invasion (MVI) is a recognized biomarker associated with poorer prognosis in patients with hepatocellular carcinoma. Dual-energy computed tomography (DECT) is a highly sensitive technique that can determine the iodine concentration (IC) in tumour and provide an indirect evaluation of internal microcirculatory perfusion. This study aimed to assess whether the combination of DECT with laboratory data can improve preoperative MVI prediction.

This retrospective study enrolled 119 patients who underwent DECT liver angiography at 2 medical centres preoperatively. To compare DECT parameters and laboratory findings between MVI-negative and MVI-positive groups, Mann-Whitney U test was used. Additionally, principal component analysis (PCA) was conducted to determine fundamental components. Mann-Whitney U test was applied to determine whether the principal component (PC) scores varied across MVI groups. Finally, a general linear classifier was used to assess the classification ability of each PC score.

Significant differences were noted (P < .05) in alpha-fetoprotein (AFP) level, normalized arterial phase IC, and normalized portal phase IC between the MVI groups in the primary and validation datasets. The PC1-PC4 accounted for 67.9% of the variance in the primary dataset, with loadings of 24.1%, 16%, 15.4%, and 12.4%, respectively. In both primary and validation datasets, PC3 and PC4 were significantly different across MVI groups, with area under the curve values of 0.8410 and 0.8373, respectively.

The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively.

Utilizing PCA, the amalgamation of DECT IC and laboratory features, considering diverse factor loadings, showed substantial promise in accurately classifying MVI. There have been limited endeavours to establish such a combination, offering a novel paradigm for comprehending data in related research endeavours.

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.

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a critical pathological factor and the degree of MVI influences treatment decisions and patient prognosis. The present study aimed to predict the MVI classification based on preoperative MRI features and clinical parameters. The present retrospective cohort study included 150 patients (training cohort, n=108; validation cohort, n=42) with pathologically confirmed HCC. Clinical and imaging characteristics data were collected from Shengli Oilfield Central Hospital (Dongying, China). Univariate and multivariate logistic regression analyses were conducted to assess the association of clinical variables and MRI parameters with MVI (grade M1 and M2) and the M2 classification. Nomograms were developed based on the predictive factors of MVI and the M2 classification. The discrimination capability, calibration and clinical usefulness of the nomograms were evaluated. Multivariate analysis revealed an association between the Lens culinaris agglutinin-reactive fraction of α-fetoprotein, protein induced by vitamin K absence-II and tumor margin and MVI-positive status, while peritumoral enhancement and tumor size were demonstrated to be marginal predictors, but were also included in the nomogram. However, among MVI-positive patients, only peritumoral hypointensity and tumor size were demonstrated to be risk factors for the M2 classification. The nomograms, incorporating these variables, exhibited a strong ability to discriminate between MVI-positive and MVI-negative patients with HCC in both the training and validation cohort [area under the curve (AUC), 0.877 and 0.914, respectively] and good performance in predicting the M2 classification in the training and validation cohorts (AUC, 0.720 and 0.782, respectively). Nomograms incorporating clinical parameters and preoperative MRI features demonstrated promising potential as straightforward and effective tools for predicting MVI and the M2 classification in patients with HCC. Such predictive tools could aid in the judicious selection of optimal clinical treatments.

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.

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).

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).

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.

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.

To investigate the MRI manifestations of the spontaneous intratumoral coagulative necrosis (iCN) in patients with hepatocellular carcinoma (HCC) and its value in predicting the postoperative early recurrence (≤ 2 years).

Patients with HCC who underwent preoperative multiparametric MRI between January 2015 and February 2019 were enrolled in this retrospective study. The MRI manifestations of iCNs on TIWI, T2WI, and ADC were recorded. The sensitivity and specificity of MRI for the detection of iCNs were also evaluated. A multivariable Cox proportional hazards model and the Kaplan-Meier method were used to verify the value of histologically-confirmed and MRI-identified iCNs, respectively, in predicting early recurrence.

A total of 163 patients (median age, 56 years; interquartile range, 49-64 years; 139 men) with HCCs were evaluated, of whom 27(16.6%) had histologically-confirmed iCNs. MRI identified 92.6% (25 of 27; 95% confidence interval [CI] 74.2%, 98.7%) of iCNs (sensitivity), with a specificity of 79.4% (78 of 136; 95% CI 71.4%, 85.7%), based on non-enhancement on post-contrast MRI. And the MRI-identified iCNs were characterized by a similar appearance to surrounding tumour tissue shown on pre-contrast MRI but not enhanced on post-contrast MRI. The multivariable Cox proportional hazards model revealed that only the presence of histologically-confirmed iCN was independently associated with early HCC recurrence (hazard ratio = 2.73; 95% CI 1.20, 6.21; P = 0.017). The Kaplan-Meier curve showed that the presence of MRI-identified iCN was also associated with early recurrence (P < 0.001).

Multiparametric MRI identified iCNs with high sensitivity and modest specificity. The presence of iCNs is associated with early HCC recurrence.

This study aimed to determine the effectiveness of postoperative adjuvant lenvatinib + PD-1 blockade for patients with early-stage hepatocellular carcinoma (HCC) with microvascular invasion (MVI).

A total of 393 patients with HCC (Barcelona Clinic Liver Cancer stage 0 or A) who underwent curative hepatectomy with histopathologically proven MVI were enrolled according to the inclusion and exclusion criteria and assigned to 2 groups: surgery alone (surgery-alone group) and surgery with lenvatinib and PD-1 blockade (surgery + lenvatinib + PD-1 group) to compare recurrence-free survival (RFS), overall survival (OS), recurrence type, and annual recurrence rate after the application of propensity score matching (PSM). The Cox proportional hazards model was used for univariate and multivariate analyses.

Overall, 99 matched pairs were selected using PSM. Patients in the surgery + lenvatinib + PD-1 group had significantly higher 3-year RFS rates (76.8%, 65.7%, and 53.5%) than patients in the surgery-alone group (60.6%, 45.5%, and 37.4%) (P = .012). The 2 groups showed no significant difference in recurrence types and OS. Surgery alone, MVI-M2, and alpha-fetoprotein of ≥200 ng/mL were independent risk factors for RFS (P < .05), and history of alcohol use disorder was an independent risk factor for OS (P = .022).

Postoperative lenvatinib + PD-1 blockade improved the RFS in patients with HCC with MVI and was particularly beneficial for specific individuals.

Early recurrence (ER) is associated with poor prognosis in hepatocellular carcinoma (HCC). In this study, we developed and externally validated a nomogram based on the hemoglobin, albumin, lymphocytes, and platelets (HALP) score to predict ER for patients with BCLC stage 0/A HCC who underwent radical liver resection.

A total of 808 BCLC stage 0/A HCC patients from six hospitals were included in this study, and they were assigned to a training cohort (n = 500) and an external validation cohort (n = 308). We used univariate and multivariate Cox regression analysis to identify the independent risk factors for disease-free survival (DFS). We also established and externally validated a nomogram based on these risk predictors. The nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), the concordance index (C-index), the calibration curve, decision curve analysis (DCA), and Kaplan‒Meier analysis.

Multivariate COX regression showed that HBV DNA ≥10,000 IU/mL (P < 0.001), HALP score ≤38.20 (P < 0.001), tumor size (P = 0.003), clinically significant portal hypertension (P = 0.001), Edmondson-Steiner grade (III-IV) (P = 0.007), satellite nodules (P < 0.001), and MVI (P = 0.001) were independent risk factors for post-operative tumor recurrence. The AUC of our nomogram for predicting the 2-year and 5-year DFS was 0.756 and 0.750, respectively, in the training cohort and 0.764 and 0.705, respectively, in the external validation cohort. We divided the patients into low-, intermediate- and high-risk groups according to the risk score calculated by the nomogram. There were statistically significant differences in the DFS and overall survival (OS) among the three groups of patients (P < 0.001).

We developed and externally validated a new nomogram, which is accurate and can predict ER in BCLC stage 0/A HCC patients after curative liver resection.

Few studies have reported the integrated characteristics of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) after long-term antiviral therapy. This study aimed to investigate the HBV integration features in HBV-HCC patients who had undergone long-term antiviral therapy, evaluate their impact on clinical indicators, and analyze the potential mechanisms involved.

We utilized genome-wide association study (GWAS) to analyze liver cancer tissues and detect the presence of HBV integration. Seventeen patients with HBV integration were included in the integration (Int) group, while the remaining five patients were included in the non-integration (N-int) group. Clinical indicators were regularly monitored and compared between the two groups. The characteristics of HBV integration patterns were analyzed, and differences between the groups were explored at the chromosome and genomic levels.

After long-term antiviral therapy, although the frequency of HBV integration in HBV-HCC was reduced, residual HBV integration still accelerated the development of HCC. It affected the diagnosis, treatment, and prognosis of patients. HBV integration events led to changes in chromosome structure, which were closely related to HCC. Novel fusion genes were detected at a high frequency and had the potential to be specific detection sites for HBV-HCC.

HBV integration events are synergistically involved in the human genome and HBV, which can lead to chromosome structural instability, gene rearrangement events closely related to HCC production, and the formation of new specific fusion genes.