Search

Objective To investigate the diagnostic value of quantitative parameters of dual-energy CT (DECT) in the microvascular invasion of HCC. Methods Clinicopathological and imaging data of 56 patients, who underwent preoperative DECT for pathologically confirmed HCC were retrospectively analyzed. The iodine densities (ID) of solid part of the lesion, edge of the lesion, within 1 cm of the lesion edge, 1 cm outside the lesion, and abdominal aorta on the same plane in the arterial and venous phases were measured. According to Standardization for Diagnosis and Treatment of Primary Hepatic Carcinoma (2019 edition), all 56 patients were classified as 28 microvascular invasion (MVI) or 28 non-MVI. The parameters of DECT were compared between the two groups. The receiver operating characteristic (ROC) curves were drawn to evaluate the diagnostic efficacy of any statistically significant quantitative parameters in the MVI of HCC. Results There were no significant differences in the ID of outside 1 cm of the lesion in the arterial phase and solid part of the lesion, edge of the lesion, within 1 cm of the lesion in venous phase (P>0.05). There was no significant difference (P> 0.05) between the ID of solid part of the lesion in arterial phase (AUC=0.756) and outside 1 cm edge the lesion in venous phase (0.694). Combined with age, alpha-fetoprotein (AFP), and aspartate aminotransferase (AST), ID of solid part of the lesion in arterial phase and ID of outside 1 cm of the lesion in the venous phase had AUC of 0.926 with sensitivity of 82.14%, and specificity of 96.43%. Conclusion The ID of solid part of the HCC in arterial phase and ID of outside 1 cm of the lesion in the venous phase on DECT can aid preoperative evaluation of HCC microvascular invasion. [ABSTRACT FROM AUTHOR]

Objective To investigate the value of the contrast enhanced ultrasound (CEUS) combined with immunohistochemistry (IHC) in preoperative diagnosis of microvascular invasion (MVI) of small hepatocellular carcinoma (HCC). Methods The data of 142 HCC patients, including 177 newly developed HCC lesions with a maximum diameter of no more than 3 cm were retrospectively collected from the Yokohama City University Medical Center of Japan and the First Affiliated Hospital of Xi'an Jiaotong University. According to the pathological diagnosis, the patients were divided into MVI (+) group (n=37) and MVI (-) group (n=140). Preoperative CEUS arterial phase (AP) and post ⁃ vascular phase (PVP) images were used to observe whether there was hypervascularity and hypovascularity around the lesion. The expressions of heat shock protein 70 (HSP70) and glypican 3 (GPC3) in preoperative biopsy samples were detected by IHC method. Results When all CEUS indicators (AP, PVP) and IHC indicators (GPC3, HSP70) were diagnosed MVI individually, PVP had the highest diagnostic efficiency, with an accuracy of 91.0% and an area under the curve (AUC) of 0.893. The diagnostic efficiency of GPC3 combined with HSP70 (accuracy: 79.8%, AUC: 0.790) was better than that of GPC3 alone (accuracy: 69.5%, AUC: 0.752) or HSP70 alone (accuracy: 60.7%, AUC: 0.701). The combined diagnosis of PVP+GPC3+HSP70 had the best diagnostic efficiency, with a specificity of 98.5%, an accuracy of 92.5%, and an AUC of 0.840. Conclusions Observing the PVP of CEUS and using IHC to detect the expressions of GPC3 and HSP70 can better diagnose the MVI of small HCC preoperatively. [ABSTRACT FROM AUTHOR]

Background: Microvascular invasion (MVI) is a major risk factor for early recurrence in patients with hepatocellular carcinoma (HCC). Preoperative accurate evaluation of the presence of MVI could enormously benefit its treatment and prognosis. Purpose: To evaluate and compare the diagnostic performance of two imaging features (non-smooth tumor margin and peritumor hypointensity) in the hepatobiliary phase (HBP) to preoperatively diagnose the presence of MVI in HCC. Material and Methods: Original articles were collected from Medline/PubMed, Web of Science, EMBASE, and the Cochrane Library up to 17 January 2021 linked to gadoxetate disodium–enhanced magnetic resonance imaging (MRI) on 1.5 or 3.0 T. The pooled sensitivity, specificity, and summary area under the receiver operating characteristic curve (AUC) were calculated and meta-regression analyses were performed. Results: A total of 14 original articles involving 2193 HCCs were included. The pooled sensitivity and specificity of non-smooth tumor margin and peritumor hypointensity were 73% and 61%, and 43% and 90%, respectively, for the diagnosis of MVI in HCC. The summary AUC of non-smooth tumor margin (0.74) was comparable to that of peritumor hypointensity (0.76) (z = 0.693, P = 0.488). The meta-regression analysis identified four covariates as possible sources of heterogeneity: average size; time interval between index test and reference test; blindness to index test during reference test; and risk of bias score. Conclusion: This meta-analysis showed moderate and comparable accuracy for predicting MVI in HCC using either non-smooth tumor margin or peritumor hypointensity in HBP. Four discovered covariates accounted for the heterogeneity. [ABSTRACT FROM AUTHOR]

Introduction: Microvascular invasion (MVI) is a known risk factor for prognosis after R0 liver resection for hepatocellular carcinoma (HCC). The aim of this study was to develop a deep learning prognostic prediction model by incorporating a new factor of MVI area to the other independent risk factors. Methods: Consecutive patients with HCC who underwent R0 liver resection from January to December 2016 at the Eastern Hepatobiliary Surgery Hospital were included in this retrospective study. For patients with MVI detected on resected specimens, they were divided into two groups according to the size of the maximal MVI area: the small-MVI group and the large-MVI group. Results: Of 193 patients who had MVI in the 337 HCC patients, 130 patients formed the training cohort and 63 patients formed the validation cohort. The large-MVI group of patients had worse overall survival (OS) when compared with the small-MVI group (p = 0.009). A deep learning model was developed based on the following independent risk factors found in this study: MVI stage, maximal MVI area, presence/absence of cirrhosis, and maximal tumor diameter. The areas under the receiver operating characteristic of the deep learning model for the 1-, 3-, and 5-year predictions of OS were 80.65, 74.04, and 79.44, respectively, which outperformed the traditional COX proportional hazards model. Conclusion: The deep learning model, by incorporating the maximal MVI area as an additional prognostic factor to the other previously known independent risk factors, predicted more accurately postoperative long-term OS for HCC patients with MVI after R0 liver resection. [ABSTRACT FROM AUTHOR]

Background: Assessment of microvascular invasion (MVI) in intrahepatic cholangiocarcinoma (ICC) by using a noninvasive method is an unresolved issue. Deep learning (DL) methods based on multiparametric fusion of MR images have the potential of preoperative assessment of MVI. Purpose: To investigate whether a multiparametric fusion DL model based on MR images can be used for preoperative assessment of MVI in ICC. Study type: Retrospective. Population: A total of 519 patients (200 females and 319 males) with a single ICC were categorized as a training (n = 361), validation (n = 90), and an external test cohort (n = 68). Field strength/Sequence: A 1.5 T and 3.0 T; axial T2‐weighted turbo spin‐echo sequence, diffusion‐weighted imaging with a single‐shot spin‐echo planar sequence, and dynamic contrast‐enhanced (DCE) imaging with T1‐weighted three‐dimensional quick spoiled gradient echo sequence. Assessment: DL models of multiparametric fusion convolutional neural network (CNN) and late fusion CNN were both constructed for evaluating MVI in ICC. Gradient‐weighted class activation mapping was used for visual interpretation of MVI status in ICC. Statistical Tests: The DL model performance was assessed through the receiver operating characteristic curve (ROC) analysis, and the area under the ROC curve (AUC) with the accuracy, sensitivity, and specificity were measured. P value < 0.05 was considered as statistical significance. Results: In the external test cohort, the proposed multiparametric fusion DL model achieved an AUC of 0.888 with an accuracy of 86.8%, sensitivity of 85.7%, and specificity of 87.0% for evaluating MVI in ICC, and the positive predictive value and negative predictive value were 63.2% and 95.9%, respectively. The late fusion DL model achieved a lower AUC of 0.866, with an accuracy of 83.8%, sensitivity of 78.6%, specificity of 85.2% for evaluating MVI in ICC. Data Conclusion: Our DL model based on multiparametric fusion of MRI achieved a good diagnostic performance in the evaluation of MVI in ICC. Level of Evidence: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is the third most common cause of cancer death worldwide, with an overall 5-year survival rate of less than 18%, which may be related to tumor microvascular invasion (MVI). This study aimed to compare the clinical prognosis of HCC patients with or without MVI after radical surgical treatment, and further analyze the preoperative risk factors related to MVI to promote the development of a new treatment strategy for HCC. Methods: According to the postoperative pathological diagnosis of MVI, 160 study patients undergoing radical hepatectomy were divided into an MVI-negative group (n = 68) and an MVI-positive group (n = 92). The clinical outcomes and prognosis were compared between the two groups, and then the parameters were analyzed by multivariate logistic regression to construct an MVI prediction model. Then, the practicability and validity of the model were evaluated, and the clinical prognosis of different MVI risk groups was subsequently compared. Result: There were no significant differences between the MVI-negative and MVI-positive groups in clinical baseline, hematological, or imaging data. Additionally, the clinical outcome comparison between the two groups presented no significant differences except for the pathological grading (P = 0.002) and survival and recurrence rates after surgery (P < 0.001). The MVI prediction model, based on preoperative AFP, tumor diameter, and TNM stage, presented superior predictive efficacy (AUC = 0.7997) and good practicability (high H-L goodness of fit, P = 0.231). Compared with the MVI high-risk group, the patients in the MVI low-risk group had a higher survival rate (P = 0.002) and a lower recurrence rate (P = 0.004). Conclusion: MVI is an independent risk factor for a poor prognosis after radical resection of HCC. The MVI prediction model, consisting of AFP, tumor diameter, and TNM stage, exhibits superior predictive efficacy and strong clinical practicability for MVI prediction and prognostication, which provides a new therapeutic strategy for the standardized treatment of HCC patients. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) patients with portal vein tumor thrombus (PVTT) have conventionally been regarded as a contraindication for liver transplantation (LT). However, the outcomes of deceased donor liver transplantation (DDLT) in patients with segmental PVTT remain unknown. The aim of this study is to evaluate the feasibility and effectiveness of DDLT in the treatment of HCC with segmental PVTT. Methods: We retrospectively analyzed 254 patients who underwent DDLT for HCC in our institution from January 2015 to November 2019. To assess the risks of PVTT, various clinicopathological variables were evaluated. Overall (OS) and recurrence-free survival (RFS) analyses based on different PVTT types were performed in HCC patients. Results: Of the 254 patients, a total of 46 patients had PVTT, of whom 35 had lobar PVTT and 11 had segmental PVTT in second-order branches or below. Alphafetoprotein (AFP) level, tumor maximal diameter, histological grade, microvascular invasion (MVI), RFS, and OS were significantly different between the control and PVTT groups. Lobar PVTT was associated with unfavorable 5-year RFS and OS compared with MVI group (28.6% and 17.1%, respectively). Instead, no significant differencewas observed between the segmental PVTT andMVI group in terms of 5-year RFS and OS (RFS: 36.4% vs. 40.4%, p=0.667; OS: 54.5% vs. 45.1%, p=0.395). Further subgroup analysis showed segmental PVTT with AFP levels =100 ng/ml presented significantly favorable RFS and OS rates than those with AFP level >100 ng/ml (p=0.050 and 0.035, respectively). Conclusions: In summary, lobar PVTT remains a contraindication to DDLT. HCC patients with segmental PVTT and AFP level =100 ng/ml may be acceptable candidates for DDLT. [ABSTRACT FROM AUTHOR]

Background: The goal is to establish and validate an innovative prognostic risk stratification and nomogram in patients of hepatocellular carcinoma (HCC) with microvascular invasion (MVI) for predicting the cancer-specific survival (CSS). Methods: 1487 qualified patients were selected from the Surveillance, Epidemiology and End Results (SEER) database and randomly assigned to the training cohort and validation cohort in a ratio of 7:3. Concordance index (Cindex), area under curve (AUC) and calibration plots were adopted to evaluate the discrimination and calibration of the nomogram. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities and compare it to the American Joint Committee on Cancer (AJCC) tumor staging system. C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate the improvement of the new model over the AJCC tumor staging system. The new risk stratifications based on the nomogram and the AJCC tumor staging system were compared. Results: Eight prognostic factors were used to construct the nomogram for HCC patients with MVI. The C-index for the training and validation cohorts was 0.785 and 0.776 respectively. The AUC values were higher than 0.7 both in the training cohort and validation cohort. The calibration plots showed good consistency between the actual observation and the nomogram prediction. The IDI values of 1-, 3-, 5-year CSS in the training cohort were 0.17, 0.16, 0.15, and in the validation cohort were 0.17, 0.17, 0.17 (P<0.05). The NRI values of the training cohort were 0.75 at 1-year, 0.68 at 3-year and 0.67 at 5-year. The DCA curves indicated that the new model more accurately predicted 1-year, 3-year, and 5-year CSS in both training and validation cohort, because it added more net benefit than the AJCC staging system. Furthermore, the risk stratification system showed the CSS in different groups had a good regional division. Conclusions: A comprehensive risk stratification system and nomogram were established to forecast CSS for patients of HCC with MVI. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to investigate the value of performing gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) radiomics for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on multiple sequences. Methods: We randomly allocated 165 patients with HCC who underwent partial hepatectomy to training and validation sets. Stepwise regression and the least absolute shrinkage and selection operator algorithm were used to select significant variables. A clinicoradiological model, radiomics model, and combined model were constructed using multivariate logistic regression. The performance of the models was evaluated, and a nomogram risk-prediction model was built based on the combined model. A concordance index and calibration curve were used to evaluate the discrimination and calibration of the nomogram model. Results: The tumour margin, peritumoural hypointensity, and seven radiomics features were selected to build the combined model. The combined model outperformed the radiomics model and the clinicoradiological model and had the highest sensitivity (90.89%) in the validation set. The areas under the receiver operating characteristic curve were 0.826, 0.755, and 0.708 for the combined, radiomics, and clinicoradiological models, respectively. The nomogram model based on the combined model exhibited good discrimination (concordance index = 0.79) and calibration. Conclusions: The combined model based on radiomics features of Gd-EOB-DTPA enhanced MRI, tumour margin, and peritumoural hypointensity was valuable for predicting HCC microvascular invasion. The nomogram based on the combined model can intuitively show the probabilities of MVI. [ABSTRACT FROM AUTHOR]

Purpose: The present study aimed to develop and validate a preoperative model based on gadobenate-enhanced magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) size of ≤5 cm. In order to provide preoperative guidance for clinicians to optimize treatment options. Methods: 164 patients with pathologically confirmed HCC and preoperative gadobenate-enhanced MRI from July 2016 to December 2020 were retrospectively included. Univariate and multivariate logistic regression (forward LR) analyses were used to determine the predictors of MVI and the model was established. Four-fold cross validation was used to verify the model, which was visualized by nomograms. The predictive performance of the model was evaluated based on discrimination, calibration, and clinical utility. Results: Elevated alpha-fetoprotein (HR 1.849, 95% CI: 1.193, 2.867, P=0.006), atypical enhancement pattern (HR 3.441, 95% CI: 1.523, 7.772, P=0.003), peritumoral hypointensity on HBP (HR 7.822, 95% CI: 3.317, 18.445, P<0.001), and HBP hypointensity (HR 3.258, 95% CI: 1.381, 7.687, P=0.007) were independent risk factors to MVI and constituted the HBP model. The mean area under the curve (AUC), sensitivity, specificity, and accuracy values for the HBP model were as follows: 0.830 (95% CI: 0.784, 0.876), 0.71, 0.78, 0.81 in training set; 0.826 (95% CI:0.765, 0.887), 0.8, 0.7, 0.79 in test set. The decision curve analysis (DCA) curve showed that the HBP model achieved great clinical benefits. Conclusion: In conclusion, the HBP imaging features of Gd-BOPTA-enhanced MRI play an important role in predicting MVI for HCC. A preoperative model, mainly based on HBP imaging features of gadobenate-enhanced MRI, was able to excellently predict the MVI for HCC size of ≤5cm. The model may help clinicians preoperatively assess the risk of MVI in HCC patients so as to guide clinicians to optimize treatment options. [ABSTRACT FROM AUTHOR]

Objective: To investigate the role of prediction microvascular invasion (mVI) in hepatocellular carcinoma (HCC) by 18F-FDG PET image texture analysis and hybrid criteria combining PET/CT and multi-parameter MRI. Materials and methods: Ninety-seven patients with HCC who received the examinations of MRI and 18F-FDG PET/CT were retrospectively included in this study and were randomized into training and testing cohorts. The lesion image texture features of 18F-FDG PET were extracted using MaZda software. The optimal predictive texture features of mVI were selected, and the classification procedure was conducted. The predictive performance of mVI by radiomics classier in training and testing cohorts was respectively recorded. Next, the hybrid model was developed by integrating the 18F-FDG PET image texture, metabolic parameters, and MRI parameters to predict mVI through logistic regression. Furthermore, the diagnostic performance of each time was recorded. Results: The 18F-FDG PET image radiomics classier showed good predicted performance in both training and testing cohorts to discriminate HCC with/without mVI, with an AUC of 0.917 (95% CI: 0.824-0.970) and 0.771 (95% CI: 0.578, 0.905). The hybrid model, which combines radiomics classier, SUVmax, ADC, hypovascular arterial phase enhancement pattern on contrast-enhanced MRI, and non-smooth tumor margin, also yielded better predictive performance with an AUC of 0.996 (95% CI: 0.939, 1.000) and 0.953 (95% CI: 0.883, 1.000). The differences in AUCs between radiomics classier and hybrid classier were significant in both training and testing cohorts (DeLong test, both p < 0.05). Conclusion: The radiomics classier based on 18F-FDG PET image texture and the hybrid classier incorporating 18F-FDG PET/CT and MRI yielded good predictive performance, which might provide a precise prediction of HCC mVI preoperatively. [ABSTRACT FROM AUTHOR]

Background and Aims: To investigate the effect of postoperative adjuvant antiviral therapy (AVT) on hepatitis B virus (HBV) related hepatocellular carcinoma (HCC) with microvascular invasion (MVI) after R0 liver resection. Methods: A total of 1008 patients with HBV-related HCC with MVI were recruited, which comprises 378 non-AVT groups and 630 AVT groups. Propensity score matching (PSM) was developed to reduce any bias in patient selection. Independent risk factors were identified by Cox regression analysis. Results: After PSM, the 1-, 3-, and 5-year overall survival rates in the AVT group and non-AVT group were 89.2%, 62.4%, 42.1%, and 73.3%, 46.3%, 22.1%, (p < 0.01), respectively. The 1-, 3-, and 5-year recurrence-free survival rates in the AVT group and non-AVT group were 52.5%, 30.4%, 22.1%, and 46.3%, 26.8%, 13.2% (p = 0.02), respectively. Multivariate Cox analysis revealed that postoperative adjuvant AVT was the independent protective factor associated with mortality (HR = 0.55, 95%CI = 0.46–0.67, p < 0.01) and tumor recurrence (HR = 0.81, 95%CI = 0.69–0.96, p = 0.01). Conclusions: Among patients who underwent curative hepatectomy for HBV-related HCC with MVI, postoperative adjuvant AVT was the independent protective factor associated with mortality and tumor recurrence. Given the high rate of postoperative recurrence and poor prognosis of HBV-related HCC with MVI, our findings may have useful clinical significance in the prevention of tumor recurrence in these patients. [ABSTRACT FROM AUTHOR]

Purpose: To analyze the long-term oncological outcomes of Barcelona Clinic Liver Cancer (BCLC) stages 0–A hepatocellular carcinoma (HCC) patients associated with or without microvascular invasion (MVI) treated with laparoscopic versus laparotomic liver resection. Methods: Clinicopathological data of HCC patients with BCLC stages 0–A from four medical centers were retrospectively reviewed. The survival outcomes of patients who underwent laparoscopic hepatectomy were compared with those who underwent laparotomic hepatectomy. Subgroup analyses in terms of MVI were further performed to explore the effect of surgical approaches on the long-term survival outcomes. Propensity score matching (PSM) analysis was used to match patients between the laparoscopic and laparotomic resection groups in a 1:1 ratio. Results: 495 HCC patients at BCLC stages 0–A were enrolled, including 243 in the laparoscopic resection group and 252 in the laparotomic resection group. Laparoscopic resection group had a shorter operation time, less blood loss, a lower frequency of blood transfusion and postoperative complication rates. The laparoscopic resection group had a significantly better overall survival (OS) and recurrence-free survival (RFS) than the laparotomic resection group before and after PSM. Subgroup analysis demonstrated that OS and RFS of patients without MVI were remarkably better in the laparoscopic resection group compared with the laparotomic resection group. However, no significant differences in OS and RFS between the two groups were found in patients with MVI after PSM. Conclusions: Pure laparoscopic hepatectomy for patients with BCLC stages 0–A HCC can be performed safely with favorable perioperative and long-term oncological outcomes at high-volume liver cancer centers, regardless of the presence of MVI. [ABSTRACT FROM AUTHOR]

Purpose: To conduct a head-to-head comparison between deep learning (DL) and radiomics models across institutions for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and to investigate the model robustness and generalizability through rigorous internal and external validation., Methods: This retrospective study included 2304 preoperative images of 576 HCC lesions from two centers, with MVI status determined by postoperative histopathology. We developed DL and radiomics models for predicting the presence of MVI using B-mode ultrasound, contrast-enhanced ultrasound (CEUS) at the arterial, portal, and delayed phases, and a combined modality (B + CEUS). For radiomics, we constructed models with enlarged vs. original regions of interest (ROIs). A cross-validation approach was performed by training models on one center's dataset and validating the other, and vice versa. This allowed assessment of the validity of different ultrasound modalities and the cross-center robustness of the models. The optimal model combined with alpha-fetoprotein (AFP) was also validated. The head-to-head comparison was based on the area under the receiver operating characteristic curve (AUC)., Results: Thirteen DL models and 25 radiomics models using different ultrasound modalities were constructed and compared. B + CEUS was the optimal modality for both DL and radiomics models. The DL model achieved AUCs of 0.802-0.818 internally and 0.667-0.688 externally across the two centers, whereas radiomics achieved AUCs of 0.749-0.869 internally and 0.646-0.697 externally. The radiomics models showed overall improvement with enlarged ROIs (P < 0.05 for both CEUS and B + CEUS modalities). The DL models showed good cross-institutional robustness (P > 0.05 for all modalities, 1.6-2.1% differences in AUC for the optimal modality), whereas the radiomics models had relatively limited robustness across the two centers (12% drop-off in AUC for the optimal modality). Adding AFP improved the DL models (P < 0.05 externally) and well maintained the robustness, but did not benefit the radiomics model (P > 0.05)., Conclusion: Cross-institutional validation indicated that DL demonstrated better robustness than radiomics for preoperative MVI prediction in patients with HCC, representing a promising solution to non-standardized ultrasound examination procedures., (© 2024. The Author(s).)

Purpose: 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)., Methods: 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., Results: 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., Conclusion: 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., Competing Interests: Declaration of competing interest The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: 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., Materials and Methods: 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., Results: 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., Conclusion: 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhong, Chen, Ding, Ou, Zhang and Weng.)

Background: Microvascular invasion (MVI) is associated with an unfavorable prognosis and early recurrence of hepatocellular carcinoma (HCC), which is the crucial pathological hallmark of immunotherapy. While microvascular invasion (MVI) in hepatocellular carcinoma (HCC) currently lacks a detailed single-cell analysis of the tumor microenvironment (TME), it holds significant promise for immunotherapy using immune checkpoint inhibitors (ICI)., Methods: We performed single-cell RNA sequencing (scRNA-seq) on 3 MVI positive (MVIP) and 14 MVI-negative (MVIN) tumor tissues, as well as their paired adjacent non-tumoral tissues., Results: We identified SPP1 + macrophages and CD4 + proliferative T cells as intertumoral populations critical for the formation of cold tumors and immunosuppressive environments in MVI-positive patients and verified their prognostic value in correlation with MVIP HCC patients. Additionally, we identified SPP1 + dominated interactions between SPP1 + macrophages and the immunosuppressive T population as contributors to MVI destruction and tumorigenesis., Conclusions: We provide a comprehensive single-cell atlas of HCC patients with MVI, shedding light on the immunosuppressive ecosystem and upregulated signaling associated with MVI. These findings demonstrate that intercellular mechanisms drive MVI and provide a potential immunotherapeutic target for HCC patients with HCC and underlying MVI., Competing Interests: All authors disclosed no relevant relationships., (© 2024 The Authors. Published by Elsevier Ltd.)

Background and Aim: Accurately predicting microvascular invasion (MVI) before surgery is beneficial for surgical decision-making, and some high-risk hepatocellular carcinoma (HCC) patients may benefit from postoperative adjuvant transarterial chemoembolization (PA-TACE). The purpose of this study was to develop and validate a novel nomogram for predicting MVI and assessing the survival benefits of selectively receiving PA-TACE in HCC patients., Methods: The 1372 HCC patients who underwent hepatectomy at four medical institutions were randomly divided into training and validation datasets according to a 7:3 ratio. We developed and validated a nomogram for predicting MVI using preoperative clinical data and further evaluated the survival benefits of selective PA-TACE in different risk subgroups., Results: The nomogram for predicting MVI integrated alpha-fetoprotein, tumor diameter, tumor number, and tumor margin, with an area under the curve of 0.724, which was greater than that of any single predictive factor. The calibration curve, decision curve, and clinical impact curve demonstrated that the nomogram had strong predictive performance. Risk stratification based on the nomogram revealed that patients in the low-risk group did not achieve better DFS and OS with PA-TACE (all p > 0.05), while patients in the medium-to-high risk groups could benefit from higher DFS (Medium-risk, p = 0.039; High-risk, p = 0.027) and OS (Medium-risk, p = 0.001; High-risk, p = 0.019) with PA-TACE., Conclusions: The nomogram predicting MVI demonstrated strong predictive performance, and its risk stratification aided in identifying different subgroups of HCC patients who may benefit from PA-TACE with improved survival outcomes., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: Hepatocellular carcinoma (HCC) recurrence is highly correlated with increased mortality. Microvascular invasion (MVI) is indicative of aggressive tumor biology in HCC., Aim: To construct an artificial neural network (ANN) capable of accurately predicting MVI presence in HCC using magnetic resonance imaging., Methods: This study included 255 patients with HCC with tumors < 3 cm. Radiologists annotated the tumors on the T1-weighted plain MR images. Subsequently, a three-layer ANN was constructed using image features as inputs to predict MVI status in patients with HCC. Postoperative pathological examination is considered the gold standard for determining MVI. Receiver operating characteristic analysis was used to evaluate the effectiveness of the algorithm., Results: Using the bagging strategy to vote for 50 classifier classification results, a prediction model yielded an area under the curve (AUC) of 0.79. Moreover, correlation analysis revealed that alpha-fetoprotein values and tumor volume were not significantly correlated with the occurrence of MVI, whereas tumor sphericity was significantly correlated with MVI ( P < 0.01)., Conclusion: Analysis of variable correlations regarding MVI in tumors with diameters < 3 cm should prioritize tumor sphericity. The ANN model demonstrated strong predictive MVI for patients with HCC (AUC = 0.79)., Competing Interests: Conflict-of-interest statement: All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)

Objective: To investigate the value of the combined application of intravoxel incoherent motion (IVIM) and enhanced T2*-weighted angiography (ESWAN) for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC)., Materials and Methods: 76 patients with pathologically confirmed HCC were retrospectively enrolled and divided into the MVI-positive group (n=26) and MVI-negative group (n=50). Conventional MRI, IVIM, and ESWAN sequences were performed. Three region of interests (ROIs) were placed on the maximum axial slice of the lesion on D, D*, and f maps derived from IVIM sequence, and R2* map derived from ESWAN sequence, and intratumoral susceptibility signal (ITSS) from the phase map derived from ESWAN sequence was also automatically measured. Receiver operating characteristic (ROC) curves were drawn to evaluate the ability for predicting MVI. Univariate and multivariate logistic regression were used to screen independent risk predictors in clinical and imaging information. The Delong's test was used to compare the differences between the area under curves (AUCs)., Results: The D and D* values of MVI-negative group were significantly higher than those of MVI-positive group ( P =0.038, and P =0.023), which in MVI-negative group were 0.892×10 -3 (0.760×10 -3 , 1.303×10 -3 ) mm 2 /s and 0.055 (0.025, 0.100) mm 2 /s, and in MVI-positive group were 0.591×10 -3 (0.372×10 -3 , 0.824×10 -3 ) mm 2 /s and 0.028 (0.006, 0.050)mm 2 /s, respectively. The R2* and ITSS values of MVI-negative group were significantly lower than those of MVI-positive group ( P =0.034, and P =0.005), which in MVI-negative group were 29.290 (23.117, 35.228) Hz and 0.146 (0.086, 0.236), and in MVI-positive group were 43.696 (34.914, 58.083) Hz and 0.199 (0.155, 0.245), respectively. After univariate and multivariate analyses, only AFP (odds ratio, 0.183; 95% CI, 0.041-0.823; P = 0.027) was the independent risk factor for predicting the status of MVI. The AUCs of AFP, D, D*, R2*, and ITSS for prediction of MVI were 0.652, 0.739, 0.707, 0.798, and 0.657, respectively. The AUCs of IVIM (D+D*), ESWAN (R2*+ITSS), and combination (D+D*+R2*+ITSS) for predicting MVI were 0.772, 0.800, and, 0.855, respectively. When IVIM combined with ESWAN, the performance was improved with a sensitivity of 73.1% and a specificity of 92.0% (cut-off value: 0.502) and the AUC was significantly higher than AFP ( P =0.001), D ( P =0.038), D* ( P =0.023), R2* ( P =0.034), and ITSS ( P =0.005)., Conclusion: The IVIM and ESWAN parameters showed good efficacy in prediction of MVI in patients with HCC. The combination of IVIM and ESWAN may be useful for noninvasive prediction of MVI before clinical operation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ren, Zhao, Wang, Liu, Zhang, Zhuang, Wang, Wang, Zhang, Song and Liu.)

Background: Microvascular invasion (MVI) stands as a pivotal pathological hallmark of hepatocellular carcinoma (HCC), closely linked to unfavorable prognosis, early recurrence, and metastatic progression. However, the precise mechanistic underpinnings governing its onset and advancement remain elusive., Methods: In this research, we downloaded bulk RNA-seq data from the TCGA and HCCDB repositories, single-cell RNA-seq data from the GEO database, and spatial transcriptomics data from the CNCB database. Leveraging the Scissor algorithm, we delineated prognosis-related cell subpopulations and discerned a distinct MVI-related malignant cell subtype. A comprehensive exploration of these malignant cell subpopulations was undertaken through pseudotime analysis and cell-cell communication scrutiny. Furthermore, we engineered a prognostic model grounded in MVI-related genes, employing 101 algorithm combinations integrated by 10 machine-learning algorithms on the TCGA training set. Rigorous evaluation ensued on internal testing sets and external validation sets, employing C-index, calibration curves, and decision curve analysis (DCA)., Results: Pseudotime analysis indicated that malignant cells, showing a positive correlation with MVI, were primarily concentrated in the early to middle stages of differentiation, correlating with an unfavorable prognosis. Importantly, these cells showed significant enrichment in the MYC pathway and were involved in extensive interactions with diverse cell types via the MIF signaling pathway. The association of malignant cells with the MVI phenotype was corroborated through validation in spatial transcriptomics data. The prognostic model we devised demonstrated exceptional sensitivity and specificity, surpassing the performance of most previously published models. Calibration curves and DCA underscored the clinical utility of this model., Conclusions: Through integrated multi-transcriptomics analysis, we delineated MVI-related malignant cells and elucidated their biological functions. This study provided novel insights for managing HCC, with the constructed prognostic model offering valuable support for clinical decision-making., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Huang, Wu, Yu, Xu, Chen, Huo and Li.)

Background: The accurate identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance., Purpose: To develop a radiomics nomogram based on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) for predicting MVI in early-stage (Barcelona Clinic Liver Cancer stages 0 and A) HCC patients., Materials and Methods: A prospective cohort of 189 participants with HCC was included for model training and testing, and an additional 34 participants were enrolled for external validation. ITK-SNAP was used to manually segment the tumour, and PyRadiomics was used to extract radiomic features from the SWI and T2W images. Variance filtering, student's t test, least absolute shrinkage and selection operator regression and random forest (RF) were applied to select meaningful features. Four machine learning classifiers, including K-nearest neighbour, RF, logistic regression and support vector machine-based models, were established. Independent clinical and radiological risk factors were also determined to establish a clinical model. The best radiomics and clinical models were further evaluated in the validation set. In addition, a nomogram was constructed from the radiomic model and independent clinical factors. Diagnostic efficacy was evaluated by receiver operating characteristic curve analysis with fivefold cross-validation., Results: AFP levels greater than 400 ng/mL [odds ratio (OR) 2.50; 95% confidence interval (CI) 1.239-5.047], tumour diameter greater than 5 cm (OR 2.39; 95% CI 1.178-4.839), and absence of pseudocapsule (OR 2.053; 95% CI 1.007-4.202) were found to be independent risk factors for MVI. The areas under the curve (AUCs) of the best radiomic model were 1.000 and 0.882 in the training and testing cohorts, respectively, while those of the clinical model were 0.688 and 0.6691. In the validation set, the radiomic model achieved better diagnostic performance (AUC = 0.888) than the clinical model (AUC = 0.602). The combination of clinical factors and the radiomic model yielded a nomogram with the best diagnostic performance (AUC = 0.948)., Conclusion: SWI and T2WI-derived radiomic features are valuable for noninvasively and accurately identifying MVI in early-stage HCC. Furthermore, the integration of radiomics and clinical factors yielded a predictive nomogram with satisfactory diagnostic performance and potential clinical benefits., (© 2024. Italian Society of Medical Radiology.)

Background: Microvascular invasion (MVI) is a risk factor for postoperative recurrence of hepatocellular carcinoma (HCC), even in early-stage HCC. In small HCC ≤ 3 cm, treatment options include anatomical resection or non-anatomical resection, and MVI has a major effect on treatment decisions. We aimed to identify the predictors of MVI in small HCC ≤ 3 cm., Methods: We retrospectively studied 129 patients with very early or early-stage HCC ≤ 3 cm who had undergone 18 F-fluorodeoxyglucose positron emission tomography/computed tomography and subsequent hepatic resection from January 2016 to August 2023. These patients were divided into the derivation cohort (n = 86) and validation cohort (n = 43). We examined the risk factors for MVI using logistic regression analysis, and established a predictive scoring system in the derivation cohort. We evaluated the accuracy of our scoring system in the validation cohort., Results: In the derivation cohort, a Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3), prothrombin induced by vitamin K deficiency or antagonist-II (PIVKA-II), and metabolic tumor volume (MTV) were independent predictors of MVI. We established the scoring system using these three factors. In the validation test, there were no MVI-positive cases with a score of 0 and 1, and all cases were MVI-positive with a score of 4. Moreover, with a score ≥ 2, the sensitivity, specificity, and accuracy of our scoring system were 100%, 71.4%, and 81.4%, respectively., Conclusions: Our scoring system can accurately predict MVI in small HCC ≤ 3 cm, and could contribute to establishing an appropriate treatment strategy., (© 2024. The Author(s) under exclusive licence to Japan Society of Clinical Oncology.)

Objectives: 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., Methods: 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., Results: 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., Conclusions: The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively., Advances in Knowledge: 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., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Objectives: To explore the predictive performance of tumor and multiple peritumoral regions on dynamic contrast-enhanced magnetic resonance imaging (MRI), to identify optimal regions of interest for developing a preoperative predictive model for the grade of microvascular invasion (MVI)., Methods: A total of 147 patients who were surgically diagnosed with hepatocellular carcinoma, and had a maximum tumor diameter ≤ 5 cm were recruited and subsequently divided into a training set (n = 117) and a testing set (n = 30) based on the date of surgery. We utilized a pre-trained AlexNet to extract deep learning features from seven different regions of the maximum transverse cross-section of tumors in various MRI sequence images. Subsequently, an extreme gradient boosting (XGBoost) classifier was employed to construct the MVI grade prediction model, with evaluation based on the area under the curve (AUC)., Results: The XGBoost classifier trained with data from the 20-mm peritumoral region showed superior AUC compared to the tumor region alone. AUC values consistently increased when utilizing data from 5-mm, 10-mm, and 20-mm peritumoral regions. Combining arterial and delayed-phase data yielded the highest predictive performance, with micro- and macro-average AUCs of 0.78 and 0.74, respectively. Integration of clinical data further improved AUCs values to 0.83 and 0.80., Conclusion: Compared with those of the tumor region, the deep learning features of the peritumoral region provide more important information for predicting the grade of MVI. Combining the tumor region and the 20-mm peritumoral region resulted in a relatively ideal and accurate region within which the grade of MVI can be predicted., Clinical Relevance Statement: The 20-mm peritumoral region holds more significance than the tumor region in predicting MVI grade. Deep learning features can indirectly predict MVI by extracting information from the tumor region and directly capturing MVI information from the peritumoral region., Key Points: We investigated tumor and different peritumoral regions, as well as their fusion. MVI predominantly occurs in the peritumoral region, a superior predictor compared to the tumor region. The peritumoral 20 mm region is reasonable for accurately predicting the three-grade MVI., (© 2024. The Author(s).)

Background: In this study, we aimed to establish nomograms to predict the microvascular invasion (MVI) and early recurrence in patients with small hepatocellular carcinoma (SHCC), thereby guiding individualized treatment strategies for prognosis improvement., Methods: This study retrospectively analyzed 326 SHCC patients who underwent radical resection at Wuhan Union Hospital between April 2017 and January 2022. They were randomly divided into a training set and a validation set at a 7:3 ratio. The preoperative nomogram for MVI was constructed based on univariate and multivariate logistic regression analysis, and the prognostic nomogram for early recurrence was constructed based on univariate and multivariate Cox regression analysis. We used the receiver operating characteristic (ROC) curves, area under the curves (AUCs), and calibration curves to estimate the predictive accuracy and discriminability of nomograms. Decision curve analysis (DCA) and Kaplan-Meier survival curves were employed to further confirm the clinical effectiveness of nomograms., Results: The AUCs of the preoperative nomogram for MVI on the training set and validation set were 0.749 (95%CI: 0.684-0.813) and 0.856 (95%CI: 0.805-0.906), respectively. For the prognostic nomogram, the AUCs of 1-year and 2-year RFS respectively reached 0.839 (95%CI: 0.775-0.903) and 0.856 (95%CI: 0.806-0.905) in the training set, and 0.808 (95%CI: 0.719-0.896) and 0.874 (95%CI: 0.804-0.943) in the validation set. Subsequent calibration curves, DCA analysis and Kaplan-Meier survival curves demonstrated the high accuracy and efficacy of the nomograms for clinical application., Conclusions: The nomograms we constructed could effectively predict MVI and early recurrence in SHCC patients, providing a basis for clinical decision-making., (© 2024. The Author(s).)

Abstract Background The accuracy of estimating microvascular invasion (MVI) preoperatively in hepatocellular carcinoma (HCC) by clinical observers is low. Most recent studies constructed MVI predictive models utilizing radiological and/or radiomics features extracted from computed tomography (CT) images. These methods, however, rely heavily on human experiences and require manual tumor contouring. We developed a deep learning-based framework for preoperative MVI prediction by using CT images of arterial phase (AP) with simple tumor labeling and without the need of manual feature extraction. The model was further validated on CT images that were originally scanned at multiple different hospitals. Methods CT images of AP were acquired for 309 patients from China Medical University Hospital (CMUH). Images of 164 patients, who took their CT scanning at 54 different hospitals but were referred to CMUH, were also collected. Deep learning (ResNet-18) and machine learning (support vector machine) models were constructed with AP images and/or patients’ clinical factors (CFs), and their performance was compared systematically. All models were independently evaluated on two patient cohorts: validation set (within CMUH) and external set (other hospitals). Subsequently, explainability of the best model was visualized using gradient-weighted class activation map (Grad-CAM). Results The ResNet-18 model built with AP images and patients’ clinical factors was superior than other models achieving a highest AUC of 0.845. When evaluating on the external set, the model produced an AUC of 0.777, approaching its performance on the validation set. Model interpretation with Grad-CAM revealed that MVI relevant imaging features on CT images were captured and learned by the ResNet-18 model. Conclusions This framework provide evidence showing the generalizability and robustness of ResNet-18 in predicting MVI using CT images of AP scanned at multiple different hospitals. Attention heatmaps obtained from model explainability further confirmed that ResNet-18 focused on imaging features on CT overlapping with the conditions used by radiologists to estimate MVI clinically.

ObjectivesTo evaluate the potential improvement of prediction performance of a proposed double branch multimodality-contribution-aware TripNet (MCAT) in microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on a small sample.MethodsIn this retrospective study, 121 HCCs from 103 consecutive patients were included, with 44 MVI positive and 77 MVI negative, respectively. A MCAT model aiming to improve the accuracy of deep neural network and alleviate the negative effect of small sample size was proposed and the improvement of MCAT model was verified among comparisons between MCAT and other used deep neural networks including 2DCNN (two-dimentional convolutional neural network), ResNet (residual neural network) and SENet (squeeze-and-excitation network), respectively.ResultsThrough validation, the AUC value of MCAT is significantly higher than 2DCNN based on CT, MRI, and both imaging (P < 0.001 for all). The AUC value of model with single branch pretraining based on small samples is significantly higher than model with end-to-end training in CT branch and double branch (0.62 vs 0.69, p=0.016, 0.65 vs 0.83, p=0.010, respectively). The AUC value of the double branch MCAT based on both CT and MRI imaging (0.83) was significantly higher than that of the CT branch MCAT (0.69) and MRI branch MCAT (0.73) (P < 0.001, P = 0.03, respectively), which was also significantly higher than common-used ReNet (0.67) and SENet (0.70) model (P < 0.001, P = 0.005, respectively).ConclusionA proposed Double branch MCAT model based on a small sample can improve the effectiveness in comparison to other deep neural networks or single branch MCAT model, providing a potential solution for scenarios such as small-sample deep learning and fusion of multiple imaging modalities.

This study aimed to clarify the role of Orosomucoid 1 (ORM1) in the development and therapy resistance in hepatocellular carcinoma (HCC). The mRNA expression level of ORM1 was analyzed via integrative analysis of Gene Express Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. The protein expression level of ORM1 in our cohort was determined using immunohistochemistry. Correlation analysis was used to investigate the relationship between ORM1 expression and clinical parameters. The Cell Counting Kit-8 assay was used to clarify the role of ORM1 in HCC malignant behaviors, including cell growth and sorafenib sensitivity, in vitro. The results indicated that ORM1 was significantly downregulated in the hepatic cancer cells compared to that in the non-cancerous cells. However, it was upregulated in microvascular invasion samples, especially in the cancer embolus compared to that in the surrounding tumor cells. Though Kaplan-Meier analysis did not show an association of ORM1 expression with the overall survival rates of HCC patients, univariate analysis indicated that ORM1 expression was highly correlated with tumor grade and stage. An in vitro assay also revealed that downregulation of ORM1 led to the suppression of tumor growth and enhancement of sorafenib sensitivity without epithelial-to-mesenchymal transition (EMT) alteration, which was consistent with our bioinformatic analysis. Hence, ORM1 played a key role in HCC tumorigenesis and may serve as a potential target for the development of therapeutics against HCC in the future.

Background and Aim: Microvascular invasion (MVI) has been established as one of the most important contributors to the prognosis of primary hepatocellular carcinoma (HCC). The objective of this study was to investigate the potential effect of postoperative adjuvant therapy with lenvatinib on the long-term prognosis after radical resection in hepatitis B virus (HBV)-related HCC patients with MVI, as well as to predict the long-term survival based on nomograms. Methods: Data from 293 HBV-related hepatocellular carcinoma patients with histologically confirmed MVI who underwent R0 resection at Eastern Hepatobiliary Surgery Hospital (EHBH) was retrospectively analyzed. 57 patients received postoperative adjuvant therapy with lenvatinib, while 236 patients did not. The survival outcome of patients who received postoperative adjuvant lenvatinib versus those who did not was analyzed. Results: The 1-year, 2-year recurrence rates and survival rates of the lenvatinib group were improved compared to the non-lenvatinib group (15.9%, 43.2% vs 40.1%, 57.2%, P=0.002; 85.8%, 71.2% vs 69.6%, 53.3%, P=0.009, respectively). Similar findings were also observed after Propensity Score Matching (PSM) compared to non-PSM analyses The 1-year, 2-year recurrence rates and survival rates were more favorable for the lenvatinib group compared to the non-lenvatinib group (15.9%, 43.2% vs 42.1%, 57.4%, P=0.028; 85.8%, 71.2% vs 70.0%, 53.4%, P=0.024, respectively). As shown by univariate and multivariate analyses, absence of adjuvant lenvatinib treatment was identified as an independent risk factor for recurrence and survival. The established nomograms displayed good performance for the prediction of recurrence and survival, with a C-index of 0.658 and 0.682 respectively. Conclusions: Postoperative adjuvant therapy with lenvatinib was associated with improved long-term prognosis after R0 Resection in HBV-related HCC patients with MVI, which could be accurately predicted from nomograms. [ABSTRACT FROM AUTHOR]

Background and Aims: Microvascular Invasion (MVI) Is a well-known risk factor for poor prognosis in hepatocellular carcinoma (HCC). This study aimed to develop a deep convolutional neural network (DCNN) model based on contrast-enhanced ultrasound (CEUS) to predict MVI, and thus to predict prognosis in patients with HCC. Methods: A total of 436 patients with surgically resected HCC who underwent preoperative CEUS were retrospectively enrolled. Patients were divided into training (n = 301), validation (n = 102), and test (n = 33) sets. A clinical model (Clinical model), a CEUS video-based DCNN model (CEUS-DCNN model), and a fusion model based on CEUS video and clinical variables (CECL-DCNN model) were built to predict MVI. Survival analysis was used to evaluate the clinical performance of the predicted MVI. Results: Compared with the Clinical model, the CEUS-DCNN model exhibited similar sensitivity, but higher specificity (71.4% vs. 38.1%, p = 0.03) in the test group. The CECL-DCNN model showed significantly higher specificity (81.0% vs. 38.1%, p = 0.005) and accuracy (78.8% vs. 51.5%, p = 0.009) than the Clinical model, with an AUC of 0.865. The Clinical predicted MVI could not significantly distinguish OS or RFS (both p > 0.05), while the CEUS-DCNN predicted MVI could only predict the earlier recurrence (hazard ratio [HR] with 95% confidence interval [CI 2.92 [1.1-7.75], p = 0.024). However, the CECL-DCNN predicted MVI was a significant prognostic factor for both OS (HR with 95% CI: 6.03 [1.7-21.39], p = 0.009) and RFS (HR with 95% CI: 3.3 [1.23-8.91], p = 0.011) in the test group. Conclusions: The proposed CECL-DCNN model based on preoperative CEUS video can serve as a noninvasive tool to predict MVI status in HCC, thereby predicting poor prognosis. [ABSTRACT FROM AUTHOR]

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a histopathological marker and risk factor for HCC recurrence. We integrated diffusion-weighted imaging (DWI) and magnetic resonance (MR) image findings of tumors into a scoring system for predicting MVI. In total, 228 HCC patients with pathologically confirmed MVI who underwent surgical resection or liver transplant between November 2012 and March 2021 were enrolled retrospectively. Patients were divided into a right liver lobe group (n = 173, 75.9%) as the model dataset and a left liver lobe group (n = 55, 24.1%) as the model validation dataset. Multivariate logistic regression identified two-segment involved tumor (Score: 1; OR: 3.14; 95% CI: 1.22 to 8.06; p = 0.017); ADCmin ≤ 0.95 × 10−3 mm2/s (Score: 2; OR: 10.88; 95% CI: 4.61 to 25.68; p = 0.000); and largest single tumor diameter ≥ 3 cm (Score: 1; OR: 5.05; 95% CI: 2.25 to 11.30; p = 0.000), as predictive factors for the scoring model. Among all patients, sensitivity was 89.66%, specificity 58.04%, positive predictive value 68.87%, and negative predictive value 84.41%. For validation of left lobe group, sensitivity was 80.64%, specificity 70.83%, positive predictive value 78.12%, and negative predictive value 73.91%. The scoring model using ADCmin, largest tumor diameter, and two-segment involved tumor provides high sensitivity and negative predictive value in MVI prediction for use in routine functional MR. [ABSTRACT FROM AUTHOR]

The goal of the study described here was to define the predictive value of pre-operative clinical information and contrast-enhanced ultrasound (CEUS) imaging characteristics in combined hepatocellular-cholangiocarcinoma (CHC) patients with microvascular invasion (MVI). Seventy-six patients with pathologically confirmed CHC were enrolled in this study, comprising 18 patients with MVI-positive status and 58 with MVI-negative CHC nodules. The pre-operative clinical data and CEUS imaging features were retrospectively analyzed. Univariate and multivariate analyses were performed to identify the potential predictors of MVI in CHC. Recurrence-free survival (RFS) after hepatectomy was compared between patients with different MVI status using the log-rank test and Kaplan-Meier survival curves. Univariate analysis indicated that the following parameters of patients with CHC significantly differed between the MVI-positive and MVI-negative groups (p<0.05): tumor size, α-fetoprotein ≥400 ng/mL, enhancement patterns in arterial phase and marked washout during the portal venous phase on CEUS. On multivariate logistic regression analysis, only the CEUS characteristics of heterogeneous enhancement (odds ratio = 6.807; 95% confidence interval [CI]: 1.099, 42.147; p = 0.039) and marked washout (odds ratio = 4.380; 95% CI: 1.050,18.270; p = 0.043) were identified as independent predictors of MVI in CHC. The combination of the two risk factors in predicting MVI achieved a better diagnostic performance than each parameter alone, with an area under the receiver operating characteristic curve of 0.736 (0.622, 0.830). After hepatectomy, CHC patients with MVI exhibited earlier recurrence compared with those without MVI (hazard ratio = 1.859; 95% CI: 0.8699-3.9722, p = 0.046). The CEUS imaging features of heterogeneous enhancement in the arterial phase and marked washout during the portal venous phase were the potential predictors of MVI in CHC. Aside from that, CHC patients with MVI had an earlier recurrence rate than those without MVI after surgery. [ABSTRACT FROM AUTHOR]

Purpose: The aim was to investigate the association between microvascular invasion (MVI) and the peritumoral imaging features of gadolinium ethoxybenzyl DTPA-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) in hepatocellular carcinoma (HCC). Methods: Up until Feb 24, 2022, the PubMed, Embase, and Cochrane Library databases were carefully searched for relevant material. The software packages utilized for this meta-analysis were Review Manager 5.4.1, Meta-DiSc 1.4, and Stata16.0. Summary results are presented as sensitivity (SEN), specificity (SPE), diagnostic odds ratios (DORs), area under the receiver operating characteristic curve (AUC), and 95% confidence interval (CI). The sources of heterogeneity were investigated using subgroup analysis. Results: An aggregate of nineteen articles were remembered for this meta-analysis: peritumoral enhancement on the arterial phase (AP) was described in 13 of these studies and peritumoral hypointensity on the hepatobiliary phase (HBP) in all 19 studies. The SEN, SPE, DOR, and AUC of the 13 investigations on peritumoral enhancement on AP were 0.59 (95% CI, 0.41−0.58), 0.80 (95% CI, 0.75−0.85), 4 (95% CI, 3−6), and 0.73 (95% CI, 0.69−0.77), respectively. The SEN, SPE, DOR, and AUC of 19 studies on peritumoral hypointensity on HBP were 0.55 (95% CI, 0.45−0.64), 0.87 (95% CI, 0.81−0.91), 8 (95% CI, 5−12), and 0.80 (95% CI, 0.76−0.83), respectively. The subgroup analysis of two imaging features identified ten and seven potential factors for heterogeneity, respectively. Conclusion: The results of peritumoral enhancement on the AP and peritumoral hypointensity on HBP showed high SPE but low SEN. This indicates that the peritumoral imaging features on Gd-EOB-DTPA-enhanced MRI can be used as a noninvasive, excluded diagnosis for predicting hepatic MVI in HCC preoperatively. Moreover, the results of this analysis should be updated when additional data become available. Additionally, in the future, how to improve its SEN will be a new research direction. [ABSTRACT FROM AUTHOR]

Background: The efficacy of radiofrequency ablation (RFA) for patients with early-stage recurrent hepatocellular carcinoma (HCC) with microvascular invasion (MVI) at the initial hepatectomy is limited. Our study aimed to explore whether adjuvant sorafenib following RFA could improve the situation. Methods: We retrospectively included 211 patients with early-stage (tumor number of ≤3 and tumor size of 2–5 cm) recurrent HCC with MVI at the initial hepatectomy who underwent adjuvant sorafenib following RFA or RFA alone in 13 centers from June 2013 to June 2020. In the combination group, sorafenib of 400 mg twice daily was administered within 7 days after RFA. Overall survival (OS) and recurrence-free survival (RFS) were compared. Subgroup analysis based on MVI grade was performed. MVI grade was based on the practice guidelines for the pathological diagnosis of HCC and included M1 (≤5 MVI sites, all located within adjacent peritumoral liver tissues 0–1 cm away from the tumor margin) and M2 (>5 MVI sites, or any MVI site located within adjacent peritumoral liver tissues > 1 cm away from the tumor margin). Results: A total of 103 patients received the combination therapy and 108 patients received RFA alone. The combination therapy provided better survival than RFA alone (median RFS: 17.7 vs. 13.1 months, P < 0.001; median OS: 32.0 vs. 25.0 months, P = 0.002). Multivariable analysis revealed that treatment allocation was an independent prognostic factor. On subgroup analysis, the combination therapy provided better survival than RFA alone in patients with M1 along with either a tumor size of 3–5 cm, tumor number of two to three, or alpha-fetoprotein (AFP) > 400 μg/L, and in those with M2 along with either a tumor size of 2–3 cm, one recurrent tumor, or AFP ≤ 400 μg/L. Conclusions: Adjuvant sorafenib following RFA was associated with better survival than RFA alone in patients with early-stage recurrent HCC with MVI at the initial hepatectomy. Moreover, MVI grade could guide the application of adjuvant sorafenib. [ABSTRACT FROM AUTHOR]

Background and objectives: To evaluate the individual and combined associations of cytokeratin 19 (CK19) and microvascular invasion (MVI) with prognosis of patients with hepatocellular carcinoma (HCC). Methods: Clinicopathological data on 352 patients with HCC who underwent radical resection at our hospital between January 2013 and December 2015 were retrospectively analyzed. Patients were divided into four groups: CK19(−)/MVI(−), CK19(−)/MVI(+), CK19(+)/MVI(−), and CK19(+)/MVI(+). Results: Of the 352 HCC patients, 154 (43.8%) were CK19(−)/MVI(−); 116 (33.0%), CK19(−)/MVI(+); 31 (8.8%), CK19(+)/MVI(−); and 51 (14.5%), CK19(+)/MVI(+). The disease-free survival of CK19(−)/MVI(−) patients was significantly higher than that of CK19(−)/MVI(+) patients and CK19(+)/MVI(+) patients. Similar results were observed for overall survival. CK19(+)/MVI(+) patients showed significantly lower overall survival than the other three groups. Conclusions: CK19 expression and MVI predict poor prognosis after radical resection of HCC, and the two markers jointly contribute to poor OS. Combining CK19 and MVI may predict post-resection prognosis better than using either factor on its own. [ABSTRACT FROM AUTHOR]

Background: Microscopic portal vein invasion (MPVI) strongly predicts poor prognosis in patients with hepatocellular carcinoma (HCC). This study aims to investigate the impact of MPVI on the efficacy of postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE). Methods: From April 2014 to July 2019, a total of 512 HCC patients who underwent curative liver resection (LR) with microscopic vascular invasion (MVI) confirmed by histopathological examination were enrolled and divided into LR alone and PA-TACE groups. They were subsequently stratified into subgroups according to the presence of MPVI. Recurrence-free survival (RFS) and overall survival (OS) were compared using Kaplan–Meier curves and the log-rank test. The efficacy of PA-TACE was tested using univariate and multivariate Cox regression analyses. Sensitivity analysis was conducted after propensity score matching (PSM). Results: Among all patients, 165 (32.3%) patients underwent PA-TACE, and 196 (38.2%) patients presented MPVI. In the entire cohort, PA-TACE and the presence of MPVI were identified as independent predictors for RFS and OS (all p<0.05). In the subgroup analysis, patients without MPVI who received PA-TACE had significantly better outcomes than those who underwent LR alone before and after PSM (all p<0.05). For patients with MPVI, PA-TACE displayed no significant benefit in terms of improving either RFS or OS, which was consistent with the results from the PSM cohort. Conclusion: Among the HCC patients without MPVI who underwent curative liver resection, those who received PA-TACE had better RFS and OS outcomes than those who underwent LR alone. For patients with MPVI, PA-TACE had no significant effect on either RFS or OS outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Microvascular invasion is an important indicator for reflecting the prognosis of hepatocellular carcinoma, but the traditional diagnosis requires a postoperative pathological examination. This study is the first to propose an end-to-end deep learning architecture for predicting microvascular invasion in hepatocellular carcinoma by collecting retrospective data. This method can achieve noninvasive, accurate and efficient preoperative prediction only through the patient's radiomic data, which is very beneficial to doctors for clinical decision making in HCC patients. Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) directly affects a patient's prognosis. The development of preoperative noninvasive diagnostic methods is significant for guiding optimal treatment plans. In this study, we investigated 138 patients with HCC and presented a novel end-to-end deep learning strategy based on computed tomography (CT) radiomics (MVI-Mind), which integrates data preprocessing, automatic segmentation of lesions and other regions, automatic feature extraction, and MVI prediction. A lightweight transformer and a convolutional neural network (CNN) were proposed for the segmentation and prediction modules, respectively. To demonstrate the superiority of MVI-Mind, we compared the framework's performance with that of current, mainstream segmentation, and classification models. The test results showed that MVI-Mind returned the best performance in both segmentation and prediction. The mean intersection over union (mIoU) of the segmentation module was 0.9006, and the area under the receiver operating characteristic curve (AUC) of the prediction module reached 0.9223. Additionally, it only took approximately 1 min to output a prediction for each patient, end-to-end using our computing device, which indicated that MVI-Mind could noninvasively, efficiently, and accurately predict the presence of MVI in HCC patients before surgery. This result will be helpful for doctors to make rational clinical decisions. [ABSTRACT FROM AUTHOR]

Background: Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC. Methods: We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated, and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression. Results: Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and a-fetoprotein (AFP) were independently associated with MVI: DL-MVI (odds ratio [OR] = 35.738; 95% confidence interval [CI] 14.027–91.056; p < 0.001), AFP (OR = 4.634, 95% CI 2.576–8.336; p < 0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824. Conclusions: Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC. [ABSTRACT FROM AUTHOR]

To elucidate the impact of acute-phase protein serum amyloid A (aSAA) on microvascular invasion (MVI) and early recurrence in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). HBV-related HCC patients (n = 192) undergoing liver resection were included in the study. The protein levels of aSAA were analyzed by immunohistochemical staining in 172 tumor specimens, and further detected via western blotting in HCC and their corresponding portal vein tumor thrombus (PVTT) (n = 20). Cox and logit regression analysis was performed. Exploratory subgroup analysis was used to balance the potential confounders. HBV-related HCC patients with high aSAA levels tended to have high HBV-DNA loads. Logit and Cox regression analyses revealed high expression of aSAA is an independent risk factor not only for MVI (OR 5.384, 95% CI 2.286–13.301, P < 0.001) but also for early recurrence (HR 6.040, 95% CI 1.970–18.540, P = 0.002), overall recurrence (HR 3.720, 95% CI 2.140–6.450, P < 0.001), and overall survival (HR 4.15, 95% CI 2.380–7.230, P < 0.001). Subgroup analysis showed that the effects of aSAA were consistent across all subgroups examined. Additionally, the aSAA protein level was significantly higher in PVTT than that in its corresponding tumor specimen. A high HBV-DNA level and large tumor size were the independent risk factors for early HCC recurrence in patients with high levels of aSAA. High expression of aSAA was an independent risk factor for MVI and early tumor recurrence in HBV-related HCC patients after liver resection. The aSAA protein level could thus be a promising biomarker for predicting MVI and early recurrence in these patients. [ABSTRACT FROM AUTHOR]

Purpose: Evaluation of perfusion CT and dual-energy CT (DECT) quantitative parameters for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) prior to surgery. Methods: This prospective single-center study included fifty-six patients (44 men; median age 67; range 31–84) who provided written informed consent. Inclusion criteria were (1) treatment-naïve patients with a diagnosis of HCC, (2) an indication for hepatic resection, and (3) available arterial DECT phase and perfusion CT (GE revolution HD-GSI). Iodine concentrations (IC), arterial density (AD), and 9 quantitative perfusion parameters for HCC were correlated to pathological results. Radiological parameters based principal component analysis (PCA), corroborated by unsupervised heatmap classification, was meant to deliver a model for predicting MVI in HCC. Survival analysis was performed using univariable log-rank test and multivariable Cox model, both censored at time of relapse. Results: 58 HCC lesions were analyzed (median size 42.3 mm; range of 20–140). PCA showed that the radiological model was predictive of tumor grade (p = 0.01), intratumoral MVI (p = 0.004), peritumoral MVI (p = 0.04), MTM (macrotrabecular-massive) subtype (p = 0.02), and capsular invasion (p = 0.02) in HCC. Heatmap classification of HCC showed tumor heterogeneity, stratified into three main clusters according to the risk of relapse. Survival analysis confirmed that permeability surface-area product (PS) was the only significant independent parameter, among all quantitative tumoral CT parameters, for predicting a risk of relapse (Cox p value = 0.004). Conclusion: A perfusion CT and DECT-based quantitative imaging profile can provide a diagnosis of histological MVI in HCC. PS is an independent parameter for relapse. Clinical trials: ClinicalTrials.gov: NCT03754192. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study is to establish and validate a radiomics-based model using preoperative Gd-EOB-DTPA-enhanced MRI to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma ≤ 5 cm. Methods: Clinicopathologic and MRI data of 178 patients with solitary hepatocellular carcinoma (HCC) (≤5 cm) were retrospectively collected from a single medical center between May 2017 and November 2020. Patients were randomly assigned into training and test subsets by a ratio of 7:3. Imaging features were extracted from the segmented tumor volume of interest with 1-cm expansion on arterial phase (AP) and hepatobiliary phase (HBP) images. Different models based on the significant clinical risk factors and/or selected imaging features were established and the predictive performance of the models was evaluated. Results: Three radiomics models, the AP_model, the HBP_model, and the AP+HBP_model, were constructed for MVI prediction. Among them, the AP+HBP_model outperformed the other two. When it was combined with a clinical model, consisting of tumor size and alpha-fetoprotein (AFP), the combined model (AP+HBP+Clin_model) showed an area under the curve of 0.90 and 0.70 in the training and test subsets, respectively. Its sensitivity and specificity were 0.91 and 0.76 in the training subset and 0.60 and 0.79 in the test subset, respectively. The calibration curve illustrated that the combined model possessed a good agreement between the predicted and the actual probabilities. Conclusions: The radiomics-based model combining imaging features from the arterial and hepatobiliary phases of Gd-EOB-DTPA-enhanced MRI and clinical risk factors provides an effective and reliable tool for the preoperative prediction of MVI in patients with HCC ≤ 5 cm. [ABSTRACT FROM AUTHOR]

Background: To assess the values of two elastography techniques combined with serological examination and clinical features in preoperative diagnosis of microvascular invasion in HCC patients. Methods: A total of 74 patients with single Hepatocellular carcinoma (HCC) were included in this study. Shear wave measurement and real-time tissue elastography were used to evaluate the hardness of tumor-adjacent tissues and tumor tissues, as well as the strain rate ratio per lesion before surgery. According to the pathological results, the ultrasound parameters and clinical laboratory indicators related to microvascular invasion were analyzed, and the effectiveness of each parameter in predicting the occurrence of microvascular invasion was compared. Results: 33/74 patients exhibited microvascular invasion. Univariate analysis showed that the hardness of tumor-adjacent tissues (P = 0.003), elastic strain rate ratio (P = 0.032), maximum tumor diameter (P < 0.001), and alpha-fetoprotein (AFP) level (P = 0.007) was significantly different in the patients with and without microvascular invasion. The binary logistic regression analysis showed that the maximum tumor diameter (P = 0.001) was an independent risk factor for predicting microvascular invasion, while the hardness of tumor-adjacent tissues (P = 0.028) was a protective factor. The receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) of the hardness of tumor-adjacent tissues, the maximum diameter of the tumor, and the predictive model Logit(P) in predicting the occurrence of MVI was 0.718, 0.775 and 0.806, respectively. Conclusion: The hardness of tumor-adjacent tissues, maximum tumor diameter, and the preoperative prediction model predict the occurrence of MVI in HCC patients. [ABSTRACT FROM AUTHOR]