Independent Predictors Of Microvascular Invasion Research Articles

Comparison of Sonazoid-Contrast‑Enhanced Ultrasound and Gd‑EOB‑DTPA‑Enhanced MRI for Predicting Microvascular Invasion in Hepatocellular Carcinoma

ObjectiveThis 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). MethodsIn 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. ResultsMultifactorial 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%. ConclusionSonazoid-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.

New predictors of microvascular invasion for small hepatocellular carcinoma ≤ 3cm.

Microvascular invasion (MVI) is a risk factor for postoperative recurrence of hepatocellular carcinoma (HCC), even in early-stage HCC. In small HCC ≤ 3cm, 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 ≤ 3cm. We retrospectively studied 129 patients with very early or early-stage HCC ≤ 3cm who had undergone 18F-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. 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. Our scoring system can accurately predict MVI in small HCC ≤ 3cm, and could contribute to establishing an appropriate treatment strategy.

Nomogram for preoperative estimation of microvascular invasion risk in hepatocellular carcinoma

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

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

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.

Clinical prediction of microvascular invasion in hepatocellular carcinoma using an MRI-based graph convolutional network model integrated with nomogram.

Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram. We retrospectively collected 182 HCC patients confirmed histopathologically, all of them performed enhanced MRI before surgery. The patients were randomly divided into training and validation groups. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), respectively. After removing redundant features, the graph structure by constructing the distance matrix with the feature matrix was built. Screening the superior phases and acquired GCN Score (GS). Finally, combining clinical, radiological and GS established the predicting nomogram. 27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoural artery (P = 0.007) was an independent predictor of MVI. GCN model with grey-level cooccurrence matrix-grey-level run length matrix features exhibited area under the curves of the training group was 0.532, 0.690, and 0.885 and the validation group was 0.583, 0.580, and 0.854 for AP, PVP, and DP, respectively. DP was selected to develop final model and got GS. Combining GS with diameter, corona enhancement, mosaic architecture, and intratumoural artery constructed a nomogram which showed a C-index of 0.884 (95% CI: 0.829-0.927). The GCN model based on DP has a high predictive ability. A nomogram combining GS, clinical and radiological characteristics can be a simple and effective guiding tool for selecting HCC treatment options. GCN based on MRI could predict MVI on HCC.

Radiomics and radiogenomics in intrahepatic cholangiocarcinoma

Purpose of the study. Analytical review and analysis of available literature on texture analysis of computed tomgraphy (CT) and magnetic resonance imaging (MRI) in noninvasive diagnosis of ICC and correlation with molecular genetic features and tumor immunophenotype.Materials and methods. The scientific publications and clinical guidelines in the information- analytical systems PubMed, Scopus for 2012–2022 were carried out using the keywords: "mri", "radiomics", "texture analysis", "radiogenomics", "intrahepatic cholangiocarcinoma", "molecular". 49 articles were selected for analysis after excluding studies dealing with technical aspects of radiomics and describing individual clinical observations.Results. The presented review demonstrated the broad possibilities and prospects of application of CT and MRI texture analysis in the study of cholangiocellular cancer, including the first results in the study of molecular features (signatures) of this tumor. Correlation of texture features with the expression of immunotherapy target genes KRAS/NRAS/BRAF as well as IDH1/2 mutation was shown. Texture scores were the predominant independent predictor of microvascular invasion, which was a major independent risk factor for postoperative recurrence.Conclusion. The use of texture analysis undoubtedly demonstrates promising possibilities both in noninvasive assessment of the HCC histological differentiation grade, as well as in differential diagnosis with hepatocellular carcinoma, metastases, and requires further study for systematization and standardization of the obtained data.

Three-Dimensional Multifrequency MR Elastography for Microvascular Invasion and Prognosis Assessment in Hepatocellular Carcinoma.

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.

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

Both contrast-enhanced ultrasound (CEUS) and contrast-enhanced magnetic resonance (CEMR) are important imaging methods for hepatocellular carcinoma (HCC). This study aimed to establish a model using preoperative CEUS parameters to predict microvascular invasion (MVI) in HCC, and compare its predictive efficiency with that of CEMR model. A total of 93 patients with HCC (39 cases in MVI positive group and 54 cases in MVI negative group) who underwent surgery in our hospital from January 2020 to June 2021 were retrospectively analyzed. Their clinical and imaging data were collected to establish CEUS and CEMR models for predicting MVI. The predictive efficiencies of both models were compared. By the univariate and multivariate regression analyses of patients' clinical information, preoperative CEUS static and dynamic images, we found that serrated edge and time to peak were independent predictors of MVI. The CEUS prediction model achieved a sensitivity of 92.3%, a specificity of 83.3%, and an accuracy of 84.6% (Az: 0.934). By analyzing the clinical and CEMR information, we found that tumor morphology, fast-in and fast-out, peritumoral enhancement, and capsule were independent predictors of MVI. The CEMR prediction model achieved a sensitivity of 97.4%, a specificity of 77.8%, and an accuracy of 83.2% (Az: 0.900). The combination of the two models achieved a sensitivity of 84.6%, a specificity of 87.0%, and an accuracy of 86.2% (Az: 0.884). There was no significant statistical difference in the areas under the ROC curve of the three models. The CEUS model and the CEMR model have similar predictive efficiencies for MVI of HCC. CEUS is also an effective method to predict MVI before operation.

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

To identify MRI features of hepatocellular carcinoma (HCC) that predict microvascular invasion (MVI) and postoperative intrahepatic recurrence in patients without peritumoral hepatobiliary phase (HBP) hypointensity. One hundred and thirty patients with HCC who underwent preoperative gadoxetate-enhanced MRI and curative hepatic resection were retrospectively reviewed. Two radiologists reviewed all preoperative MR images and assessed the radiological features of HCCs. The ability of peritumoral HBP hypointensity to identify MVI and intrahepatic recurrence was analyzed. We then assessed the MRI features of HCC that predicted the MVI and intrahepatic recurrence-free survival (RFS) in the subgroup without peritumoral HBP hypointensity. Finally, a two-step flowchart was constructed to assist in clinical decision-making. Peritumoral HBP hypointensity (odds ratio, 3.019; 95% confidence interval: 1.071-8.512; P=0.037) was an independent predictor of MVI. The sensitivity, specificity, positive predictive value, negative predictive value, and AUROC of peritumoral HBP hypointensity in predicting MVI were 23.80%, 91.04%, 71.23%, 55.96%, and 0.574, respectively. Intrahepatic RFS was significantly shorter in patients with peritumoral HBP hypointensity (P<0.001). In patients without peritumoral HBP hypointensity, the only significant difference between MVI-positive and MVI-negative HCCs was the presence of a radiological capsule (P=0.038). Satellite nodule was an independent risk factor for intrahepatic RFS (hazard ratio,3.324; 95% CI: 1.733-6.378; P<0.001). The high-risk HCC detection rate was significantly higher when using the two-step flowchart that incorporated peritumoral HBP hypointensity and satellite nodule than when using peritumoral HBP hypointensity alone (P<0.001). In patients without peritumoral HBP hypointensity, a radiological capsule is useful for identifying MVI and satellite nodule is an independent risk factor for intrahepatic RFS.

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

AFP-negative hepatocellular carcinoma (AFPN-HCC) within 5 cm is a special subgroup of HCC. This study aimed to investigate the value of dual-layer spectral-detector CT (DLCT) and construct a scoring model based on imaging features as well as DLCT for predicting microvascular invasion (MVI) in AFPN-HCC within 5 cm. This retrospective study enrolled 104 HCC patients who underwent multiphase contrast-enhanced DLCT studies preoperatively. Combined radiological features (CR) and combined DLCT quantitative parameter (CDLCT) were constructed to predict MVI. Multivariable logistic regression was applied to identify potential predictors of MVI. Based on the coefficient of the regression model, a scoring model was developed. The predictive efficacy was assessed through ROC analysis. Microvascular invasion (MVI) was found in 28 (26.9%) AFPN-HCC patients. Among single parameters, the effective atomic number in arterial phase demonstrated the best predictive efficiency for MVI with an area under the curve (AUC) of 0.792. CR and CDLCT showed predictive performance with AUCs of 0.848 and 0.849, respectively. A risk score (RS) was calculated using the independent predictors of MVI as follows: RS=2×(mosaic architecture)+2×(corona enhancement)+2×(incomplete tumor capsule)+2×(2-trait predictor of venous invasion [TTPVI])+3×(CDLCT > -1.229). Delong's test demonstrated this scoring system could significantly improve the AUC to 0.929 compared with CR (p=0.016) and CDLCT (p=0.034). The scoring model combining radiological features with DLCT provides a promising tool for predicting MVI in solitary AFPN-HCC within 5 cm preoperatively.

Predictive factors of microvascular invasion in patients with intrahepatic mass-forming cholangiocarcinoma based on magnetic resonance images.

The aim of this retrospective study was to develop and validate a preoperative nomogram for predicting microvascular invasion (MVI) in patients with intrahepatic mass-forming cholangiocarcinoma (IMCC) based on magnetic resonance imaging (MRI). In this retrospective study, 224 consecutive patients with clinicopathologically confirmed IMCC were enrolled. Patients whose data were collected from February 2010 to December 2020 were randomly divided into the training (131 patients) and internal validation (51 patients) datasets. The data from January 2021 to November 2021 (42 patients) were allocated to the time-independent validation dataset. Univariate and multivariate forward logistic regression analyses were used to identify preoperative MRI features that were significantly related to MVI, which were then used to develop the nomogram. We used the area under the receiver operating characteristic curve (AUC) and calibration curve to evaluate the performance of the nomogram. Interobserver agreement of MRI qualitative features was good to excellent, with κ values of 0.613-0.882. Multivariate analyses indicated that the following variables were independent predictors of MVI: multiple tumours (odds ratio [OR]) = 4.819, 95% confidence interval [CI] 1.562-14.864, P = 0.006), ill-defined margin (OR = 6.922, 95% CI 2.883-16.633, P < 0.001), and carbohydrate antigen 19-9 (CA 19-9) > 37U/ml (OR = 2.890, 95% CI 1.211-6.897, P = 0.017). A nomogram incorporating these factors was established using well-fitted calibration curves. The nomogram showed good diagnostic efficacy for MVI, with AUC values of 0.838, 0.819, and 0.874 for the training, internal validation, and time-independent validation datasets, respectively. A nomogram constructed using independent factors, namely the presence of multiple tumours, ill-defined margins, and CA 19-9 > 37U/ml could predict the presence of MVI. This can facilitate personalised therapeutic strategy and clinical management in patients with IMCC.

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

The presence of microvascular invasion of hepatocellular carcinoma has a significantly decreased outcome following hepatectomy or liver transplantation. Currently, it is still based on histological examination. Identification of microvascular invasion by using pre-operative imaging is important for the decision-making of surgeons and interventional radiologists. Aim of the study was to predict the microvascular invasion of hepatocellular carcinoma based on tumour margin enhancement of pre-operative multiphase computed tomography (CT) images. Fifty-three patients with hepatocellular carcinoma, who underwent pre-operative multiphase CT scans, were included in this study. Tumour margin enhancing patterns were analysed in the late arterial phase, portovenous phase, and delay phase. The CT features including peritumoral enhancement, arterial rim-enhancement, presence of daughter nodules, complete capsule enhancement in portovenous/delay phase, and nodular capsule enhancement in portovenous/delay phase were reviewed with calculations for sensitivity and specificity. Univariate analysis and multivariate analysis were used to identify predictive features for microvascular invasion (MVI). In the late arterial phase, peritumoral enhancement or the presence of daughter nodules were not predictors for MVI. Nodular capsule enhancement in the portovenous phase and delay phase were independent predictors for MVI with odds ratios of 29.25 and 33.09, respectively. The sensitivity and specificity for incomplete/nodular capsule enhancement in the portovenous phase were 69.23% and 96.86%, respectively. The sensitivity and specificity for incomplete/nodular capsule enhancement in the delay phase were 71.79% and 96.86%, respectively. Nodular capsule enhancement in the portovenous phase or delay phase was a good predictor for MVI.

Prediction for Aggressiveness and Postoperative Recurrence of Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced Magnetic Resonance Imaging

To investigate the predictive value of gadoxetic acid-enhanced magnetic resonance imaging (MRI) features on the pathologic grade, microvascular invasion (MVI), and cytokeratin-19 (CK19) expression in hepatocellular carcinomas (HCC), and to evaluate their association with postoperative recurrence of HCC. This retrospective study included 147 patients with surgically confirmed HCCs who underwent gadoxetic-enhanced MRI. The lesions were evaluated quantitatively in terms of the relative enhancement ratio (RER), and qualitatively based on imaging features and clinical parameters. Logistic regression analyses were performed to investigate the value of these parameters in predicting the pathologic grade, MVI, and CK19 in HCC. Predictive factors for postoperative recurrence were determined using a Cox proportional hazards model. Peritumoral enhancement (odds ratio [OR], 3.396; p=0.025) was an independent predictor of high pathologic grades. Serum protein induced by vitamin K absence or antagonist (PIVKA) level > 40 mAU/mL (OR, 3.763; p=0.018) and peritumoral hypointensity (OR, 4.343; p=0.003) were independent predictors of MVI. Predictors of CK19 included serum alpha-fetoprotein (AFP) level > 400 ng/mL (OR, 4.576; p=0.005), rim enhancement (OR, 5.493; p=0.024), and lower RER (OR, 0.013; p=0.011). Peritumoral hypointensity (hazard ratio [HR], 1.957; p=0.027) and poor pathologic grades (HR, 2.339; p=0.043) were independent predictors of recurrence. We demonstrated the value of preoperative gadoxetic-enhanced MRI in predicting aggressive pathological features of HCC. Poor pathologic grades and peritumoral hypointensity may independently predict the recurrence of HCC.

Prediction of Microvascular Invasion in Combined Hepatocellular-Cholangiocarcinoma Based on Pre-operative Clinical Data and Contrast-Enhanced Ultrasound Characteristics.

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.

Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma.

BACKGROUNDThe prognosis of hepatocellular carcinoma (HCC) remains poor and relapse occurs in more than half of patients within 2 years after hepatectomy. In terms of recent studies, microvascular invasion (MVI) is one of the potential predictors of recurrence. Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning.AIMTo develop a radiomic analysis model based on pre-operative magnetic resonance imaging (MRI) data to predict MVI in HCC.METHODSA total of 113 patients recruited to this study have been diagnosed as having HCC with histological confirmation, among whom 73 were found to have MVI and 40 were not. All the patients received preoperative examination by Gd-enhanced MRI and then curative hepatectomy. We manually delineated the tumor lesion on the largest cross-sectional area of the tumor and the adjacent two images on MRI, namely, the regions of interest. Quantitative analyses included most discriminant factors (MDFs) developed using linear discriminant analysis algorithm and histogram analysis with MaZda software. Independent significant variables of clinical and radiological features and MDFs for the prediction of MVI were estimated and a discriminant model was established by univariate and multivariate logistic regression analysis. Prediction ability of the above-mentioned parameters or model was then evaluated by receiver operating characteristic (ROC) curve analysis. Five-fold cross-validation was also applied via R software.RESULTSThe area under the ROC curve (AUC) of the MDF (0.77-0.85) outperformed that of histogram parameters (0.51-0.74). After multivariate analysis, MDF values of the arterial and portal venous phase, and peritumoral hypointensity in the hepatobiliary phase were identified to be independent predictors of MVI (P < 0.05). The AUC value of the model was 0.939 [95% confidence interval (CI): 0.893-0.984, standard error: 0.023]. The result of internal five-fold cross-validation (AUC: 0.912, 95%CI: 0.841-0.959, standard error: 0.0298) also showed favorable predictive efficacy.CONCLUSIONNoninvasive MRI radiomic model based on MDF values and imaging biomarkers may be useful to make preoperative prediction of MVI in patients with primary HCC.

Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using ^{18}F-FDG PET/CT

PurposeTo develop a clinical-radiomics nomogram by incorporating radiomics score and clinical predictors for preoperative prediction of microvascular invasion in hepatocellular carcinoma.MethodsA total of 97 HCC patients were retrospectively enrolled from Shanghai Universal Medical Imaging Diagnostic Center and Changhai Hospital Affiliated to the Second Military Medical University. 909 CT and 909 PET slicers from 97 HCC patients were divided into a training cohort (N = 637) and a validation cohort (N = 272). Radiomics features were extracted from each CT or PET slicer, and features selection was performed with least absolute shrinkage and selection operator regression and radiomics score was also generated. The clinical-radiomics nomogram was established by integrating radiomics score and clinical predictors, and the performance of the models were evaluated from its discrimination ability, calibration ability, and clinical usefulness.ResultsThe radiomics score consisted of 45 selected features, and age, the ratio of maximum to minimum tumor diameter, and ^{18}F-FDG uptake status were independent predictors of microvascular invasion. The clinical-radiomics nomogram showed better performance for MVI detection (0.890 [0.854, 0.927]) than the clinical nomogram (0.849 [0.804, 0.893]) (p<0.05). Both nomograms showed good calibration and the clinical-radiomics nomogram’s clinical practicability outperformed the clinical nomogram.ConclusionsWith the combination of radiomics score and clinical predictors, the clinical-radiomics nomogram can significantly improve the predictive efficacy of microvascular invasion in hepatocellular carcinoma (p<0.05) compared with clinical nomogram.

A nomogram based on multi-modal ultrasound for prediction of microvascular invasion and recurrence of hepatocellular carcinoma

ObjectiveTo establish and validate a nomogram based on multi-modal ultrasound for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC), and to assess the ability thereof to stratify recurrence-free survival (RFS). MethodsA total of 287 HCC patients undergoing surgical resection were prospectively enrolled, including 210 patients in the training cohort and 77 patients in the test cohort. All patients underwent conventional ultrasound, contrast-enhanced ultrasonography, and shear wave elastography examinations within one week before surgery. Taking histopathological examination result as the reference standard, independent factors associated with MVI in HCC were determined by logistic regression and a nomogram was established and further evaluated. The Kaplan-Meier method was used to analyze the prognostic value of histologic MVI status and nomogram-predicted MVI status. ResultsMultivariate analysis showed that tumor diameter, echogenicity, tumor shape, arterial phase peritumoral enhancement and enhancement level in portal venous phase were independent predictors of MVI (all p < 0.05). The nomogram based on these variables showed good discrimination and calibration with the areas under the receiver operating characteristic curve (AUC) of 0.821 (0.762–0.870) and 0.789 (0.681–0.874) in the training and test cohorts. There was a significant difference in RFS between the nomogram-predicted MVI positive and the nomogram-predicted MVI negative groups in training and test cohorts (p < 0.001 and p = 0.004 respectively). ConclusionsThe multimodal ultrasound features were effective imaging markers for preoperative prediction of MVI of HCC and the nomogram might be an effective tool to stratify the risk of recurrence and guide the individualized treatment of HCC.

MRI-based radiomics and molecular classification of hepatocellular carcinoma — is there a correlation between them?

Introduction: Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor, usually developing in the context of chronic liver disease, most commonly associated with hepatitis B (HBV) or C (HCV) infection, alcohol use, or metabolic syndrome. Hepatocellular carcinoma is a very heterogeneous cancer, both at the histological and molecular level. According to recent scientific studies based on gene expression profiling, regardless of the nomenclature used by different authors, HCC can be divided into 2 main subgroups: the non-proliferative type and the proliferative type.Purpose. A review and analysis of the available foreign literature on the textural analysis of magnetic resonance imaging in noninvasive prediction of molecular classification and molecular structure of hepatocellular carcinoma.Material and methods. We searched for scientific publications and clinical guidelines in the PubMed information and analytical system for 2016–2021 using the keywords: «MRI», «radiomics», «texture analysis», «radiogenomics», «HCC», «proliferative», «nonproliferative», «molecular». After excluding studies devoted to technical aspects and the description of individual clinical observations, 16 articles were selected for analyzes.Results. The presented review has demonstrated the broad possibilities and prospects of the use of texture analysis of magnetic resonance imaging in the study of hepatocellular cancer, including the first results in the study of molecular characteristics (signatures) of this tumor. Correlation of textural characteristics with the expression of genes of immunotherapeutic targets CTLA-4 and PD-1 was shown, and a correlation was also shown between ring enhancement of a tumor in the arterial phase by MRI with gadoxetic acid (hepatospecific contrast agent) and proliferative HCC. Texture analysis scores were the predominant independent predictor of microvascular invasion, which was the main independent risk factor for postoperative recurrence.Conclusion. Although texture analysis in the study of HCC is an actively developing field, further study of the structure of this tumor at the molecular level is necessary to develop an personalized treatment, supplement or replace tumor biopsy, as well as to develop new prognostic biomarkers in patients with HCC.

Role of Plasma methylated SEPT9 for Predicting Microvascular Invasion and Tumor Proliferation in Hepatocellular Carcinoma.

Background: Methylated SEPT9 (mSEPT9) has a role in the occurrence and development of hepatocellular carcinoma (HCC). Here, we studied the significance of plasma mSEPT9 for predicting prognosis-associated pathological parameters in patients with HCC. Methods: We retrospectively analyzed data from 205 subjects, including 111 HCC patients, 53 patients with at-risk liver disease (ARD) and 41 healthy donors (HDs). Analysis of plasma mSEPT9 was performed using methylation-specific polymerase chain reaction. Levels of mSEPT9 among different groups were compared using a nonparametric Mann-Whitney U test or a one-way ANOVA test. Correlations between pretreatment plasma mSEPT9 and clinicopathological characteristics were analyzed using the Chi-square. Univariate and multivariate analyses were used to identify factors related to microvascular invasion (MVI). Performance of variables for MVI prediction was evaluated by receiver operating characteristics curve. Results: A specific increase of plasma mSEPT9 in HCC was found when compared with ARD and HDs (HCC vs ARD, P = 1.1 × 10-5 and HCC vs HDs, P = 3.7 × 10-10). Pretreatment plasma mSEPT9 was significantly correlated tumor number (P = .004), tumor size (P = 4.6 × 10-5), MVI (P = .002) and Barcelona Clinic Liver Cancer stage (P = .012). Levels of plasma mSEPT9 correlated significantly with Ki67 expression in tumor (r = 0.356, P = 1.3 × 10-4). Univariate and multivariate analyses showed that plasma mSEPT9 and serum protein induced by vitamin K absence or antagonist-II (PIVKA-II) were independent predictors for MVI. A combination of these 2 markers exhibited a larger areas under the curve (areas under the curve [AUC] = 0.72) than mSEPT9 or PIVKA alone (AUC = 0.67 and 0.65), especially in early-stage HCC. Conclusions: Plasma mSEPT9 is a promising noninvasive biomarker for predicting MVI and tumor proliferation in HCC. Integration plasma mSEPT9 detection into clinical settings might facilitate the patient management.

Value of gadoxetic acid-enhanced MRI\xa0for microvascular invasion of small hepatocellular carcinoma: a retrospective study

BackgroundThe objective of this study was to analyze the accuracy of gadolinium–ethoxybenzyl–diethylenetriamine penta–acetic acid enhanced magnetic resonance imaging (Gd–EOB–DTPA–MRI) for predicting microvascular invasion (MVI) in patients with small hepatocellular carcinoma (sHCC) preoperatively.MethodsA total of 60 sHCC patients performed with preoperative Gd–EOB–DTPA–MRI in the Harbin Medical University Cancer Hospital from October 2018 to October 2019 were involved in the study. Univariate and multivariate analyses were performed by chi–square test and logistic regression analysis. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of Gd–EOB–DTPA–MRI were performed by receiver operating characteristic (ROC) curves.ResultsUnivariate analysis indicated that alanine aminotransferase (≥ 39.00U/L), poorly differentiated pathology, and imaging features including grim enhancement, capsule enhancement, arterial halo sign and hepatobiliary features (tumor highly uptake, halo sign, spicule sign and brush sign) were associated with the occurrence of MVI (p < 0.05). Multivariate analysis revealed that rim enhancement and hepatobiliary spicule sign were independent predictors of MVI (p < 0.05). The area under the ROC curve was 0.917 (95% confidence interval 0.838–0.996), and the sensitivity was 94.74%.ConclusionsThe morphologies of hepatobiliary phase imaging, especially the spicule sign, showed high accuracy in diagnosing MVI of sHCC. Rim enhancement played a significant role in diagnosing MVI of sHCC.