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Objective To investigate the value of liver imaging reporting and data system version 2018(LI-RADS v2018)features in predicting microvascular invasion(MVI)of hepatocellular carcinoma(HCC).Methods Ninety-two patients diagnosed as LR-5 and pathologically confirmed HCC were included and divided into MVI positive group and MVI negative group.The major and ancillary features of LI-RADS v2018 were compared between the two groups.Logistic univariate and multivariate regression analyses were used to obtain the independent risk factors for MVI,and the value of differential features in predicting MVI was also assessed using the receiver operating characteristic(ROC)curve.Results In the MVI positive group,the pathological grade of HCC was higher than that in the MVI negative group,with larger tumor diameter and higher incidence of mosaic architecture and corona enhancement.However,there was no difference in any other features.In logistic univariate analysis,tumor diameter,mosaic architecture and corona enhancement were independent risk factors for predicting the MVI,with area under the curve(AUC)values of 0.763 and 0.628 and 0.670 for the diagnosis of MVI,respectively.Conclusion In high-risk LR-5 population,tumor diameter,mosaic architecture and corona enhancement can help to predict the MVI.Accurate identification of LI-RADS v2018 features may facilitate personalized management of HCC patients.
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Objective To explore the value of dynamic nomogram constructed by multi spiral computed tomography(MSCT)features combined with inflammatory indicators in predicting the status of microvascular invasion(MVI)of hepatocellular carcinoma(HCC)before surgery.Methods The clinical and imaging data of 137 patients with postoperative pathologically confirmed HCC were analyzed retrospectively.According to the status of the MVI,they were divided into positive group(44 cases)and negative group(93 cases).Multivariate logistic regression analysis was used to screen independent risk factors for predicting the MVI status of HCC patients,and a joint prediction model was constructed,which was displayed in the form of a dynamic nomogram.The receiver operating characteristic(ROC)curve,calibration curve and Hosmer-Lemeshow test were used to evaluate the diagnostic efficiency,calibration and goodness of fit of the model,Akaike information criterion(AIC)and Bayesian information criterion(BIC)were used for comparison between the models,and a 5-fold cross-validation and decision curve analysis(DCA)were also used to evaluate the stability and clinical applicability of the model.Results Multivariate logistic regression analysis showed that necrosis and delayed-phase enhancement(DEd),and alkaline phosphatase to lymphocyte ratio(ALR)were independent risk factors for predicting MVI status in HCC patients.The area under the curve(AUC)of the dynamic nomogram was 0.721,with the sensitivity of 0.705 and the specificity of 0.656.The AIC and BIC values were 152.372 and 158.212,respectively.The calibration curve and the Hosmer-Lemeshow test showed that the model had a high degree of calibration and goodness of fit(χ2=2.372,P=0.967),the average AUC of the 5-fold cross-validation was 0.787,and the DCA showed that the nomogram model had a good clinical applicability.Conclusion The dynamic nomogram model constructed by MSCT features combined with inflammatory indicators is feasible to predict the MVI status of HCC patients before surgery,and the dynamic nomogram can directly generate the prediction results of different individuals.
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Objective:To investigate the influencing factors for microvascular invasion (MVI) in hepatocellular carcinoma based on three-dimensional visualization and the construction of its nomogram model.Methods:The retrospective cohort study method was conducted. The clinico-pathological data of 190 patients with hepatocellular carcinoma who were admitted to Henan University People′s Hospital from May 2018 to May 2021 were collected. There were 148 males and 42 females, aged (58±12)years. The 190 patients were randomly divided into the training set of 133 cases and the validation set of 57 cases by the method of random number table in the ratio of 7:3. The abdominal three-dimensional visualization system was used to characterize the tumor morphology and other imaging features. Observation indicators: (1) analysis of influencing factors for MVI in hepatocellular carcinoma; (2) construction and evaluation of nomogram model of MVI in hepatocellular carcinoma. Measurement data with normal distribution were expressed as Mean± SD, and independent sample t test was used for comparison between groups. Measurement data with skewed distribution were expressed as M( Q1, Q3), and non-parametric rank sum test was used for comparison between groups. Count data were expressed as absolute numbers, and the chi-square test was used for comparison between groups. Corresponding statistical methods were used for univariate analysis. Binary Logistic regression model was used for multivariate analysis. Receiver operator characteristic (ROC) curves were plotted, and the nomogram model was assessed by area under the curve (AUC), calibration curve, and decision curve. Results:(1) Analysis of influencing factors for MVI in hepatocellular carcinoma. Among 190 patients with hepatocellular carcinoma, there were 97 cases of positive MVI (including 63 cases in the training set and 34 cases in the validation set) and 93 cases of negative MVI (including 70 cases in the training set and 23 cases in the validation set). Results of multivariate analysis showed that alpha-fetoprotein, vascular endothelial growth factor, tumor volume, the number of tumors, and tumor morphology were independent factors affecting the MVI of patients with hepatocellular carcinoma ( odds ratio=5.06, 3.62, 1.00, 2.02, 2.59, 95% confidence interval as 1.61-15.90, 1.28-10.20, 1.00-1.01, 1.02-3.98, 1.03-6.52, P<0.05). (2) Construction and evaluation of nomogram model of MVI in hepatocellular carcinoma. The results of multivariate analysis were incorporated to construct a nomogram prediction model for MVI of hepatocellular carcinoma. ROC curves showed that the AUC of the training set of nomogram model was 0.85 (95% confidence interval as 0.79-0.92), the optimal fractional cutoff based on the Jordon′s index was 0.51, the sensitivity was 0.71, and the specificity was 0.84. The above indicators of validation set were 0.92 (95% confidence interval as 0.85-0.99), 0.50, 0.90, and 0.82, respectively. The higher total score of the training set suggested a higher risk of MVI in hepatocellular carcinoma. The calibration curves of both training and validation sets of nomogram model fitted well with the standard curves and have a high degree of calibration. The decision curve showed a high net gain of nomogram model. Conclusions:Alpha-fetoprotein, vascular endothelial growth factor, tumor volume, the number of tumors, and tumor morphology are independent influencing factors for MVI in patients with hepatocellular carcinoma. A nomogram model constructed based on three-dimensional visualized imaging features can predict MVI in hepatocellular carcinoma.
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Objective To evaluate the prediction of microvascular invasion(MVI)and its grading in patients with hepatocellular carcinoma(HCC)by computed tomography(CT)and magnetic resonance imaging(MRI)features combined with laboratory indices.Methods Using plain and enhanced CT and MRI scan to examine the participants preoperatively,the features of tumor length diam-eter,shape,number,margin and tumor capsule,whether multiple nodules are fused,whether external convex nodules are visible locally,whether blood supply vessels are visible inside or degeneration or necrosis exists,and whether low density or signal exists around the tumor that are extracted from the examination results,combined with clinical indicators,such as age,preoperative alpha-fetoprotein(AFP)level,and presence of hepatitis B surface and e antigens to analyze the occurrence of MVI in patients with HCC.Results Patients with HCC and MVI were more likely to have elevated AFP;the larger the tumor length and diameter,the higher the incidence of MVI.CT and MRI showed that the features of blurred tumor edges and incomplete local capsule were independent risk factors for MVI of HCC.All the extracted image features and clinical indicators had no predictive value for MVI grading.Conclusion A few imaging features and clin-ical indicators of HCC have definite predictive value for the occurrence of MVI.
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Objective Microvascular invasion(MVI)risk scoring model was established based on the preoperative data of hepatocellular carcinoma(HCC)patients.Methods The clinical data of 1153 HCC patients who underwent hepatectomy in Hangzhou First People's Hospital from January 2000 to December 2021 were retrospectively analyzed.Random sampling method was used to divide the samples into modeling group(n=864)and verification group(n=289)at a ratio of 3:1.The modeling group used Logistic regression analysis model to explore the independent risk factors of MVI and established a prediction model accordingly.Receiver operating characteristic(ROC)curve and correction curve were drawn to evaluate the predictive ability and performance of the risk model.Results The incidence of MVI was 24.1%(208/864)in modeling group.Multivariate Logistic regression analysis showed that alpha-fetoprotein(AFP)>160ng/ml and total tumor volume(TTV)>30cm3 were independent risk factors for MVI(P<0.05).The total score of risk scoring model was 6 points,0-1 was classified as low risk,2-3 was classified as medium risk,and 4-6 was classified as high risk.The model predicted that the area under the curve(AUC)of MVI was 0.714 in modeling group and 0.731 in verification group.The calibration diagram showed that the prediction model had good performance.Conclusion The MVI risk prediction model for HCC patients based on TTV and AFP is simple and easy to use,which is conducive to preoperative treatment decision-making and doctor-patient communication.
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OBJECTIVE@#To investigate the consistency and diagnostic performance of magnetic resonance imaging (MRI) for detecting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and the validity of deep learning attention mechanisms and clinical features for MVI grade prediction.@*METHODS@#This retrospective study was conducted among 158 patients with HCC treated in Shunde Hospital Affiliated to Southern Medical University between January, 2017 and February, 2020. The imaging data and clinical data of the patients were collected to establish single sequence deep learning models and fusion models based on the EfficientNetB0 and attention modules. The imaging data included conventional MRI sequences (T1WI, T2WI, and DWI), enhanced MRI sequences (AP, PP, EP, and HBP) and synthesized MRI sequences (T1mapping-pre and T1mapping-20 min), and the high-risk areas of MVI were visualized using deep learning visualization techniques.@*RESULTS@#The fusion model based on T1mapping-20min sequence and clinical features outperformed other fusion models with an accuracy of 0.8376, a sensitivity of 0.8378, a specificity of 0.8702, and an AUC of 0.8501 for detecting MVI. The deep fusion models were also capable of displaying the high-risk areas of MVI.@*CONCLUSION@#The fusion models based on multiple MRI sequences can effectively detect MVI in patients with HCC, demonstrating the validity of deep learning algorithm that combines attention mechanism and clinical features for MVI grade prediction.
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Humanos , Carcinoma Hepatocelular , Estudos Retrospectivos , Neoplasias Hepáticas , Imageamento por Ressonância Magnética , AlgoritmosRESUMO
Hepatocellular carcinoma is a highly aggressive malignant tumor. Although the progress of surgical technology has made some achievements in surgical treatment alone, it still fails to significantly improve the long-term survival of patients. Studies have shown that the recurrence rate of hepatocellular carcinoma is extremely high, while microvascular invasion is an important reason for early recurrence and poor prognosis. Therefore, appropriate postoperative adjuvant therapy measures are crucial to improve the survival prognosis of hepatocellular carcinoma patients with microvascular invasion.
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Objective:To explore the risk factors of microvascular invasion (MVI) in hepatocellular carcinoma (HCC), and to predict MVI preoperatively, non-invasively and accurately.Methods:A total of 150 HCC patients (183 HCC lesions) were retrospectively collected in the First Affiliated Hospital of Xi′an Jiaotong University from January 2016 to June 2022.The clinical data and hematological data, gray-scale ultrasonography (US), contrast-enhanced ultrasonography (CEUS), enhanced magnetic resonance imaging with gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (EOB-MRI) and pathological data of these patients were recorded. According to the pathological diagnosis of MVI, the lesions were divided into MVI (+ ) group and MVI (-) group. The indicators between the two groups were compared. All 183 lesions were put into the training set, and the prediction model with nomogram was constructed according to the risk factors of MVI selected by multivariate Logistic regression. The internal verification was carried out by ten-fold cross-validation method.Results:There were significant statistical differences in the following parameters between MVI (+ ) group ( n=109) and MVI (-) group ( n=74) (all P<0.05). These were cirrhosis, serological parameters (alpha-fetoprotein, albumin, total bilirubin), qualitative indexes of US (size, boundary, internal echo), qualitative indexes of CEUS (hyper/iso/hypovascularity of lesions in arterial phase, portal phase, and delayed phase compared with hepatic parenchyma), and quantitative indexes of EOB-MRI [post enhancement rate (post ratio) and gadolinium disodium rate (EOB ratio)] calculated mainly in terms of lesions and surrounding liver parenchyma in hepatobiliary phase and unenhanced T1 images). Finally, cirrhosis of patients, the size, boundary, internal echo of lesions in US; arterial phase (AP), portal phase (PP), post-vascular phase (PVP) features in CEUS; the EOB rate and post rate of EOB-MRI entered the prediction model of MVI. The training set exhibited good calibration and net gain rate. The areas under the ROC curve for the training set and the validation set were 0.981 and 0.961, respectively, while the diagnostic accuracy were 92.9% and 85.8%, respectively. Conclusions:The model constructed mainly by multimodality imaging methods can achieve favorable predictive performance for MVI, which provides valuable ideas for noninvasively predicting the incidence of MVI and optimizing the MVI-related treatment of MVI in HCC patients.
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Objective:To analyze the value of laminin γ2 (LAMC2) in the diagnosis of hepatocellular carcinoma (HCC) and the difference in patients with different types of microvascular invasion (MVI).Methods:A cohort of 100 patients with HCC who underwent surgical treatment at the Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital from January 2021 to March 2022 were prospectively enrolled. There were 80 males and 20 females, aged (55.7±11.1) years. The data of 17 patients with hepatic hemangioma without cirrhosis who underwent operation at the same hospital during the study period were collected to serve as the control group (6 males, 11 females), aged (42.8±9.8) years. LAMC2 in serum was determined by enzyme linked immunosorbent assay. The levels of alpha-fetoprotein (AFP) and LAMC2 were compared between the two groups, and receiver operating characteristic (ROC) curves were drawn to compare these two markers in the diagnosis of HCC. The LAMC2 of different MVI patients were compared.Results:The levels of LAMC2 and AFP were 1 334.2(838.9, 2 656.0) pg/ml and 19.0(4.6, 778.6) μg/L in the HCC group, which were significantly higher than 375.2(221.2, 691.7)pg/ml and 3.3(2.5, 3.5) μg/L in the control group ( Z=-4.32, -4.63, both P<0.001). The areas under the ROC curve were 0.829(95% CI: 0.748-0.892) for LAMC2 and 0.852(95% CI: 0.769-0.910) for AFP, and was 0.949(95% CI: 0.911-0.988) for using both in the diagnoses. The diagnostic efficacy of combining LAMC2 and AFP was significantly better than that of LAMC2 alone and AFP alone (area under ROC: Z=3.15, 3.07, P=0.002, 0.002). When the patients were divided into the M0 group (61 patients), the M1 Group (25 patients) and the M2 Group (14 patients) based on MVIs, the concentrations of LAMC2 were 1 168.6(834.3, 2 521.4) pg/ml, 942.2(614.0, 2 056.6) pg/ml and 3 128.4(1 852.7, 7 191.3) pg/ml, respectively. The level of LAMC2 in the M2 group was significantly higher than that in the M0 and M1 groups ( Z=-3.46, -3.32, P=0.001, 0.004). Conclusion:The diagnostic efficacy of LAMC2 combined with AFP for HCC was significantly higher than that of either LAMC2 alone or AFP alone. Serum LAMC2 levels were significant different among the groups of HCC patients with different types of MVI.
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Objective:To develop and validate a nomogram model for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on preoperative enhanced computed tomography imaging features and clinical data.Methods:The clinical data of 210 patients with HCC undergoing surgery in the Second Affiliated Hospital of Anhui Medical University from May 2018 to May 2022 were retrospectively analyzed, including 172 males and 38 females, aged (59±10) years old. Patients were randomly divided into the training group ( n=147) and validation group ( n=63) by systematic sampling at a ratio of 7∶3. Preoperative enhanced computed tomography imaging features and clinical data of the patients were collected. Logistic regression was conducted to analyze the risk factors for HCC with MVI, and a nomogram model containing the risk factors was established and validated. The diagnostic efficacy of predicting MVI status in patients with HCC was assessed by receiver operating characteristic (ROC) curve, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) of the subjects in the training and validation groups. Results:The results of multifactorial analysis showed that alpha fetoprotein ≥400 μg/ml, intra-tumor necrosis, tumor length diameter ≥3 cm, unclear tumor border, and subfoci around the tumor were independent risk factors predicting MVI in HCC. A nomogram model was established based on the above factors, in which the area under the curve (AUC) of ROC were 0.866 (95% CI: 0.807-0.924) and 0.834 (95% CI: 0.729-0.939) in the training and validation groups, respectively. The DCA results showed that the predictive model thresholds when the net return is >0 ranging from 7% to 93% and 12% to 87% in the training and validation groups, respectively. The CIC results showed that the group of patients with predictive MVI by the nomogram model are highly matched with the group of patients with confirmed MVI. Conclusion:The nomogram model based on the imaging features and clinical data could predict the MVI in HCC patients prior to surgery.
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Objective:To construct a combined radiomics model based on preoperative enhanced computed tomography (CT) examination for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC), and provide biological explanations for the radiomics model.Methods:The retrospective cohort study was conducted. The messenger RNA (mRNA) of 424 HCC patients, the clinicopathological data of 39 HCC patients entered into the Cancer Genome Atlas database from its establishment until January 2023, and the clinicopathological data of 53 HCC patients who were admitted to the Gansu Provincial People′s Hospital from January 2020 to January 2023 were collected. The 92 HCC patients were randomly divided into a training dataset of 64 cases and a test dataset of 28 cases with a ratio of 7∶3 based on a random number table method. The CT images of patients in the arterial phase and portal venous phase as well as the corresponding clinical data were analyzed. The 3Dslicer software (version 5.0.3) was used to register the CT images in the arterial phase and portal venous phase and delineate the three-dimensional regions of interest. The original images were preprocessed and the corresponding features were extracted by the open-source software FAE (version 0.5.5). After selecting features using the Least Absolute Shrinkage and Selection Operator, the radiomics model was constructed and the radiomics score (R-score) was calculated. The nomogram was constructed by integrating clinical parameters, imaging features and R-score based on Logistic regression. The gene modules related to radiomics model were obtained and subjected to enrichment analysis by conducting weighted gene co-expression network analysis and correlation analysis. Observation indicators: (1) comparison of clinical characteristics of patients with different MVI properties; (2) establishment of MVI risk model; (3) evaluation of MVI risk model; (4) clustering of gene modules; (5) functional enrichment of feature-correlated gene modules. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the independent sample t test. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Comparison of count data was conducted using the chi-square test. The intra-/inter-class correlation coefficient (ICC) was used to assess the inter-observer consistency of radiomics feature extracted by different observers. ICC >0.75 indicated a good consistency in feature extraction. The Logistic regression model was used for univariate and multivariate analyses. The receiver operating characteristic curve was drawn, and the area under curve (AUC), the decision curve and the calibration curve were used to evaluate the diagnostic efficacy and clinical practicality of the model. Results:(1) Comparison of clinical characteristics of patients with different MVI properties. Of 92 HCC patients, there were 47 cases with MVI-positive and 45 cases with MVI-negative, and there were significant differences in hepatitis, tumor diameter, peritumoral enhancement, intratumoral arteries, pseudocapsule and smoothness of tumor margin between them ( χ2=5.308, 9.977, 47.370, 32.368, 21.105, 31.711, P<0.05). (2) Establishment of MVI risk model. A total of 1 781 features were extrac-ted from arterial and portal venous phases of the intratumoral and peritumoral regions. After feature dimension reduction, 8 radiomics features were selected from arterial and portal venous phases to construct the combined model. Results of multivariate analysis showed that peritumoral enhancement, intratumoral arteries, pseudocapsule, smoothness of tumor margins, and R-score were independent risk factors for MVI in patients with HCC [ hazard ratio=0.049, 0.017, 0.017, 0.021, 2.539, 95% confidence interval ( CI) as 0.005-0.446, 0.001-0.435, 0.001-0.518, 0.001-0.473, 1.220-5.283, P<0.05]. A nomogram model was constructed incorporating peritumoral enhancement, intratumoral arteries, pseudocapsule, smoothness of tumor margins, and R-score. (3) Evaluation of the MVI risk model. The AUC of radiomics model was 0.923 (95% CI as 0.887-0.944) and 0.918 (95% CI as 0.894-0.945) in the training dataset and test dataset, respectively. The AUC of nomogram model, incorpora-ting both the R-score and radiomics features, was 0.973 (95% CI as 0.954-0.988) and 0.962 (95% CI as 0.942-0.987) in the training dataset and test dataset, respectively. Results of decision curve showed that the nomogram had better clinical utility compared to the R-score. Results of calibration curve showed good consistency between the actual observed outcomes and the nomogram or the R-score. (4) Clustering of gene module. Results of weighted gene co-expression network analysis showed that 8 gene modules were obtained. (5) Functional enrichment of feature-related gene modules. Results of correlation analysis showed 4 gene modules were significantly associated with radiomics features. The radiomics features predicting of MVI may be related to pathways such as the cell cycle, neutrophil extracellular trap formation, and PPAR signaling pathway. Conclusions:The combined radiomics model based on preoperative enhanced CT imaging can predict the MVI status of HCC. By obtaining mRNA gene expression profiles associated with radiomics features, a biological interpretation of the radiomics model is provided.
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Objective: To investigate the risk factors of microvascular invasion (MVI) in China liver cancer staging system stage Ⅰa (CNLC Ⅰa) hepatocellular carcinoma (HCC), and develop a nomogram for predicting MVI based on clinical and radiographic data. Methods: This retrospective study focused on CNLC Ⅰa HCC patients who underwent radical resection at the Cancer Hospital, Chinese Academy of Medical Sciences from January 2016 to December 2020. Patients' clinical characteristics and laboratory test results and pre-surgery gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging results were collected. The clinical and radiographic risk factors for MVI were identified by univariate and multivariate logistic regression analyses and used for the construction of the predictive nomogram. The nomogram model was then internally validated, and its performance was assessed. Results: A total of 104 patients were divided into the MVI-positive group (n=28) and the MVI-negative group (n=76). Multivariate logistic regression analysis at the P<0.1 level identified serum alpha-ferroprotein >7 ng/ml, total bilirubin >21 μmol/L, prothrombin time >12.5 s, non-smooth margin, and incomplete or absent capsule as risk factors of MVI, based on which a nomogram model was built. The model achieved an area under the curve (AUC) value of 0.867 (95% confidence interval, 0.791-0.944) in the internal validation. The sensitivity and specificity of the nomogram model were 0.786 and 0.829, respectively, with the prediction curve nearly overlapping the ideal curve. Based on the Hosmer-Lemeshow test, the predicted and real results were not significantly different (P=0.956). Conclusions: The probability of MVI of CNLC Ⅰa HCC can be objectively predicted by the monogram model that quantifies the clinical and radiographic risk factors. The model can also help clinicians select individualized surgical plans to improve the long-term prognosis of patients.
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Objective: To investigate the risk factors of microvascular invasion (MVI) in China liver cancer staging system stage Ⅰa (CNLC Ⅰa) hepatocellular carcinoma (HCC), and develop a nomogram for predicting MVI based on clinical and radiographic data. Methods: This retrospective study focused on CNLC Ⅰa HCC patients who underwent radical resection at the Cancer Hospital, Chinese Academy of Medical Sciences from January 2016 to December 2020. Patients' clinical characteristics and laboratory test results and pre-surgery gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging results were collected. The clinical and radiographic risk factors for MVI were identified by univariate and multivariate logistic regression analyses and used for the construction of the predictive nomogram. The nomogram model was then internally validated, and its performance was assessed. Results: A total of 104 patients were divided into the MVI-positive group (n=28) and the MVI-negative group (n=76). Multivariate logistic regression analysis at the P<0.1 level identified serum alpha-ferroprotein >7 ng/ml, total bilirubin >21 μmol/L, prothrombin time >12.5 s, non-smooth margin, and incomplete or absent capsule as risk factors of MVI, based on which a nomogram model was built. The model achieved an area under the curve (AUC) value of 0.867 (95% confidence interval, 0.791-0.944) in the internal validation. The sensitivity and specificity of the nomogram model were 0.786 and 0.829, respectively, with the prediction curve nearly overlapping the ideal curve. Based on the Hosmer-Lemeshow test, the predicted and real results were not significantly different (P=0.956). Conclusions: The probability of MVI of CNLC Ⅰa HCC can be objectively predicted by the monogram model that quantifies the clinical and radiographic risk factors. The model can also help clinicians select individualized surgical plans to improve the long-term prognosis of patients.
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Objective:To establish and verify a nomogram model based on MRI liver imaging reporting and data system (LI-RADS) features for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) following the Milan criteria.Methods:A retrospective analysis was conducted on data from 118 HCC patients (121 lesions) confirmed by pathology from June 2016 to June 2022 at the Third Affiliated Hospital of Soochow University. Forty-seven HCCs were diagnosed as MVI-positive and 74 HCCs as MVI-negative. The data was randomly divided into the training set (83 patients with 84 HCCs, including 31 MVI-positive and 53 MVI-negative HCCs) and the test set (35 patients with 37 HCCs, including 16 MVI-positive and 21 MVI-negative HCCs) using cross-validation method. HCC imaging features were evaluated based on LI-RADS (version 2018). In the training set, the χ 2 test was used to compare the differences in LI-RADS features between the MVI-positive group and the MVI-negative group. The logistic regression analysis was conducted to identify independent risk factors for predicting MVI-positive and to construct the nomogram model. The receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the performance and clinical benefits of the nomogram model in predicting MVI tumors. Results:There were statistically significant differences between the MVI-positive group and the MVI-negative group in terms of tumor size, tumor margin, mosaic architecture, and corona enhancement ( P<0.05). Multivariate logistic analysis results showed that HCC maximum diameter>3 cm (OR=1.427, 95%CI 1.314-12.227, P=0.009), nonsmooth tumor margin (OR=3.167, 95%CI 1.227-461.232, P=0.041), mosaic architecture (OR=1.769, 95%CI 1.812-61.434, P=0.022), and corona enhancement (OR=4.015, 95%CI 3.327-836.384, P=0.011) were independent risk factors for predicting MVI-positive tumors. Based on the independent predictors, the constructed nomogram model demonstrated an area under the ROC curve of 0.863 (95%CI 0.768-0.947) and 0.887 (95%CI 0.804-0.987) in the training and test sets for predicting MVI tumors, respectively. DCA showed that the curve of the nomogram model was consistently above the treat-all and treat-none strategies across all reasonable threshold probabilities in the training set, indicating that patients could obtain clinical benefits from the model. Conclusions:The preoperative nomogram model based on MRI LI-RADS features can effectively predict MVI in HCC following the Milan criteria, which could benefit the patients.
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Objective:To analyze the effects of microvascular invasion (MVI) and anatomical hepatectomy on early recurrence and survival of patients with hepatocellular carcinoma (HCC).Methods:The data of 246 patients with HCC admitted to 215 Hospital of Shaanxi Nuclear Industry, Chinese PLA General Hospital and Beijing Tsinghua Chang Gung Hospital from July 2008 to June 2019 were retrospectively analyzed, including 208 males and 38 females, aged (53.8±9.6) years. According to the occurrence of MVI, 246 patients were divided into the MVI group ( n=83) and control group ( n=163, without MVI). Hepatitis B virus (HBV) infection, preoperative alpha-fetoprotein (AFP), maximum tumor diameter, intraoperative blood loss were compared between the two groups. The recurrence-free survival and cumulative survival were compared between the two groups before and after the inverse probability weighted correction for propensity score. Results:The propensity score was calculated by logistic regression model. After inverse probability weighted correction, the virtual sample size was 247 cases (82 cases in MVI group and 165 cases in control group). The proportion of HBV infection, with a serum level of AFP > 200 μg/L, the maximum diameter of tumor and the intraoperative blood loss were higher in MVI group (all P<0.05). The risk of early recurrence in patients undergoing anatomical hepatectomy ( n=107) was lower than that in patients undergoing non-anatomical hepatectomy ( n=139) (univariate Cox regression analysis of HR=1.60, 95% CI: 1.06 to 2.42, P=0.020), but the overall survival was comparable (univariate Cox regression analysis of HR=1.66, 95% CI: 0.80 to 3.42, P=0.200). The recurrence-free survival (RFS) of MVI group was lower than that of the control group, and the postoperative cumulative survival rate was also lower before the inverse probability weighted correction of the tendency score. The RFS in MVI group was lower than that in control group after the tendency score was adjusted by inverse probability weighting ( HR=2.62, 95% CI: 1.61 to 4.27, P<0.001). There was no significant difference in the cumulative survival between the MVI and control group ( HR=2.09, 95% CI: 0.89 to 4.93, P=0.050). Conclusion:MVI is associated with early postoperative recurrence in patients with HCC, and the early recurrence rate after anatomical hepatectomy is lower than that after non-anatomical hepatectomy.
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Objective:To screen preoperative microvascular invasion (MVI)-related indicators in patients with hepatocellular carcinoma by machine learning, and to construct a predictive model for predicting MVI and evaluate it.Methods:The clinical data of hepatocellular carcinoma patients who underwent radical resection from January 2018 to March 2023 in General Hospital of Ningxia Medical University were retrospectively analyzed. A total of 437 patients were enrolled, including 325 males and 112 females, aged (56.3±13.6) years. The 437 patients were divided into a training set ( n=305) and a test set ( n=132) by computer-generated random numbers on a 7∶3 basis; the training set was used to construct the predictive model as well as to internally validate it by the five-fold cross-validation method, and the test set was used to externally validate the model. Two machine learning Boruta algorithm and LASSO regression, were used to screen MVI characteristic variables and construct multifactorial logistic regression prediction models. Receiver operating characteristic (ROC) curve, calibration curves, and decision curve were evaluated for predictive modeling, applying Shapley's additive explanatory analysis (SHAP) of the significance of key variables. Results:The intersection (5 variables) of 8 characteristic variables selected by Boruta algorithm and 8 variables selected by LASSO regression were selected: aspartate aminotransferase/lymphocyte ratio (ALR), tumor margin, intratumbral necrosis, tumor number and tumor maximum diameter, and the logistic regression model was constructed. The area under ROC curve for predicting the MVI were 0.77 (95% CI: 0.70-0.82) (training set), 0.76 (95% CI: 0.63-0.87) (validation set), and 0.84 (95% CI: 0.78-0.91) (test set). The prediction results of calibration curve logistic regression model were close to those of reagent, and the analysis of decision curve indicates that the model had good clinical application value. According to the mean absolute SHAP value, the order of importance was tumor margin, tumor maximum diameter, tumor number, ALR, and intratumoral necrosis. Conclusion:Tumor margin, tumor maximum diameter, tumor number, ALR and intratumoral necrosis were independent influencing factors for hepatocellular carcinoma associated with MVI, and the logistic regression model based on these factors was effective in predicting MVI.
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Abstract The power of computed tomography (CT) radiomics for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) demonstrated in current research is variable. This systematic review and meta-analysis aim to evaluate the value of CT radiomics for MVI prediction in HCC, and to investigate the methodologic quality in the workflow of radiomics research. Databases of PubMed, Embase, Web of Science, and Cochrane Library were systematically searched. The methodologic quality of included studies was assessed. Validation data from studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement type 2a or above were extracted for meta-analysis. Eleven studies were included, among which nine were eligible for meta-analysis. Radiomics quality scores of the enrolled eleven studies varied from 6 to 17, accounting for 16.7%-47.2% of the total points, with an average score of 14. Pooled sensitivity, specificity, and Area Under the summary receiver operator Characteristic Curve (AUC) were 0.82 (95% CI 0.77-0.86), 0.79 (95% CI 0.75-0.83), and 0.87 (95% CI 0.84-0.91) for the predictive performance of CT radiomics, respectively. Meta-regression and subgroup analyses showed radiomics model based on 3D tumor segmentation, and deep learning model achieved superior performances compared to 2D segmentation and non-deep learning model, respectively (AUC: 0.93 vs. 0.83, and 0.97 vs. 0.83, respectively). This study proves that CT radiomics could predict MVI in HCC. The heterogeneity of the included studies precludes a definition of the role of CT radiomics in predicting MVI, but methodology warrants uniformization in the radiology community regarding radiomics in HCC.
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Objective:To investigate the application value of peripheral blood circulating tumor cell (CTC) classification in the prediction of preoperative microvascular invasion of hepato-cellular carcinoma (HCC).Methods:The retrospective case-control study was conducted. The clinico-pathological data of 102 HCC patients who were admitted to Zhengzhou University People's Hospital from September 2018 to September 2020 were collected. There were 71 males and 31 females, aged from 29 to 80 years, with a median age of 57 years. Observation indicators: (1) surgical situations; (2) results of CTC detection and microvascular invasion in HCC patients; (3) results of CTC classification and the best cut-off value of CTC classification in the prediction of microvascular invasion in HCC; (4) influencing factors for microvascular invasion in HCC; (5) comparison of clinicopathological features in HCC patients with different cell counts in mesenchymal phenotype of CTC (M-CTC). Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the independent sample t test. Measurement data with skewed distribution were represented as M(range) or M( Q1, Q3), and comparison between groups was analyzed using the nonparametric rank sum U test. Count data were described as absolute numbers or percentages, and comparison between groups was analyzed using the chi-square test. The receiver operating characteristic (ROC) curve was used to determine the best cut-off value for the risk of microvascular invasion in patients. Univariate and multivariate analysis were performed using the Logistic regression model. Results:(1) Surgical situations. All 102 patients underwent surgery successfully, including 17 cases undergoing local hepatectomy, 43 cases under-going segmentectomy, 22 cases undergoing hepatic lobectomy, 13 cases undergoing hemilectomy and 7 cases undergoing enlarged hemilectomy. The operation time and the volume of intraoperative blood loss were 235(147,293)minutes and 300(110,500)mL of the 102 patients, respectively. (2) Results of CTC detection and microvascular invasion in HCC patients. Of 102 patients, there were 36 casas with epithelial phenotype of CTC (E-CTC), 86 cases with hybrid phenotype of CTC (H-CTC), 30 cases with M-CTC, respectively, and the total CTC (T-CTC) were positive in 89 cases. Results of postoperative pathological examination showed that there were 40 cases with micro-vascular inva-sion and 62 cases without microvascular invasion in the 102 patients. Of the 40 patients with micro-vascular invasion, the count of E-CTC, H-CTC, M-CTC and T-CTC were 0(0,1) per 5 mL, 4(2,5) per 5 mL, 1(0,2) per 5 mL and 5(3,8) per 5mL, respectively. The above indicators of the 62 cases without microvascular invasion were 0(0,1) per 5 mL, 3(1,5) per 5 mL, 0(0,0) per 5 mL and 3(2,6) per 5 mL, respectively. There were significant differences in the count of M-CTC and T-CTC between patients with and without microvascular invasion ( Z=-4.83, -2.96, P<0.05). (3) Results of CTC classi-fication and the best cut-off value of CTC classification in the prediction of microvascular invasion in HCC. The ROC curve showed that best cut-off value of M-CTC and T-CTC counts in the prediction of microvascular invasion in HCC were 1 per 5 mL and 4 per 5 mL, respectively, with the area under curve, the corresponding specificity, sensitivity were 0.70 (95% confidence interval as 0.60-0.81, P<0.05), 75.8%, 62.9% and 0.67 (95% confidence interval as 0.57-0.78, P<0.05), 60.0%, 72.5%, respec-tively. (4) Influencing factors for microvascular invasion in HCC. Result of univariate analysis showed that alpha fetoprotein (AFP), aspartate aminotransferase (AST), tumor diameter, tumor number, tumor margin, Barcelona clinic liver cancer staging, M-CTC counts and T-CTC counts were related factors influencing microvascular invasion in HCC ( odds ratio=3.13, 0.43, 4.92, 5.65, 2.54, 2.93, 8.25, 4.47, 95% confidence interval as 1.34-7.33, 0.19-0.98, 2.09-11.58, 2.35-13.63, 1.13-5.75, 1.27-6.74, 3.13-21.75, 1.88-10.61, P<0.05). Result of multivariate analysis showed that tumor diameter >5 cm, tumor number as multiple and M-CTC counts ≥1 per 5 mL were independent risk factors influencing microvascular invasion in HCC ( odds ratio=2.97, 4.14, 4.36, 95% c onfidence interval as 1.01-8.70, 1.14-15.02, 1.36-13.97, P<0.05). (5) Comparison of clinicopathological features in HCC patients with different cell counts in M-CTC. The 102 HCC patients were divided into the high M-CTC group of 30 cases with M-CTC counts ≥1 per 5 mL and the low M-CTC group of 72 cases with M-CTC counts <1 per 5 mL, according to the best cut-off value of M-CTC counts. Cases with hepatitis, cases with AFP >400 μg/L, cases with AST >35 U/L, cases with irregular tumor margin, cases with tumor diameter >5 cm, cases with tumor number as multiple and cases with micro-vascular invasion were 22, 17, 13, 21, 18, 16 and 22 in the high M-CTC group of 30 cases. The above indicators were 35, 18, 48, 26, 25, 21 and 18 in the low M-CTC group of 72 cases. There were significant differences in the above indicators between the high M-CTC group and the low M-CTC group ( χ2=5.25, 9.42, 4.80, 9.79, 5.55, 5.35, 20.75, P<0.05). Conclusions:The epithelial-mesen-chymal phenotype of peripheral blood CTC can be used to predict the preoperative microvascular invasion in HCC. Tumor diameter >5 cm, tumor number as multiple and M-CTC counts ≥1 per 5 mL are independent risk factors influencing microvascular invasion in HCC patients.
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Objective:To investigate the patients with hepatocellular carcinoma suitable for transcatheter arterial chemoembolization (TACE) after radical resection who were screened based on microvascular invasion (MVI) and Ki-67 expression.Methods:Of 400 patients with hepatocellular carcinoma who underwent radical resection in the Affiliated Hospital of Qingdao University from January 2013 to December 2019 were included and analyzed retrospectively, including 324 males and 76 females, aged (59.7±9.8) years, ranging from 32 to 87 years. According to whether they received adjuvant TACE treatment after operation, they were divided into simple operation group ( n=210) and TACE + operation group ( n=190). The recurrence in the first year after operation was followed up by outpatient reexamination. Univariate and multivariate Cox regression analysis were used to analyze the influencing factors of recurrence free survival after surgical resection. Subgroup analysis was performed according to Ki-67 and MVI to compare the recurrence free survival. Results:Multivariate Cox regression analysis showed that patients with proportion of Ki-67 positive cells ≥27.5% ( HR=2.073, 95% CI: 1.433-3.000, P<0.001) and MVI positive ( HR=2.339, 95% CI: 1.584-3.456, P<0.001) had increased risk of recurrence after radical resection. The 1-year cumulative recurrence free survival rate in the simple operation group was 70.0%, and there was no significant difference compared with 67.9% in the operation + TACE group( χ 2=0.08, P=0.774). Subgroup analysis: in the low expression of Ki-67 combined with negative MVI group ( n=128), the cumulative recurrence free survival rate of one year after operation in the simple operation group ( n=84) was 91.7%, which was significantly higher than 72.7% in the operation + TACE group ( n=44)( χ 2=8.22, P=0.004). There was no significant difference in the 1-year cumulative recurrence free survival rate between the simple operation group and the operation + TACE group (both P>0.05) in patients of Ki-67 high expression combined with MVI negative or Ki-67 low expression combined with MVI positive. In the Ki-67 high expression combined with MVI positive group ( n=107), the cumulative one-year recurrence free survival rate in the simple operation group ( n=62) was 40.3%, which was significantly lower than 60.0% in the operation + TACE group ( n=45)(χ 2=4.22, P=0.040). Conclusion:High expression of Ki-67 (≥27.5%) combined with positive MVI are the prediction factors for postoperative TACE treatment. Low expression Ki-67 (<27.5%) combined with negative MVI was contraindicated for postoperative TACE treatment.
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Objective:To establish a clinical diagnostic scoring model for preoperative predicting hepatocellular carcinoma (HCC) microvascular invasion (MVI) based on gadolinium-ethoxybenzyl-diethylenetriamine pentacetic acid (Gd-EOB-DTPA) enhanced MRI, and verify its effectiveness.Methods:From January 2014 to December 2020, a total of 251 cases with pathologically confirmed HCC from Tianjin First Central Hospital and Jilin University First Hospital were retrospectively collected to serve as the training set, while 57 HCC patients from Tianjin Medical University Cancer Hospital were recruited as an independent external validation set. The HCC patients were divided into MVI positive and MVI negative groups according to the pathological results. The tumor maximum diameters and apparent diffusion coefficient (ADC) values were measured. On the Gd-EOB-DTPA MRI images, tumor morphology, peritumoral enhancement, peritumoral low intensity (PTLI), capsule, intratumoral artery, intratumoral fat, intratumoral hemorrhage, and intratumoral necrosis were observed. Univariate analysis was performed using the χ 2 test or the independent sample t-test. The independent risk factors associated with MVI were obtained in the training set using a multivariate logistic analysis. Points were assigned to each factor according to the weight value to establish a preoperative score model for predicting MVI. The receiver operating characteristic (ROC) curve was used to determine the score threshold and to verify the efficacy of this scoring model in predicting MVI in the independent external validation set. Results:The training set obtained 98 patients in the MVI positive group and 153 patients in the MVI negative group, while the external validation set obtained 16 patients in the MVI positive group and 41 patients in the MVI negative group. According to logistic analysis, tumor maximum diameter>3.66 cm (OR 3.654, 95%CI 1.902-7.018), hepatobiliary PTLI (OR 9.235, 95%CI 4.833-16.896) and incomplete capsule (OR 6.266, 95%CI 1.993-9.345) were independent risk factors for MVI in HCC, which were assigned scores of 3, 4 and 2, respectively. The total score ranged from 0 to 9. In the external validation set, ROC curve analysis showed that the area under the curve of the scoring model was 0.918 (95%CI 0.815-0.974, P=0.001). When the score>4 was used as the threshold, the accuracy, sensitivity, and specificity of the model in predicting MVI were 84.2%, 81.3%, and 85.4%, respectively. Conclusions:A scoring model based on Gd-EOB-DTPA-enhanced MRI provided a convenient and reliable way to predict MVI preoperatively.