Prediction of Microvascular Invasion and Its M2 Classification in Hepatocellular Carcinoma Based on Nomogram Analyses

Background and AimsAs a key pathological factor, microvascular invasion (MVI), especially its M2 grade, greatly affects the prognosis of liver cancer patients. Accurate preoperative prediction of MVI and its M2 classification can help clinicians to make the best treatment decision. Therefore, we aimed to establish effective nomograms to predict MVI and its M2 grade.MethodsA total of 111 patients who underwent radical resection of hepatocellular carcinoma (HCC) from January 2015 to September 2020 were retrospectively collected. We utilized logistic regression and least absolute shrinkage and selection operator (LASSO) regression to identify the independent predictive factors of MVI and its M2 classification. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to select the potential predictive factors from the results of LASSO and logistic regression. Nomograms for predicting MVI and its M2 grade were then developed by incorporating these factors. Area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were respectively used to evaluate the efficacy, accuracy, and clinical utility of the nomograms.ResultsCombined with the results of LASSO regression, logistic regression, and IDI and NRI analyses, we founded that clinical tumor-node-metastasis (TNM) stage, tumor size, Edmondson–Steiner classification, α-fetoprotein (AFP), tumor capsule, tumor margin, and tumor number were independent risk factors for MVI. Among the MVI-positive patients, only clinical TNM stage, tumor capsule, tumor margin, and tumor number were highly correlated with M2 grade. The nomograms established by incorporating the above variables had a good performance in predicting MVI (AUCMVI = 0.926) and its M2 classification (AUCM2 = 0.803). The calibration curve confirmed that predictions and actual observations were in good agreement. Significant clinical utility of our nomograms was demonstrated by DCA.ConclusionsThe nomograms of this study make it possible to do individualized predictions of MVI and its M2 classification, which may help us select an appropriate treatment plan.

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A Nomogram for Preoperative Estimation of Microvascular Invasion Risk in Hepatocellular Carcinoma: Single-Center Analyses With Internal Validation

Frontiers in Oncology ◽

10.3389/fonc.2021.616976 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jiarui Yang ◽

Shuguang Zhu ◽

Juanjuan Yong ◽

Long Xia ◽

Xiangjun Qian ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Liver Cancer ◽

Predictive Model ◽

Predictive Factors ◽

Microvascular Invasion ◽

Lasso Regression ◽

Internal Validation ◽

Treat All ◽

Upper Abdomen

BackgroundMicrovascular invasion (MVI) is highly associated with poor prognosis in patients with liver cancer. Predicting MVI before surgery is helpful for surgeons to better make surgical plan. In this study, we aim at establishing a nomogram to preoperatively predict the occurrence of microvascular invasion in liver cancer.MethodA total of 405 patients with postoperative pathological reports who underwent curative hepatocellular carcinoma resection in the Third Affiliated Hospital of Sun Yat-sen University from 2013 to 2015 were collected in this study. Among these patients, 290 were randomly assigned to the development group while others were assigned to the validation group. The MVI predictive factors were selected by Lasso regression analysis. Nomogram was established to preoperatively predict the MVI risk in HCC based on these predictive factors. The discrimination, calibration, and effectiveness of nomogram were evaluated by internal validation.ResultsLasso regression analysis revealed that discomfort of right upper abdomen, vascular invasion, lymph node metastases, unclear tumor boundary, tumor necrosis, tumor size, higher alkaline phosphatase were predictive MVI factors in HCC. The nomogram was established with the value of AUROC 0.757 (0.716–0.809) and 0.768 (0.703–0.814) in the development and the validation groups. Well-fitted calibration was in both development and validation groups. Decision curve analysis confirmed that the predictive model provided more benefit than treat all or none patients. The predictive model demonstrated sensitivity of 58.7%, specificity of 80.7% at the cut-off value of 0.312.ConclusionNomogram was established for predicting preoperative risk of MVI in HCC. Better treatment plans can be formulated according to the predicted results.

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The importance of a nonsmooth tumor margin and incomplete tumor capsule in predicting HCC microvascular invasion on preoperative imaging examination: a systematic review and meta-analysis

Clinical Imaging ◽

10.1016/j.clinimag.2020.11.057 ◽

2020 ◽

Author(s):

Ling Song ◽

Jiawu Li ◽

Yan Luo

Keyword(s):

Systematic Review ◽

Meta Analysis ◽

Microvascular Invasion ◽

Preoperative Imaging ◽

Tumor Margin ◽

Tumor Capsule ◽

Imaging Examination

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Diagnostic performance of imaging features in the HBP of gadoxetate disodium-enhanced MRI for microvascular invasion in hepatocellular carcinoma: a meta-analysis

Acta Radiologica ◽

10.1177/02841851211038806 ◽

2021 ◽

pp. 028418512110388

Author(s):

Yuhui Deng ◽

Dawei Yang ◽

Hui Xu ◽

Ahong Ren ◽

Zhenghan Yang

Keyword(s):

Hepatocellular Carcinoma ◽

Diagnostic Performance ◽

Meta Analysis ◽

Microvascular Invasion ◽

Index Test ◽

Imaging Features ◽

Gadoxetate Disodium ◽

Reference Test ◽

Tumor Margin ◽

Meta Regression

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.

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Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Preoperative Gd-EOB-DTPA-Dynamic Enhanced MRI and Histopathological Correlation

Contrast Media & Molecular Imaging ◽

10.1155/2018/9674565 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 10

Author(s):

Mengqi Huang ◽

Bing Liao ◽

Ping Xu ◽

Huasong Cai ◽

Kun Huang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prediction Model ◽

Tumor Size ◽

Univariate Analysis ◽

Microvascular Invasion ◽

Arterial Phase ◽

Hepatobiliary Phase ◽

Imaging Features ◽

Tumor Margin ◽

Enhanced Mri

Objective. To investigate the imaging features observed in preoperative Gd-EOB-DTPA-dynamic enhanced MRI and correlated with the presence of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods. 66 HCCs in 60 patients with preoperative Gd-EOB-DTPA-dynamic enhanced MRI were retrospectively analyzed. Features including tumor size, signal homogeneity, tumor capsule, tumor margin, peritumor enhancement during mid-arterial phase, peritumor hypointensity during hepatobiliary phase, signal intensity ratio on DWI and apparent diffusion coefficients (ADC), T1 relaxation times, and the reduction rate between pre- and postcontrast enhancement images were assessed. Correlation between these features and histopathological presence of MVI was analyzed to establish a prediction model. Results. Histopathology confirmed that MVI were observed in 17 of 66 HCCs. Univariate analysis showed tumor size (p=0.003), margin (p=0.013), peritumor enhancement (p=0.001), and hypointensity during hepatobiliary phase (p=0.004) were associated with MVI. A multiple logistic regression model was established, which showed tumor size, margin, and peritumor enhancement were combined predictors for the presence of MVI (α=0.1). R2 of this prediction model was 0.353, and the sensitivity and specificity were 52.9% and 93.0%, respectively. Conclusion. Large tumor size, irregular tumor margin, and peritumor enhancement in preoperative Gd-EOB-DTPA-dynamic enhanced MRI can predict the presence of MVI in HCC.

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Blood indices and circulating tumor cells for predicting metastasis of hepatocellular carcinoma after liver transplantation

10.21203/rs.3.rs-57237/v1 ◽

2020 ◽

Author(s):

Li Zhuang ◽

Xiang-yan Liu ◽

Heng-kai Zhu ◽

Zhuo-yi Wang ◽

Wu Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Transplantation ◽

Logistic Regression ◽

Circulating Tumor Cell ◽

Microvascular Invasion ◽

Term Survival ◽

Hematological Indices ◽

Blood Indices ◽

The Third

Abstract Background: Liver transplantation (LT) can benefit the long-term survival of hepatocellular carcinoma (HCC) patients. We hypothesized that circulating tumor cell (CTC) levels and subtypes are intimately associated with metastasis status of HCC patients, and this study was designed to test that compositive hematological indices including CTC can provide sensitive and accurate prediction of post-LT metastasis. Methods: Between 2017 and 2018, HCC patients receiving LT were included for analysis. The 24-month follow-up was mainly conducted by outpatient and telephone. Blood samples were collected, and hematological indices were examined. The outcomes such as PFS, recurrence, metastasis, location of recurrence/metastasis, and number of metastases were recorded. Results: The follow-up analysis showed that microvascular invasion (MVI) classification at the baseline is associated with metastasis. Next, AFP level was another useful indicator of postoperative metastasis, especially at the third or fourth month; the PIVKA-II level 3 months after LT was significantly higher for those who had later metastasis. The mesenchymal CTC level at the 45th day was increased for in the metastasis group. Using two-ends Logistic regression, the calculated value MP (metastasis predictor, by above factors). Had an AUC of 0.858 in the ROC curve, with a cutoff value of 0.328. In conclusion, microvascular invasion, AFP level at the third or fourth month, PIVKA-II level at the third month, and mesenchymal CTC level at day 45 were associated with post-LT metastasis. Conclusion: Using Logistic regression based on above variables, the 2-year metastasis can be predicted with satisfactory sensitivity and accuracy.

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Incomplete tumor capsule on preoperative imaging reveals microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis

Abdominal Radiology ◽

10.1007/s00261-019-02126-9 ◽

2019 ◽

Vol 44 (9) ◽

pp. 3049-3057 ◽

Cited By ~ 2

Author(s):

Fei Zhu ◽

Fan Yang ◽

Jing Li ◽

Weixia Chen ◽

Weilin Yang

Keyword(s):

Hepatocellular Carcinoma ◽

Systematic Review ◽

Meta Analysis ◽

Microvascular Invasion ◽

Preoperative Imaging ◽

Tumor Capsule

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A non-smooth tumor margin on preoperative imaging assesses microvascular invasion of hepatocellular carcinoma: A systematic review and meta-analysis

Scientific Reports ◽

10.1038/s41598-017-15491-6 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 8

Author(s):

HangTong Hu ◽

Qiao Zheng ◽

Yang Huang ◽

Xiao Wen Huang ◽

Zhi Cheng Lai ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Systematic Review ◽

Meta Analysis ◽

Microvascular Invasion ◽

Preoperative Imaging ◽

Tumor Margin

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Radiomics Analysis of MR Imaging with Gd-EOB-DTPA for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Investigation and Comparison of Different Hepatobiliary Phase Delay Times

BioMed Research International ◽

10.1155/2021/6685723 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Shuai Zhang ◽

Guizhi Xu ◽

Chongfeng Duan ◽

Xiaoming Zhou ◽

Xin Wang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Mr Imaging ◽

Predictive Model ◽

Microvascular Invasion ◽

Support Vector ◽

Hepatobiliary Phase ◽

Lasso Regression ◽

Preoperative Prediction ◽

Significant Difference ◽

Selection Operator

Purpose. To investigate whether the radiomics analysis of MR imaging in the hepatobiliary phase (HBP) can be used to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Method. A total of 130 patients with HCC, including 80 MVI-positive patients and 50 MVI-negative patients, who underwent MR imaging with Gd-EOB-DTPA were enrolled. Least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics parameters derived from MR images obtained in the HBP 5 min, 10 min, and 15 min images. The selected features at each phase were adopted into support vector machine (SVM) classifiers to establish models. Multiple comparisons of the AUCs at each phase were performed by the Delong test. The decision curve analysis (DCA) was used to analyze the classification of MVI-positive and MVI-negative patients. Results. The most predictive features between MVI-positive and MVI-negative patients included 9, 8, and 14 radiomics parameters on HBP 5 min, 10 min, and 15 min images, respectively. A model incorporating the selected features produced an AUC of 0.685, 0.718, and 0.795 on HBP 5 min, 10 min, and 15 min images, respectively. The predictive model for HBP 5 min, 10 min and 15 min showed no significant difference by the Delong test. DCA indicated that the predictive model for HBP 15 min outperformed the models for HBP 5 min and 10 min. Conclusions. Radiomics parameters in the HBP can be used to predict MVI, with the HBP 15 min model having the best differential diagnosis ability.

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