Predictive Factors of Microvascular Invasion in Patients with Hepatocellular Carcinoma Larger Than 5 cm

2008 ◽  
Vol 32 (10) ◽  
pp. 2218-2222 ◽  
Author(s):  
Yasuhiko Nagano ◽  
Hiroshi Shimada ◽  
Kazuhisa Takeda ◽  
Michio Ueda ◽  
Kenichi Matsuo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Predictive Factors ◽  
Microvascular Invasion

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

2022 ◽  
Vol 11 ◽  
Author(s):  
Shengsen Chen ◽  
Chao Wang ◽  
Yuwei Gu ◽  
Rongwei Ruan ◽  
Jiangping Yu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Predictive Factors ◽  
Calibration Curve ◽  
Clinical Utility ◽  
Microvascular Invasion ◽  
Lasso Regression ◽  
Tumor Margin ◽  
Tumor Capsule ◽  
Tumor Number

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.

Potential predictive factors for microvascular invasion in hepatocellular carcinoma classified within the Milan criteria

2017 ◽  
Vol 23 (1) ◽  
pp. 98-103 ◽  
Author(s):  
Satoru Imura ◽  
Hiroki Teraoku ◽  
Masato Yoshikawa ◽  
Daichi Ishikawa ◽  
Shinichiro Yamada ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Predictive Factors ◽  
Microvascular Invasion ◽  
Milan Criteria

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

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.

Predictors of microvascular invasion in patients with hepatocellular carcinoma who are candidates for orthotopic liver transplanation

2001 ◽  
Vol 120 (5) ◽  
pp. A90-A90
Author(s):  
N ESNAOLA ◽  
G LAUWERS ◽  
N MIRZA ◽  
D NAGORNEY ◽  
D DOHERTY ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Microvascular Invasion

scholarly journals Survival analysis of sorafenib in hepatocellular carcinoma patients with microvascular invasion after hepatectomy

2018 ◽  
Vol 29 ◽  
pp. viii679
Author(s):  
Z. Zhang ◽  
Y. Huang ◽  
Y. Zhou ◽  
J. Yang ◽  
K. Hu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Microvascular Invasion

scholarly journals MRI Features for Predicting Microvascular Invasion of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Liver Cancer ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 94-106
Author(s):  
Seung Baek Hong ◽  
Sang Hyun Choi ◽  
So Yeon Kim ◽  
Ju Hyun Shim ◽  
Seung Soo Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Size ◽  
Meta Analysis ◽  
Diagnostic Odds Ratio ◽  
Microvascular Invasion ◽  
Hepatobiliary Phase ◽  
Phase Imaging ◽  
Arterial Enhancement ◽  
Mri Features ◽  
Peritumoral Enhancement

<b><i>Purpose:</i></b> Microvascular invasion (MVI) is an important prognostic factor in patients with hepatocellular carcinoma (HCC). However, the reported results of magnetic resonance imaging (MRI) features for predicting MVI of HCC are variable and conflicting. Therefore, this meta-analysis aimed to identify the significant MRI features for MVI of HCC and to determine their diagnostic value. <b><i>Methods:</i></b> Original studies reporting the diagnostic performance of MRI for predicting MVI of HCC were identified in MEDLINE and EMBASE up until January 15, 2020. Study quality was assessed using QUADAS-2. A bivariate random-effects model was used to calculate the meta-analytic pooled diagnostic odds ratio (DOR) and 95% confidence interval (CI) for each MRI feature for diagnosing MVI in HCC. The meta-analytic pooled sensitivity and specificity were calculated for the significant MRI features. <b><i>Results:</i></b> Among 235 screened articles, we found 36 studies including 4,274 HCCs. Of the 15 available MRI features, 7 were significantly associated with MVI: larger tumor size (&#x3e;5 cm) (DOR = 5.2, 95% CI [3.0–9.0]), rim arterial enhancement (4.2, 95% CI [1.7–10.6]), arterial peritumoral enhancement (4.4, 95% CI [2.8–6.9]), peritumoral hypointensity on hepatobiliary phase imaging (HBP) (8.2, 95% CI [4.4–15.2]), nonsmooth tumor margin (3.2, 95% CI [2.2–4.4]), multifocality (7.1, 95% CI [2.6–19.5]), and hypointensity on T1-weighted imaging (T1WI) (4.9, 95% CI [2.5–9.6]). Both peritumoral hypointensity on HBP and multifocality showed very high meta-analytic pooled specificities for diagnosing MVI (91.1% [85.4–94.8%] and 93.3% [74.5–98.5%], respectively). <b><i>Conclusions:</i></b> Seven MRI features including larger tumor size, rim arterial enhancement, arterial peritumoral enhancement, peritumoral hypointensity on HBP, nonsmooth margin, multifocality, and hypointensity on T1WI were significant predictors for MVI of HCC. These MRI features predictive of MVI can be useful in the management of HCC.

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