A non-smooth tumor margin on preoperative imaging predicts microvascular invasion of hepatocellular carcinoma

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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Microvascular Invasion in HCC: The Molecular Imaging Perspective

Contrast Media & Molecular Imaging ◽

10.1155/2018/9487938 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 7

Author(s):

Vincenzo Cuccurullo ◽

Giuseppe Danilo Di Stasio ◽

Giuseppe Mazzarella ◽

Giuseppe Lucio Cascini

Keyword(s):

Hepatocellular Carcinoma ◽

Positron Emission Tomography ◽

Liver Transplantation ◽

Microvascular Invasion ◽

Emission Tomography ◽

Milan Criteria ◽

Imaging Biomarker ◽

Preoperative Imaging ◽

Primary Liver Tumor ◽

Positron Emission

Hepatocellular carcinoma represents the most frequent primary liver tumor; curative options are only surgical resection and liver transplantation. From 1996, Milan Criteria are applied in consideration of patients with cirrhosis and hepatocellular for liver transplantation; nonetheless, more recently, Milan Criteria have been criticized because they appear over conservative. Apart from number and size of lesions and biomarker levels, which already have been associated with poorer prognosis, overall survival and recurrence rates after transplantation are affected also by the presence of vascular invasion. Microvascular invasion suggests a poor prognosis but it is often hard to detect before transplant. Diagnostic imaging and tumor markers may play an important role and become the main tools to define microvascular invasion. In particular, a possible role could be found for computed tomography, magnetic resonance imaging, and positron emission tomography. In this paper, we analyze the possible role of positron emission tomography as a preoperative imaging biomarker capable of predicting microvascular invasion in patients with hepatocellular carcinoma and thus selecting optimal candidates for liver transplantation.

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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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Can Current Preoperative Imaging Be Used to Detect Microvascular Invasion of Hepatocellular Carcinoma?

Radiology ◽

10.1148/radiol.2015150998 ◽

2016 ◽

Vol 279 (2) ◽

pp. 432-442 ◽

Cited By ~ 99

Author(s):

Matteo Renzulli ◽

Stefano Brocchi ◽

Alessandro Cucchetti ◽

Federico Mazzotti ◽

Cristina Mosconi ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Microvascular Invasion ◽

Preoperative Imaging

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

Frontiers in Oncology ◽

10.3389/fonc.2021.774800 ◽

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.

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