A Nomogram Model for Predicting Hepatocellular Carcinoma with Microvascular Invasion Based on CT Imaging Features

2022 ◽  
Vol 12 (01) ◽  
pp. 140-148
Author(s):  
天豪 李
Keyword(s):  
Hepatocellular Carcinoma ◽  
Ct Imaging ◽  
Microvascular Invasion ◽  
Imaging Features

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

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.

Dual energy spectral CT imaging for the evaluation of small hepatocellular carcinoma microvascular invasion

2017 ◽  
Vol 95 ◽  
pp. 222-227 ◽  
Author(s):  
Chuang-bo Yang ◽  
Shuang Zhang ◽  
Yong-jun Jia ◽  
Yong Yu ◽  
Hai-feng Duan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dual Energy ◽  
Ct Imaging ◽  
Microvascular Invasion ◽  
Small Hepatocellular Carcinoma ◽  
Spectral Ct

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

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
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.

scholarly journals Importance of Microvascular Invasion Risk and Tumor Size on Recurrence and Survival of Hepatocellular Carcinoma After Anatomical Resection and Non-anatomical Resection

2021 ◽  
Vol 11 ◽  
Author(s):  
Haoyu Hu ◽  
Shuo Qi ◽  
Silue Zeng ◽  
Peng Zhang ◽  
Linyun He ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Predictive Model ◽  
Tumor Size ◽  
Microvascular Invasion ◽  
Low Risk ◽  
Hepatobiliary Phase ◽  
Imaging Features ◽  
Anatomical Resection ◽  
Anatomic Resection

Purpose: To establish a valid prediction model to prognose the occurrence of microvascular invasion (MVI), and to compare the efficacy of anatomic resection (AR) or non-anatomic resection (NAR) for hepatocellular carcinoma (HCC).Methods: Two hundred twenty-eight patients with HCC who underwent surgical treatment were enrolled. Their hematological indicators, MRI imaging features, and outcome data were acquired.Result: In the multivariable analysis, alpha-fetoprotein >15 ng/mL, neutrophil to lymphocyte ratio >3.8, corona enhancement, and peritumoral hypointensity on hepatobiliary phase were associated with MVI. According on these factors, the AUROC of the predictive model in the primary and validation cohorts was 0.884 (95% CI: 0.829, 0.938) and 0.899 (95% CI: 0.821, 0.967), respectively. Patients with high risk of MVI or those with low risk of MVI but tumor size >5 cm in the AR group were associated with a lower rate of recurrence and death than patients in the NAR group; however, when patients are in the state of low-risk MVI with tumor size >5 cm, there is no difference in the rate of recurrence and death between AR and NAR.Conclusion: Our predictive model for HCC with MVI is convenient and accurate. Patients with high-risk of MVI or low-risk of MVI but tumor size >5 cm executing AR is of great necessity.

scholarly journals Prediction of microvascular invasion of hepatocellular carcinoma by pre-operative CT imaging

2012 ◽  
Vol 85 (1014) ◽  
pp. 778-783 ◽  
Author(s):  
C-T Chou ◽  
R-C Chen ◽  
C-W Lee ◽  
C-J Ko ◽  
H-K Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Ct Imaging ◽  
Microvascular Invasion

scholarly journals Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shu-Cheng Liu ◽  
Jesyin Lai ◽  
Jhao-Yu Huang ◽  
Chia-Fong Cho ◽  
Pei Hua Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Ct Images ◽  
Microvascular Invasion ◽  
University Hospital ◽  
Arterial Phase ◽  
Support Vector ◽  
Imaging Features ◽  
Clinical Factors ◽  
Validation Set

Abstract Background The accuracy of estimating microvascular invasion (MVI) preoperatively in hepatocellular carcinoma (HCC) by clinical observers is low. Most recent studies constructed MVI predictive models utilizing radiological and/or radiomics features extracted from computed tomography (CT) images. These methods, however, rely heavily on human experiences and require manual tumor contouring. We developed a deep learning-based framework for preoperative MVI prediction by using CT images of arterial phase (AP) with simple tumor labeling and without the need of manual feature extraction. The model was further validated on CT images that were originally scanned at multiple different hospitals. Methods CT images of AP were acquired for 309 patients from China Medical University Hospital (CMUH). Images of 164 patients, who took their CT scanning at 54 different hospitals but were referred to CMUH, were also collected. Deep learning (ResNet-18) and machine learning (support vector machine) models were constructed with AP images and/or patients’ clinical factors (CFs), and their performance was compared systematically. All models were independently evaluated on two patient cohorts: validation set (within CMUH) and external set (other hospitals). Subsequently, explainability of the best model was visualized using gradient-weighted class activation map (Grad-CAM). Results The ResNet-18 model built with AP images and patients’ clinical factors was superior than other models achieving a highest AUC of 0.845. When evaluating on the external set, the model produced an AUC of 0.777, approaching its performance on the validation set. Model interpretation with Grad-CAM revealed that MVI relevant imaging features on CT images were captured and learned by the ResNet-18 model. Conclusions This framework provide evidence showing the generalizability and robustness of ResNet-18 in predicting MVI using CT images of AP scanned at multiple different hospitals. Attention heatmaps obtained from model explainability further confirmed that ResNet-18 focused on imaging features on CT overlapping with the conditions used by radiologists to estimate MVI clinically.

Imaging features of microvascular invasion in hepatocellular carcinoma developed after direct-acting antiviral therapy in HCV-related cirrhosis

2017 ◽  
Vol 28 (2) ◽  
pp. 506-513 ◽  
Author(s):  
Matteo Renzulli ◽  
Federica Buonfiglioli ◽  
Fabio Conti ◽  
Stefano Brocchi ◽  
Ilaria Serio ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Antiviral Therapy ◽  
Microvascular Invasion ◽  
Imaging Features ◽  
Direct Acting Antiviral ◽  
Direct Acting

scholarly journals Value of gadoxetic acid-enhanced MRI for microvascular invasion of small hepatocellular carcinoma: a retrospective study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Meng Zhou ◽  
Dan Shan ◽  
Chunhui Zhang ◽  
Jianhua Nie ◽  
Guangyu Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Predictive Value ◽  
Univariate Analysis ◽  
Microvascular Invasion ◽  
Hepatobiliary Phase ◽  
Phase Imaging ◽  
Imaging Features ◽  
Small Hepatocellular Carcinoma ◽  
Halo Sign ◽  
Rim Enhancement

Abstract Background The 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. Methods A 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. Results Univariate 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%. Conclusions The 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.

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
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