scholarly journals Nomogram For Preoperative Prediction Of Microvascular Invasion Risk In Hepatocellular Carcinoma

2019 ◽  
Vol Volume 11 ◽  
pp. 9037-9045 ◽  
Author(s):  
Guangtong Deng ◽  
Lei Yao ◽  
Furong Zeng ◽  
Liang Xiao ◽  
Zhiming Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Microvascular Invasion ◽  
Invasion Risk ◽  
Preoperative Prediction

A Novel Hematological Nomogram for Preoperative Prediction of Microvascular Invasion Risk in Patients with Hepatocellular Carcinoma

2019 ◽  
Author(s):  
Minjie Mao ◽  
Yukun Song ◽  
Shuqi Chen ◽  
Wenjuan Shi ◽  
Yijun Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Microvascular Invasion ◽  
Invasion Risk ◽  
Preoperative Prediction

Nomograms for Preoperative Prediction of Microvascular Invasion and Lymph Node Metastasis Risk in Hepatocellular Carcinoma

2019 ◽  
Author(s):  
Yongcong Yan ◽  
Qianlei Zhou ◽  
Haohan Liu ◽  
Kai Mao ◽  
Chuanchao He ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Microvascular Invasion ◽  
Node Metastasis ◽  
Preoperative Prediction

Preoperative prediction of the microvascular invasion of hepatocellular carcinoma with diffusion-weighted imaging

2012 ◽  
Vol 18 (10) ◽  
pp. 1171-1178 ◽  
Author(s):  
Young Joo Suh ◽  
Myeong-Jin Kim ◽  
Jin-Young Choi ◽  
Mi-Suk Park ◽  
Ki Whang Kim
Keyword(s):  
Hepatocellular Carcinoma ◽  
Diffusion Weighted Imaging ◽  
Microvascular Invasion ◽  
Preoperative Prediction ◽  
Diffusion Weighted

scholarly journals Preoperative prediction of microvascular invasion in hepatocellular carcinoma using quantitative image analysis

HPB ◽  
2017 ◽  
Vol 19 ◽  
pp. S48 ◽  
Author(s):  
J. Zheng ◽  
J. Chakraborty ◽  
P. Allen ◽  
V. Balachandran ◽  
W. Chapman ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Image Analysis ◽  
Quantitative Image Analysis ◽  
Microvascular Invasion ◽  
Preoperative Prediction ◽  
Quantitative Image

scholarly journals Development and validation of a nomogram combining hematological and imaging features for preoperative prediction of microvascular invasion in hepatocellular carcinoma patients

2021 ◽  
Vol 9 (5) ◽  
pp. 402-402
Author(s):  
Qiang Zhou ◽  
Chenhao Zhou ◽  
Yirui Yin ◽  
Wanyong Chen ◽  
Chunxiao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Microvascular Invasion ◽  
Imaging Features ◽  
Preoperative Prediction ◽  
Development And Validation

scholarly journals Nomogram for Preoperative Estimation of Microvascular Invasion Risk in Hepatitis B Virus–Related Hepatocellular Carcinoma Within the Milan Criteria

JAMA Surgery ◽  
2016 ◽  
Vol 151 (4) ◽  
pp. 356 ◽  
Author(s):  
Zhengqing Lei ◽  
Jun Li ◽  
Dong Wu ◽  
Yong Xia ◽  
Qing Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Microvascular Invasion ◽  
Milan Criteria ◽  
Invasion Risk ◽  
B Virus

Deep-Learning Based Analysis of Preoperative MRI Predicts Microvascular Invasion and Outcome in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Bao-Ye sun ◽  
Pei-Yi Gu ◽  
Ruo-Yu Guan ◽  
Cheng Zhou ◽  
Jian-Wei Lu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Predictive Model ◽  
Clinical Outcomes ◽  
Area Under The Curve ◽  
Microvascular Invasion ◽  
Dce Mri ◽  
Preoperative Prediction ◽  
Model Combining ◽  
Contrast Enhanced

Abstract Background & Aims: Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC. Methods We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression. Results Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and AFP were independently associated with MVI: DL-MVI (odds ratio [OR]=35.738; 95% confidence interval [CI]: 14.027-91.056; p<0.001), AFP (OR=4.634, 95% CI: 2.576-8.336; p<0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824. Conclusions Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC.

Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma based on kupffer phase radiomic features of sonazoid contrast-enhanced ultrasound (SCEUS): A prospective study

2022 ◽  
pp. 1-11
Author(s):  
Yi Dong ◽  
Dan Zuo ◽  
Yi-Jie Qiu ◽  
Jia-Ying Cao ◽  
Han-Zhang Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Microvascular Invasion ◽  
Ultrasound Images ◽  
Contrast Enhanced Ultrasound ◽  
Model Based ◽  
Preoperative Prediction ◽  
Contrast Enhanced ◽  
Receiver Operating

OBJECTIVES: To establish and evaluate a machine learning radiomics model based on grayscale and Sonazoid contrast enhanced ultrasound images for the preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. METHODS: 100 cases of histopathological confirmed HCC lesions were prospectively included. Regions of interest were segmented on both grayscale and Kupffer phase of Sonazoid contrast enhanced (CEUS) images. Radiomic features were extracted from tumor region and region containing 5 mm of peritumoral liver tissues. Maximum relevance minimum redundancy (MRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) were used for feature selection and Support Vector Machine (SVM) classifier was trained for radiomic signature calculation. Radiomic signatures were incorporated with clinical variables using univariate-multivariate logistic regression for the final prediction of MVI. Receiver operating characteristic curves, calibration curves and decision curve analysis were used to evaluate model’s predictive performance of MVI. RESULTS: Age were the only clinical variable significantly associated with MVI. Radiomic signature derived from Kupffer phase images of peritumoral liver tissues (kupfferPT) displayed a significantly better performance with an area under the receiver operating characteristic curve (AUROC) of 0.800 (95% confidence interval: 0.667, 0.834), the final prediction model using Age and kupfferPT achieved an AUROC of 0.804 (95% CI: 0.723, 0.878), accuracy of 75.0%, sensitivity of 87.5% and specificity of 69.1%. CONCLUSIONS: Radiomic model based on Kupffer phase ultrasound images of tissue adjacent to HCC lesions showed an observable better predictive value compared to grayscale images and has potential value to facilitate preoperative identification of HCC patients at higher risk of MVI.

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