Predictive value of a constructed artificial neural network model for microvascular invasion in hepatocellular carcinoma:A retrospective study  

作  者:Hai-Yang Nong Yong-Yi Cen Shan-Jin Lu Rui-Sui Huang Qiong Chen Li-Feng Huang Jian-Ning Huang Xue Wei Man-Rong Liu Lin Li Ke Ding 

机构地区:[1]Department of Radiology,The Third Affiliated Hospital of Guangxi Medical University,Nanning 530031,Guangxi Zhuang Autonomous Region,China [2]Department of Radiology,Affiliated Hospital of Youjiang Medical University for Nationalities,Baise 533000,Guangxi Zhuang Autonomous Region,China [3]Guangxi Clinical Medical Research Center for Hepatobiliary Diseases,Affiliated Hospital of Youiiang Medical University for Nationalities,Baise 533000,Guangxi Zhuang Autonomous Region,China [4]Department of Ultrasound,The Third Affiliated Hospital of Guangxi Medical University,Nanning 530031,Guangxi Zhuang Autonomous Region,China [5]Department of Hepatobiliary Surgery,The Third Affiliated Hospital of Guangxi Medical University,Nanning 530031,Guangxi Zhuang Autonomous Region,China

出  处:《World Journal of Gastrointestinal Oncology》2025年第1期88-100,共13页世界胃肠肿瘤学杂志(英文)

基  金:Supported by the National Natural Science Foundation of China,No.81560278;the Health Commission of Guangxi Zhuang Autonomous Region,No.Z20200953,No.G201903023,and No.Z-A20221157;Scientific Research and Technology Development Project of Nanning,No.20213122.

摘  要:BACKGROUND Microvascular invasion(MVI)is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma(HCC)surgery.Currently,there is a paucity of preoperative evaluation approaches for MVI.AIM To investigate the predictive value of texture features and radiological signs based on multiparametric magnetic resonance imaging in the non-invasive preoperative prediction of MVI in HCC.METHODS Clinical data from 97 HCC patients were retrospectively collected from January 2019 to July 2022 at our hospital.Patients were classified into two groups:MVI-positive(n=57)and MVI-negative(n=40),based on postoperative pathological results.The correlation between relevant radiological signs and MVI status was analyzed.MaZda4.6 software and the mutual information method were employed to identify the top 10 dominant texture features,which were combined with radiological signs to construct artificial neural network(ANN)models for MVI prediction.The predictive performance of the ANN models was evaluated using area under the curve,sensitivity,and specificity.ANN models with relatively high predictive performance were screened using the DeLong test,and the regression model of multilayer feedforward ANN with backpropagation and error backpropagation learning method was used to evaluate the models’stability.RESULTS The absence of a pseudocapsule,an incomplete pseudocapsule,and the presence of tumor blood vessels were identified as independent predictors of HCC MVI.The ANN model constructed using the dominant features of the combined group(pseudocapsule status+tumor blood vessels+arterial phase+venous phase)demonstrated the best predictive performance for MVI status and was found to be automated,highly operable,and very stable.CONCLUSION The ANN model constructed using the dominant features of the combined group can be recommended as a noninvasive method for preoperative prediction of HCC MVI status.

关 键 词:Hepatocellular carcinoma Texture analysis Magnetic resonance imaging Microvascular invasion Pseudocapsule Tumor blood vessels 

分 类 号:R735.7[医药卫生—肿瘤]

 

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