Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (4): 284-290.doi: 10.11896/jsjkx.250600188

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Tensor-based Multimodal Fusion Technique to Diagnose Microvascular Invasion

WANG Shaodong1, LI Liujun2, LI Rui1, SU Zhongzhen2, LU Yao1   

  1. 1 School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
    2 Department of Ultrasound, the Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong 519000, China
  • Received:2025-06-26 Revised:2025-09-08 Online:2026-04-15 Published:2026-04-08
  • About author:WANG Shaodong,born in 2000,postgraduate.His main research interests include deep learning and medical imaging.
    LU Yao,born in 1979,Ph.D,professor.His main research interests include medical artificial intelligence,computer-aided diagnosis,medical image analysis,machine learning,radiomics,and medical big data analysis.
  • Supported by:
    National Key R & D Program of China(2023YFE0204300),the R&D Project of Pazhou Lab(HuangPu)(2023K0606),National Natural Science Foundation of China(82441027,62371476),Guangzhou Science and Technology Bureau(2023B03J1237),Health Research Major Projects of Hunan Health Commission(W20241010) and Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University(2020B1212060032).

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Abstract: Microvascular invasion(MVI) is a critical prognostic factor for postoperative recurrence and reduced survival in hepatocellular carcinoma(HCC),making its precise preoperative localization essential for treatment planning.To address limitations of existing radiomics approaches—including poor feature generalizability,weak interpretability,and neglect of spatial peritumoral MVI distribution crucial for surgical strategies—this study proposes:1) multimodal fusion-based 3D localization of MVI via spatial alignment of whole-slide pathology images(WSIs) with 3D ultrasound(3D US);2) a feature tensor fusion deep learning model integrating multiscale features,tensor fusion,and orthogonal loss functions to extract semantic features of peritumoral MVI distribution.Model performance is evaluated on the curated dataset using metrics such as the area under the receiver operating cha-racteristic curve(AUC),Accuracy and F1 score,demonstrating its effectiveness.Experimental validation demonstrates exceptional performance(AUC:0.910,Accuracy:0.930,F1-score:0.852),confirming the proposed model’s clinical potential for precise preoperative MVI diagnosis.

Key words: Microvascular invasion, Multimodal, Tensor fusion, Multi-scale fusion, Hepatocellular carcinoma

CLC Number: 

  • TP183
[1]SUNG H,FERLAY J,SIEGEL R L,et al.Global cancer statistics 2020:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J].CA:A Cancer Journal for Clinicians,2021,71(3):209-249.
[2]VILLANUEVA A.Hepatocellular carcinoma.reply[J].TheNew England journal of medicine,2019,381(1):e2.
[3]VITALE A,PECK-RADOSAVLJEVIC M,GIANNINI E G,et al.Personalized treatment of patients with very early hepatocellular carcinoma[J].Journal of Hepatology,2017,66(2):412-423.
[4]ZHOU J,SUN H,WANG Z,et al.Guidelines for the diagnosis and treatment of hepatocellularcarcinoma(2019 edition)[J].Liver Cancer,2020,9(6):682-720.
[5]NJEI B,ROTMAN Y,DITAH I,et al.Emerging trends in hepatocellular carcinoma incidence and mortality[J].Hepatology,2015,61(1):191-199.
[6]MARENGO A,ROSSO C,BUGIANESI E.Liver cancer:con-nections with obesity,fatty liver,and cirrhosis[J].Annual Review of Medicine,2016,67(1):103-117.
[7]LAI C L,RATZIU V,YUEN M F,et al.Viral hepatitis B[J].The Lancet,2003,362(9401):2089-2094.
[8]YANG L,GU D,WEI J,et al.A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma[J].Liver Cancer,2019,8(5):373-386.
[9]CHAN A W H,ZHONG J,BERHANE S,et al.Development ofpre and post-operative models to predict early recurrence of hepatocellular carcinoma after surgical resection[J].Journal of Hepatology,2018,69(6):1284-1293.
[10]CHEN Z H,ZHANG X P,FENG J K,et al.Actual long-term survival in hepatocellular carcinoma patients with microvascular invasion:a multicenter study from China[J].Hepatology International,2021,15:642-650.
[11]DONG Y,WANG Q M,LI Q,et al.Preoperative prediction of microvascular invasion of hepatocellular carcinoma:radiomics algorithm based on ultrasound original radio frequency signals[J].Frontiers in Oncology,2019,9:1203.
[12]XU X,ZHANG H L,LIU Q P,et al.Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma[J].Journal of Hepatology,2019,70(6):1133-1144.
[13]NITTA H,ALLARD M A,SEBAGH M,et al.Prognostic value and prediction of extratumoral microvascular invasion for hepatocellular carcinoma[J].Annals of Surgical Oncology,2019,26:2568-2576.
[14]LI S,ZENG Q,LIANG R,et al.Using systemic inflammatorymarkers to predict microvascular invasion before surgery in patients with hepatocellular carcinoma[J].Frontiers in Surgery,2022,9:833779.
[15]MAO S,YU X,YANG Y,et al.Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma[J].Scientific Reports,2021,11(1):13999.
[16]SHI L,QU Z,YANG Y,et al.Analysis of Related Risk Factors of Microvascular Invasion in Hepatocellular Carcinoma[J].Computational and Mathematical Methods in Medicine,2022,2022(1):8195512.
[17]ZHOU Q,ZHOU C,YIN Y,et al.Development and validation of a nomogram combining hematological and imaging features for preoperative prediction of microvascular invasion in hepatocellular carcinoma patients[J].Annals of Translational Medicine,2021,9(5):402.
[18]COMEN E A,BOWMAN R L,KLEPPE M.Underlying causes and therapeutic targeting of the inflammatory tumor microenvironment[J].Frontiers in Cell and Developmental Biology,2018,6:56.
[19]WANG H,LU Y,LIU R,et al.A non-invasive nomogram for preoperative prediction of microvascular invasion risk in hepatocellular carcinoma[J].Frontiers in Oncology,2021,11:745085.
[20]ZHAN J,WANG J,ZHANG Z,et al.Noninvasive diagnosis of significant liver inflammation in patients with chronic hepatitis B in the indeterminate phase[J].Virulence,2023,14(1):2268497.
[21]CHONG H H,YANG L,SHENG R F,et al.Multi-scale and multi-parametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma≤ 5 cm[J].European Radiology,2021,31:4824-4838.
[22]ABI-JAOUDEH N,KRUECKER J,KADOURY S,et al.Multimodality image fusion-guided procedures:technique,accuracy,and applications[J].Cardiovascular and Interventional Radiology,2012,35:986-998.
[23]CARCINOMA VILLANUEVA A.Hepatocellular[J].The New England Journal of Medicine,2019,380:1450-1462.
[24]ZHANG R,XU L,WEN X,et al.A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma[J].Quantitative Imaging in Medicine and Surgery,2019,9(9):1503.
[25]LIU B,ZENG Q,HUANG J,et al.IVIM using convolutional neural networks predicts microvascular invasion in HCC[J].European Radiology,2022,32(10):7185-7195.
[26]WANG L,WU M,LI R,et al.MVI-Mind:a novel deep-learning strategy using computed tomography(CT)-based radiomics for end-to-end high efficiency prediction of microvascular invasion in hepatocellular carcinoma[J].Cancers,2022,14(12):2956.
[27]PERCIE DU SERT N,HURST V,AHLUWALIA A,et al.The ARRIVE guidelines 2.0:Updated guidelines for reporting animal research[J].Journal of Cerebral Blood Flow & Metabolism,2020,40(9):1769-1777.
[28]BEUTEL J.Handbook of medical imaging[M].Spie Press,2000.
[29]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Proceedings of the 31st International Confe-rence on Neural Information Processing Systems.2017:6000-6010.
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