Deep Learning-based Risk Prediction Model for Recurrence-free Survival in Patients with Hepatocellular Carcinoma Using Multi-phase CT Image

doi:10.1109/GCCE56475.2022.10014204

科研成果详情

题名	Deep Learning-based Risk Prediction Model for Recurrence-free Survival in Patients with Hepatocellular Carcinoma Using Multi-phase CT Image
作者	Wang, Weibin 1; Wang, Fang 2; Yang, Yunjun3; Li, Yinhao 4; Liu, Jing 5; Han, Xianhua 6; Lin, Lanfen 7; Tong, Ruofeng 7; Hu, Hongjie 2; Chen, Yen-Wei 4
发表日期	2022
会议录名称	Institute of Electrical and Electronics Engineers Inc. 影响因子和分区
语种	英语
原始文献类型	Conference Paper
关键词	C-index deep learning hepatocellular carcinoma recurrence recurrence-free time risk prediction
摘要	Risk prediction for recurrence is a critical task for patients with hepatocellular carcinoma (HCC). Effective prediction can evaluate treatment options and guide personalized medicine. Traditional approaches use clinical data to construct cox-regression models to predict the risk of recurrence. Recently, prognosis analysis based on image information of patient often has better performance compared with traditional approaches. In particular, deep learning has demonstrated its superiority in medical image processing. In this paper, we propose a deep learning-based model to predict the risk of recurrence. We collected 292 patients with HCC from two independent centers, which were divided into a training set, an internal validation set, and an external validation set. Our models were compared with traditional clinical models and better performance was obtained. Our proposed method achieves a C-index performance of 0.627 and 0.630 for the internal validation set and external validation set, respectively.
资助项目	Major Scientific Research Project of Zhejiang Lab[2020ND8AD01];Postdoctor Research from Zhejiang Province[ZJ2021028];Key Research and Development Program of Zhejiang Province[2019C03064,2021FZZX003-02-17,226-2022-00160];National Natural Science Foundation of China[82071988];Natural Science Foundation of Zhejiang Province[LZ22F020012]
出版者	Institute of Electrical and Electronics Engineers Inc.
页码	926-929
DOI	10.1109/GCCE56475.2022.10014204
收录类别	SCOPUS
URL	查看原文
Scopus记录号	2-s2.0-85147247888
通讯作者地址	[Hu, Hongjie]Zhejiang University,Department of Radiology,Sir Run Run Shaw Hospital,Hangzhou,China ; [Chen, Yen-Wei]Ritsumeikan University,College of Information Science and Engineering,Kusatsu,Japan ; [Lin, Lanfen]Zhejiang University,College of Computer Science and Technology,Hangzhou,China
Scopus学科分类	Signal Processing;Information Systems and Management;Electrical and Electronic Engineering;Media Technology;Instrumentation;Social Psychology
会议名称	11th IEEE Global Conference on Consumer Electronics, GCCE 2022
会议地点	jpn,Osaka
会议日期	2022-10-18
引用统计
文献类型	会议论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/184910
专题	第一临床医学院（信息与工程学院）、附属第一医院附属第一医院
通讯作者	Lin, Lanfen; Hu, Hongjie; Chen, Yen-Wei
作者单位	1.Ritsumeikan University,Graduate School of Information Science and Engineering,Kusatsu,Japan; 2.Zhejiang University,Department of Radiology,Sir Run Run Shaw Hospital,Hangzhou,China; 3.Wenzhou Medical University,Department of Radiology,the First Affiliated Hospital,Wenzhou,China; 4.Ritsumeikan University,College of Information Science and Engineering,Kusatsu,Japan; 5.Research Center for Healthcare Data Science,Zhejiang Lab,Hangzhou,China; 6.College of Information Science and Engineering,Yamaguchi University,Yamaguchi,Japan; 7.Zhejiang University,College of Computer Science and Technology,Hangzhou,China
推荐引用方式 GB/T 7714	Wang, Weibin,Wang, Fang,Yang, Yunjun,et al. Deep Learning-based Risk Prediction Model for Recurrence-free Survival in Patients with Hepatocellular Carcinoma Using Multi-phase CT Image[C]:Institute of Electrical and Electronics Engineers Inc.,2022:926-929.