[1]尹梓名,董东民,陈涛.基于3D-ResNet深度影像特征的胆囊癌生存预测模型[J].中国医学物理学杂志,2022,39(7):919-924.[doi:DOI:10.3969/j.issn.1005-202X.2022.07.022]
　YIN Ziming,DONG Dongmin,CHEN Tao.Predictive model for survival in gallbladder cancer patients based on 3D-ResNet deep image features[J].Chinese Journal of Medical Physics,2022,39(7):919-924.[doi:DOI:10.3969/j.issn.1005-202X.2022.07.022]
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基于3D-ResNet深度影像特征的胆囊癌生存预测模型()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第7期
页码:
919-924
栏目:
医学人工智能
出版日期:
2022-07-15

文章信息/Info

Title:
Predictive model for survival in gallbladder cancer patients based on 3D-ResNet deep image features
文章编号:
1005-202X（2022）07-0919-06
作者:
尹梓名1; 董东民1; 陈涛2
1.上海理工大学医疗器械与食品学院， 上海 200093； 2.上海交通大学医学院附属仁济医院胆胰外科， 上海 200127
Author(s):
YIN Ziming1;  DONG Dongmin1;  CHEN Tao2
1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Department of Biliary-Pancreatic Surgery, Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai 200127, China
关键词:
深度学习; 三维卷积神经网络; 胆囊癌; 生存模型; 预后
Keywords:
Keywords: deep learning three-dimensional convolutional neural network gallbladder cancer survival model prognosis
分类号:
R318；R735.8
DOI:
DOI:10.3969/j.issn.1005-202X.2022.07.022
文献标志码:
A
摘要:
建立一个精准的个体化胆囊癌患者生存预测模型，分析、寻找新的胆囊癌预后因素，对于患者预后评估、治疗模式选择、手术患者筛选、术后辅助治疗方案确定及医疗资源合理使用均具有重要意义。本文提出一种基于3D-ResNet提取深度影像特征建立胆囊癌患者生存预后模型的方法，通过迁移学习以及训练3D-ResNet自动提取患者CT的深度特征，并利用提取的深度影像特征，通过Cox比例风险回归模型建立胆囊癌患者的生存预测模型。实验结果表明，基于深度影像特征建立的胆囊癌患者预后因子在预测患者生存时的C指数达到0.734，利用深度影像特征预后因子预测患者的1、3、5年存活率AUC分别达到0.833、0.791、0.813。本方法对胆囊癌预后预测有着良好的指示作用。
Abstract:
Abstract: Establishing an accurate individualized predictive model for survival in gallbladder cancer patients and finding new prognostic factors for gallbladder cancer are of great significance for prognosis evaluation, treatment model selection, surgical patient screening, postoperative adjuvant treatment plan determination, and rational utilization of medical resources. A method to build a predictive model for survival in gallbladder cancer patients based on deep image features extracted by 3D-ResNet is proposed in the study. The deep features of the patients CT image are automatically extracted through transfer learning and 3D-ResNet training, and the extracted deep image features are used to establish a predictive model for survival in gallbladder cancer patients through the Cox proportional hazard regression model. The experimental results show that the C index of prognostic factors obtained based on deep image features is 0.734 for predicting survival in gallbladder cancer patients, and that the AUC of 1-year, 3-year and 5-year survival rates predicted by deep image features reaches 0.833, 0.791 and 0.813, respectively. The proposed method has a good indication for predicting the prognosis of gallbladder cancer.

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备注/Memo

备注/Memo:
【收稿日期】2021-11-04 【基金项目】国家自然科学基金（81801797, 82074581）；国家重点研发计划（2020YFC2005801, 2020YFC2005800） 【作者简介】尹梓名，博士，讲师，主要研究方向：医学人工智能、临床决策支持，E-mail: yinziming1@163.com
更新日期/Last Update: 2022-07-15