[1]邱成羽,李兵,林世杰,等.放射多组学协同学习预测鼻咽癌自适应放疗触发机制[J].智能系统学报,2024,19(1):58-66.[doi:10.11992/tis.202304029]
　QIU Chengyu,LI Bing,LAM Saikit,et al.Radioactive multi-omics collaborative learning for adaptive radiation therapy eligibility prediction in nasopharyngeal carcinoma[J].CAAI Transactions on Intelligent Systems,2024,19(1):58-66.[doi:10.11992/tis.202304029]
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放射多组学协同学习预测鼻咽癌自适应放疗触发机制

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 19 期数: 2024年第1期 页码: 58-66 栏目: 学术论文—机器学习 出版日期: 2024-01-05
Title:
Radioactive multi-omics collaborative learning for adaptive radiation therapy eligibility prediction in nasopharyngeal carcinoma
作者:
邱成羽1,2, 李兵3,4, 林世杰5, 盛嘉宝1, 滕信智1, 张将1, 程煜婷2, 张馨匀2, 周塔1,4, 葛红3, 张远鹏1,2,4, 蔡璟1,4
1. 香港理工大学 健康科技与资讯学系, 香港 999077;
2. 南通大学 医学信息学系, 江苏 南通 226019;
3. 郑州大学附属肿瘤医院, 河南 郑州 450008;
4. 香港理工大学深圳研究院, 广东 深圳 518057;
5. 香港理工大学 生物医学工程学系, 香港 999077
Author(s):
QIU Chengyu1,2, LI Bing3,4, LAM Saikit5, SHENG Jiabao1, TENG Xinzhi1, ZHANG Jiang1, CHENG Yuting2, ZHANG Xingyun2, ZHOU Ta1,4, GE Hong3, ZHANG Yuanpeng1,2,4, CAI Jing1,4
1. Department of Health Science, Technology and Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China;
2. Department of Medical Informatics, Nantong University, Nantong 226019, China;
3. The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 450008, China;
4. The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518057, China;
5. Department of Biomedical Engineering, Hong Kong Polytechnic University, Hong Kong 999077, China
关键词:
数据融合; 机器学习; 特征提取; 特征选择; 预测; 图像分析; 自适应算法; 鼻咽癌; 多组学
Keywords:
data fusion; machine learning; feature extraction; feature selection; forecasting; image analysis; adaptive algorithms; nasopharyngeal carcinoma; multi-omic
分类号:
TP391.71
DOI:
10.11992/tis.202304029
文献标志码:
2023-08-01
摘要:
针对传统的放射多组学(影像组学、剂量组学和轮廓组学)模型往往采用特征拼接的方式，容易忽略不同组学特定统计属性、产生过拟合的问题，提出了以一致性约束和自适应权重为核心构建的多组学协同学习算法(multi-omics collaborative learning, MOCL)。该算法采用一致性约束挖掘不同组学特征之间的互补模式，再通过香农熵自适应学习不同组学特征的权重，最后引入紧致度图来避免过拟合现象。通过将MOCL在311名鼻咽癌患者组成的临床影像数据上得到的实验结果与3种传统的机器学习算法以及2种多视角算法进行比较，结果表明MOCL在多组学协同学习上，具有一定的优势，能为鼻咽癌自适应放疗资格预测提供有价值的决策依据。
Abstract:
Traditional radiation omics models, including radiomics, dosiomics, and contouromics, typically adopt feature splicing, which tends to ignore the specific statistical attributes of different omics and therefore leads to overfitting. A multi-omics collaborative learning (MOCL) algorithm focused on consistency constraints and adaptive weights was proposed in the study to address this problem. The MOCL algorithm employs consistency constraints to explore complementary patterns among heterogeneous omics features and adaptively learns their weights using Shannon entropy while avoiding overfitting through compactness mapping. An experiment was conducted on the clinical imaging data of 311 patients with nasopharyngeal carcinoma using MOCL. The experimental result is compared with three traditional machine learning algorithms and two multiperspective algorithms. The results demonstrate that MOCL has certain advantages in collaborative learning of multi-omics and can provide a valuable prediction basis for adaptive radiotherapy qualification in the case of nasopharyngeal carcinoma.
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备注/Memo

收稿日期:2023-04-13。
基金项目:国家自然科学基金项目（82072019）；深圳市科技创新委员会深圳市基础研究计划（JCYJ20210324130209023）；深圳-香港-澳门科技计划（C类） （SGDX20201103095002019）；江苏省自然科学基金项目（BK20201441）；河南省医学科学技术研究省部共建项目（SBGJ202103038，SBGJ202102056）；河南省重点研发与推广项目（科学技术研究） （222102310015）；河南省自然科学基金（222300420575，232300420231）；河南省科学技术研究项目（222102310322）
作者简介:邱成羽，硕士研究生，主要研究方向为智能医学工程。E-mail：1277294613@qq.com;张远鹏，博士，教授，2019届香江学者，江苏省人工智能协会不确定性人工智能专业委员会委员，IEEE会员，TCYB、TNNLS、TFS、SMCA、TCBB等权威期刊的审稿人和客座编委。主要研究方向为人工智能与模式识别相关(模糊聚类、TSK模糊系统、特征选择等)研究及其在医学上(脑电信号处理、多模态影像组学分析)的应用。主持国家自然科学基金项目2项、江苏省自然科学基金项目1项、江苏省博士后基金项目1项、南通市科技计划项目1项。发表学术论文30篇。E-mail：maxbirdzhang@ntu.edu.cn;蔡璟，教授、临床医学物理住院师，博士。任多家领域内顶尖杂志执行主编/高级副主编/副主编/编委，国际科研项目评审专家。主持参与科研项目50余项。发表学术论文130余篇，发表会议摘要和其他著作200多个。E-mail：jing.cai@polyu.edu.hk
通讯作者:蔡璟. E-mail：jing.cai@polyu.edu.hk

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