基于残差多层感知器的非小细胞肺癌基因型复发预测

Q3 Computer Science
Yang Ai, Yinhao Li, Yen-Wei Chen, Panyanat Aonpong, Xianhua Han
{"title":"基于残差多层感知器的非小细胞肺癌基因型复发预测","authors":"Yang Ai, Yinhao Li, Yen-Wei Chen, Panyanat Aonpong, Xianhua Han","doi":"10.18178/joig.11.2.185-194","DOIUrl":null,"url":null,"abstract":"Non-small Cell Lung Cancer (NSCLC) is one of the malignant tumors with the highest morbidity and mortality. The postoperative recurrence rate in patients with NSCLC is high, which directly endangers the lives of patients. In recent years, many studies have used Computed Tomography (CT) images to predict NSCLC recurrence. Although this approach is inexpensive, it has low prediction accuracy. Gene expression data can achieve high accuracy. However, gene acquisition is expensive and invasive, and cannot meet the recurrence prediction requirements of all patients. In this study, a low-cost, high-accuracy residual multilayer perceptrons-based genotype-guided recurrence (ResMLP_GGR) prediction method is proposed that uses a gene estimation model to guide recurrence prediction. First, a gene estimation model is proposed to construct a mapping function of mixed features (handcrafted and deep features) and gene data to estimate the genetic information of tumor heterogeneity. Then, from gene estimation data obtained using a regression model, representations related to recurrence are learned to realize NSCLC recurrence prediction. In the testing phase, NSCLC recurrence prediction can be achieved with only CT images. The experimental results show that the proposed method has few parameters, strong generalization ability, and is suitable for small datasets. Compared with state-of-the-art methods, the proposed method significantly improves recurrence prediction accuracy by 3.39% with only 1% of parameters.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"71 9 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ResMLP_GGR: Residual Multilayer Perceptrons- Based Genotype-Guided Recurrence Prediction of Non-small Cell Lung Cancer\",\"authors\":\"Yang Ai, Yinhao Li, Yen-Wei Chen, Panyanat Aonpong, Xianhua Han\",\"doi\":\"10.18178/joig.11.2.185-194\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Non-small Cell Lung Cancer (NSCLC) is one of the malignant tumors with the highest morbidity and mortality. The postoperative recurrence rate in patients with NSCLC is high, which directly endangers the lives of patients. In recent years, many studies have used Computed Tomography (CT) images to predict NSCLC recurrence. Although this approach is inexpensive, it has low prediction accuracy. Gene expression data can achieve high accuracy. However, gene acquisition is expensive and invasive, and cannot meet the recurrence prediction requirements of all patients. In this study, a low-cost, high-accuracy residual multilayer perceptrons-based genotype-guided recurrence (ResMLP_GGR) prediction method is proposed that uses a gene estimation model to guide recurrence prediction. First, a gene estimation model is proposed to construct a mapping function of mixed features (handcrafted and deep features) and gene data to estimate the genetic information of tumor heterogeneity. Then, from gene estimation data obtained using a regression model, representations related to recurrence are learned to realize NSCLC recurrence prediction. In the testing phase, NSCLC recurrence prediction can be achieved with only CT images. The experimental results show that the proposed method has few parameters, strong generalization ability, and is suitable for small datasets. Compared with state-of-the-art methods, the proposed method significantly improves recurrence prediction accuracy by 3.39% with only 1% of parameters.\",\"PeriodicalId\":36336,\"journal\":{\"name\":\"中国图象图形学报\",\"volume\":\"71 9 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中国图象图形学报\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.18178/joig.11.2.185-194\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国图象图形学报","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.18178/joig.11.2.185-194","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0

摘要

非小细胞肺癌(NSCLC)是发病率和死亡率最高的恶性肿瘤之一。NSCLC患者术后复发率高,直接危及患者生命。近年来,许多研究使用计算机断层扫描(CT)图像预测非小细胞肺癌的复发。这种方法虽然成本低廉,但预测精度较低。基因表达数据可以达到较高的准确性。然而,基因获取是昂贵的和侵入性的,并不能满足所有患者的复发预测要求。本研究提出了一种基于残差多层感知器的低成本、高精度基因型引导复发(ResMLP_GGR)预测方法,该方法利用基因估计模型指导复发预测。首先,提出了一种基因估计模型,构建混合特征(手工特征和深度特征)与基因数据的映射函数来估计肿瘤异质性的遗传信息。然后,利用回归模型获得基因估计数据,学习与复发相关的表征,实现对NSCLC复发的预测。在检测阶段,仅通过CT图像即可预测NSCLC的复发。实验结果表明,该方法参数少,泛化能力强,适用于小数据集。与现有方法相比,该方法仅使用1%的参数,递归预测准确率显著提高3.39%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ResMLP_GGR: Residual Multilayer Perceptrons- Based Genotype-Guided Recurrence Prediction of Non-small Cell Lung Cancer
Non-small Cell Lung Cancer (NSCLC) is one of the malignant tumors with the highest morbidity and mortality. The postoperative recurrence rate in patients with NSCLC is high, which directly endangers the lives of patients. In recent years, many studies have used Computed Tomography (CT) images to predict NSCLC recurrence. Although this approach is inexpensive, it has low prediction accuracy. Gene expression data can achieve high accuracy. However, gene acquisition is expensive and invasive, and cannot meet the recurrence prediction requirements of all patients. In this study, a low-cost, high-accuracy residual multilayer perceptrons-based genotype-guided recurrence (ResMLP_GGR) prediction method is proposed that uses a gene estimation model to guide recurrence prediction. First, a gene estimation model is proposed to construct a mapping function of mixed features (handcrafted and deep features) and gene data to estimate the genetic information of tumor heterogeneity. Then, from gene estimation data obtained using a regression model, representations related to recurrence are learned to realize NSCLC recurrence prediction. In the testing phase, NSCLC recurrence prediction can be achieved with only CT images. The experimental results show that the proposed method has few parameters, strong generalization ability, and is suitable for small datasets. Compared with state-of-the-art methods, the proposed method significantly improves recurrence prediction accuracy by 3.39% with only 1% of parameters.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
中国图象图形学报
中国图象图形学报 Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.20
自引率
0.00%
发文量
6776
期刊介绍: Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics. Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art. Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信