质谱成像常用计算方法及其在肿瘤中的应用
Common computational methods of mass spectrometry imaging and their applications in tumors
孙甜甜 1吕嘉丽 1张帅 1张涛 1王成1
作者信息
- 1. 山东大学公共卫生学院,国家健康医疗大数据研究院,山东济南 250012
- 折叠
摘要
目的 介绍质谱成像(MSI)统计分析场景中的机器学习算法,并总结近年来其在肿瘤应用中的研究进展,推动MSI技术在肿瘤研究中的应用.方法 以"质谱成像、图像分割、分类、肿瘤"为中文关键词,以"mass spectrometry ima-ging、1mage segmentation、cancer"为英文关键词,检索中国知网和PubMed数据库2018年1月—2024年4月的相关文献.纳入标准:(1)MSI在肿瘤图像分割中的应用相关文献;(2)MSI在肿瘤亚型分类中的应用相关文献;(3)应用于肿瘤MSI数据的机器学习算法相关文献.排除标准:(1)研究机制模糊文献;(2)会议性、评论等文献;(3)内容相似或重复文献.最终共纳入符合条件文献41篇.结果 MSI是近年来飞速发展的针对各种生物分子进行空间成像的技术,该技术将样品表面分成一系列像素点,对每个像素点进行质谱分析,将得到的质谱信息转化为化合物在样品原位的分布图像.与传统质谱技术相比,MSI纳入了分子空间定位信息,近年来在肿瘤空间异质性研究中应用广泛.目前已有众多图像算法应用于肿瘤MSI研究,尝试通过数据分析及可视化描绘肿瘤微环境的异质性.近年来随着软电离技术和质谱仪器的不断发展,MSI数据结构更加复杂,基于MSI数据进行合理的统计分析是目前的研究重点.对肿瘤MSI数据进行统计分析的步骤主要为降维和聚类,不同机器学习方法在其中的应用实现了肿瘤相关分子机制等研究.结论 现有的计算方法实现了肿瘤图像分割、肿瘤亚型的分类模型构建及生物标志物筛选等基础研究,为肿瘤诊断、治疗和预后等临床研究提供了技术支持.
Abstract
Objective To introduce machine learning algorithms in mass spectrometry imaging(MSI)statistical analysis sce-narios and summarize the recent research progress in its application in tumors,promoting the application of MSI technolo-gy in tumor research.Methods Using"mass spectrometry imaging,image segmentation,classification,cancer"as key-words,relevant literature from the China National Knowledge Infrastructure(CNKI)and PubMed databases was re-trieved.Inclusion criteria include:(1)Literature related to the application of MSI in tumor image segmentation;(2)Lit-erature related to the application of MSI in tumor subtype classification;(3)Literature related to machine learning algo-rithms applied to tumor MSI data.Exclusion criteria include:(1)Literature with unclear research mechanisms;(2)Con-ference-related,review-type documents;(3)Literature that is similar or redundant.A total of 41 eligible documents were included.Results MSI is a rapidly developing technology for the spatial imaging of various biomolecules in recent years.This technique segments the surface of a sample into an array of pixel points.Each pixel is undergone mass spectrometry analysis,and the resulting spectral data are transformed into images showing the spatial distribution of compounds within the sample.Compared with traditional mass spectrometry techniques,MSI contains information about the spatial localiza-tion of molecules and has been widely used in tumor heterogeneity studies recently.A multitude of imaging algorithms have been applied to MSI research in tumors,aiming to delineate the heterogeneity of the tumor microenvironment through data analysis and visualization.With the ongoing development of soft ionization techniques and advancements in mass spectrometry equipment,the complexity of MSI data has increased.Proper statistical analysis of this data,focu-sing on dimension reduction and clustering,has become a pivotal research area.The main steps in the statistical analysis of tumor MSI data are dimensionality reduction and clustering.Various machine learning methods are employed to explore tumor-related molecular mechanisms through these analyses.Conclusions Existing computational methods have success-fully achieved tumor image segmentation,constructed tumor subtype classification models,and screened for biomarkers.These fundamental research achievements provide essential technical support for clinical studies focused on tumor diagno-sis,treatment,and prognosis.
关键词
质谱成像/肿瘤/降维/聚类/机器学习/综述文献Key words
mass spectrometry imaging/cancer/dimensionality reduction/cluster/machine learning/review literature引用本文复制引用
基金项目
国家自然科学基金(82304247)
国家自然科学基金(82222064)
国家自然科学基金(81973147)
出版年
2024
NETL
NSTL
万方数据