{"title":"基于深度学习的癌症多模态空间转录组学分析","authors":"Pankaj Rajdeo, Bruce Aronow, V B Surya Prasath","doi":"10.1016/bs.acr.2024.08.001","DOIUrl":null,"url":null,"abstract":"<p><p>The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast datasets, significantly enhancing diagnostic and treatment applications. In oncology, convolutional neural networks excel in image classification, segmentation, and tumor volume analysis, essential for identifying tumors and optimizing radiotherapy. The chapter also delves into multimodal data analysis, which integrates genomic, proteomic, imaging, and clinical data to offer a holistic understanding of cancer biology. Leveraging diverse data sources, researchers can uncover intricate details of tumor heterogeneity, microenvironment interactions, and treatment responses. Examples include integrating MRI data with genomic profiles for accurate glioma grading and combining proteomic and clinical data to uncover drug resistance mechanisms. DL's integration with multimodal data enables comprehensive and actionable insights for cancer diagnosis and treatment. The synergy between DL models and multimodal data analysis enhances diagnostic accuracy, personalized treatment planning, and prognostic modeling. Notable applications include ST, which maps gene expression patterns within tissue contexts, providing critical insights into tumor heterogeneity and potential therapeutic targets. In summary, the integration of DL and multimodal ST represents a paradigm shift towards more precise and personalized oncology. This chapter elucidates the methodologies and applications of these advanced technologies, highlighting their transformative potential in cancer research and clinical practice.</p>","PeriodicalId":94294,"journal":{"name":"Advances in cancer research","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11431148/pdf/","citationCount":"0","resultStr":"{\"title\":\"Deep learning-based multimodal spatial transcriptomics analysis for cancer.\",\"authors\":\"Pankaj Rajdeo, Bruce Aronow, V B Surya Prasath\",\"doi\":\"10.1016/bs.acr.2024.08.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast datasets, significantly enhancing diagnostic and treatment applications. In oncology, convolutional neural networks excel in image classification, segmentation, and tumor volume analysis, essential for identifying tumors and optimizing radiotherapy. The chapter also delves into multimodal data analysis, which integrates genomic, proteomic, imaging, and clinical data to offer a holistic understanding of cancer biology. Leveraging diverse data sources, researchers can uncover intricate details of tumor heterogeneity, microenvironment interactions, and treatment responses. Examples include integrating MRI data with genomic profiles for accurate glioma grading and combining proteomic and clinical data to uncover drug resistance mechanisms. 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引用次数: 0
摘要
深度学习(DL)和多模态空间转录组学(ST)的出现彻底改变了癌症研究,为肿瘤生物学提供了前所未有的见解。本书的这一章探讨了深度学习与空间转录组学的整合,以推进癌症诊断、治疗规划和精准医疗。卷积神经网络是人工智能的一个子集,它利用神经网络对庞大数据集中的复杂模式进行建模,大大提高了诊断和治疗应用的效率。在肿瘤学领域,卷积神经网络在图像分类、分割和肿瘤体积分析方面表现出色,对于识别肿瘤和优化放疗至关重要。本章还深入探讨了多模态数据分析,它整合了基因组、蛋白质组、成像和临床数据,提供了对癌症生物学的整体理解。利用不同的数据源,研究人员可以发现肿瘤异质性、微环境相互作用和治疗反应的复杂细节。这方面的例子包括将核磁共振成像数据与基因组图谱相结合,以准确进行胶质瘤分级;将蛋白质组学数据与临床数据相结合,以揭示耐药机制。DL 与多模态数据的整合可为癌症诊断和治疗提供全面、可行的见解。DL 模型与多模态数据分析之间的协同作用提高了诊断准确性、个性化治疗计划和预后建模。值得注意的应用包括 ST,它可以绘制组织背景下的基因表达模式图,为了解肿瘤异质性和潜在治疗靶点提供重要依据。总之,DL 与多模态 ST 的整合代表着向更精确、更个性化肿瘤学方向的范式转变。本章阐明了这些先进技术的方法和应用,强调了它们在癌症研究和临床实践中的变革潜力。
Deep learning-based multimodal spatial transcriptomics analysis for cancer.
The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast datasets, significantly enhancing diagnostic and treatment applications. In oncology, convolutional neural networks excel in image classification, segmentation, and tumor volume analysis, essential for identifying tumors and optimizing radiotherapy. The chapter also delves into multimodal data analysis, which integrates genomic, proteomic, imaging, and clinical data to offer a holistic understanding of cancer biology. Leveraging diverse data sources, researchers can uncover intricate details of tumor heterogeneity, microenvironment interactions, and treatment responses. Examples include integrating MRI data with genomic profiles for accurate glioma grading and combining proteomic and clinical data to uncover drug resistance mechanisms. DL's integration with multimodal data enables comprehensive and actionable insights for cancer diagnosis and treatment. The synergy between DL models and multimodal data analysis enhances diagnostic accuracy, personalized treatment planning, and prognostic modeling. Notable applications include ST, which maps gene expression patterns within tissue contexts, providing critical insights into tumor heterogeneity and potential therapeutic targets. In summary, the integration of DL and multimodal ST represents a paradigm shift towards more precise and personalized oncology. This chapter elucidates the methodologies and applications of these advanced technologies, highlighting their transformative potential in cancer research and clinical practice.