Deep learning in cancer diagnosis, prognosis and treatment selection.
Deep learning in cancer diagnosis, prognosis and treatment selection.
复制标题
癌症诊断、预后和治疗选择中的深度学习。
DOI：
10.1186/s13073-021-00968-x
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发表时间：
2021-09-27
期刊：
影响因子：
12.3
通讯作者：
Waddell N
中科院分区：
文献类型：
--
作者：
Tran KA;Kondrashova O;Bradley A;Williams ED;Pearson JV;Waddell N
深度学习是人工智能的一个分支学科，它使用一种被称为人工神经网络的机器学习技术，从大型数据集中提取模式并做出预测。医疗保健领域越来越多地采用深度学习，加上高度特化的癌症数据集的可用性，加速了对深度学习在癌症复杂生物学分析中的应用的研究。虽然早期的结果很有希望，但这是一个快速发展的领域，在癌症生物学和深度学习方面都出现了新的知识。在这篇综述中，我们概述了新兴的深度学习技术以及它们如何应用于肿瘤学。我们专注于组学数据类型的深度学习应用，包括基因组，甲基化和转录组学数据，以及基于组织病理学的基因组推断，并提供了如何集成不同数据类型以开发决策支持工具的观点。我们提供了深度学习如何应用于癌症诊断、预后和治疗管理的具体示例。我们还评估了目前深度学习在精确肿瘤学中应用的局限性和挑战，包括缺乏表型丰富的数据和对更可解释的深度学习模型的需求。最后，我们讨论了如何克服当前的障碍，以实现深度学习在未来的临床应用。
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.
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影响因子：
48
作者：
Amodio, Matthew;van Dijk, David;Srinivasan, Krishnan;Chen, William S.;Mohsen, Hussein;Moon, Kevin R.;Campbell, Allison;Zhao, Yujiao;Wang, Xiaomei;Venkataswamy, Manjunatha;Desai, Anita;Ravi, V.;Kumar, Priti;Montgomery, Ruth;Wolf, Guy;Krishnaswamy, Smita
通讯作者：
Krishnaswamy, Smita
影响因子：
4.6
作者：
Bychkov D;Linder N;Turkki R;Nordling S;Kovanen PE;Verrill C;Walliander M;Lundin M;Haglund C;Lundin J
通讯作者：
Lundin J
影响因子：
16.6
作者：
Cortes-Ciriano I;Lee S;Park WY;Kim TM;Park PJ
通讯作者：
Park PJ
影响因子：
12.3
作者：
Chereda H;Bleckmann A;Menck K;Perera-Bel J;Stegmaier P;Auer F;Kramer F;Leha A;Beißbarth T
通讯作者：
Beißbarth T
影响因子：
78.8
作者：
Bera, Kaustav;Schalper, Kurt A.;Madabhushi, Anant
通讯作者：
Madabhushi, Anant