Deep learning in cancer pathology: a new generation of clinical biomarkers.
Deep learning in cancer pathology: a new generation of clinical biomarkers.
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癌症病理学中的深度学习：新一代临床生物标志物
DOI：
10.1038/s41416-020-01122-x
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发表时间：
2021-03
影响因子：
8.8
通讯作者：
Kather JN
中科院分区：
文献类型：
--
作者：
Echle A;Rindtorff NT;Brinker TJ;Luedde T;Pearson AT;Kather JN
肿瘤学的临床工作流程依赖于预测和预后的分子生物标志物。然而，这些复杂生物标志物数量的增加往往会增加日常肿瘤学实践决策的成本和时间；此外，生物标志物通常需要常规诊断材料之上的肿瘤组织。然而，常规可用的肿瘤组织包含大量临床相关信息，目前尚未充分利用。深度学习（DL）是一种人工智能（AI）技术的进步，可以直接从常规癌症组织学图像中提取以前隐藏的信息，从而提供潜在的临床有用信息。在这里，我们概述了DL如何直接从组织学图像中提取生物标志物的新兴概念，并总结了癌症组织学基本和高级图像分析的研究。基本的图像分析任务包括组织图像中肿瘤组织的检测、分级和分型；它们旨在使病理工作流程自动化，因此不能立即转化为临床决策。除了这些基本方法之外，深度学习还被用于高级图像分析任务，这些任务有可能直接影响临床决策过程。这些先进的方法包括分子特征推断，生存预测和治疗反应的端到端预测。这种深度学习系统的预测可以简化和丰富临床决策，但需要在临床环境中进行严格的外部验证。
Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.
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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：
3.7
作者：
Araújo T;Aresta G;Castro E;Rouco J;Aguiar P;Eloy C;Polónia A;Campilho A
通讯作者：
Campilho A
影响因子：
168.9
作者：
Hiley, Crispin T.;Le Quesne, John;Swanton, Charles
通讯作者：
Swanton, Charles
DOI：
10.1093/annonc/mdy520
发表时间：
2019-02-01
期刊：
Annals of oncology : official journal of the European Society for Medical Oncology
影响因子：
--
作者：
Haenssle, H A;Fink, C;Uhlmann, L
通讯作者：
Uhlmann, L
影响因子：
2.6
作者：
Fassler, Danielle J.;Abousamra, Shahira;Saltz, Joel
通讯作者：
Saltz, Joel