Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.
Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.
复制标题
癌症的定量成像在后基因组时代：无线电（Geno）麦克风，深度学习和栖息地。
DOI：
10.1002/cncr.31630
复制
发表时间：
2018-12-15
期刊：
影响因子：
6.2
通讯作者：
Gillies RJ
中科院分区：
文献类型：
--
作者：
Napel S;Mu W;Jardim-Perassi BV;Aerts HJWL;Gillies RJ
尽管癌症通常被称为“基因疾病”，但无可争议的是，即使在同一肿瘤内，单个癌细胞的（表观）遗传特性也存在很大差异。因此，在针对敏感克隆的治疗选择之后，先前存在的抗性克隆将会出现并增殖。在此，作者提出定量图像分析（称为“放射组学”）可用于量化和表征这种异质性。事实上，每位癌症患者都会接受放射成像。放射组学基于这样的信念：这些图像反映了潜在的病理生理学，并且它们可以转换为可挖掘的数据，以改进诊断、预后、预测和治疗监测。在过去的十年中，癌症放射组学已经从几个实验室发展成为一个全球性的企业。在这一发展过程中，放射组学建立了一个惯例，其中从分割的感兴趣区域中提取大量带注释的图像特征（1-2000 个特征），并用于构建分类器模型，以将个体患者分为适当的类别（例如，惰性疾病与侵袭性疾病）。这种传统放射组学的扩展是“深度学习”的应用，其中卷积神经网络可用于检测信息最丰富的区域和特征，而无需人工干预。放射组学的进一步扩展涉及自动分割肿瘤内的信息亚区域（“栖息地”），这可以与潜在的肿瘤病理生理学联系起来。放射组学企业的目标是为精准肿瘤学实践提供明智的决策支持。事实上，每个癌症患者都会接受放射成像。放射组学领域将这些图像的定量分析与机器学习相结合，以改善诊断、预后、预测和治疗监测。这篇综述描述了放射组学，包括新颖的人工智能方法，并介绍了定义肿瘤内亚区域或“栖息地”的实践。
Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as “radiomics,” can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1‐2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of “deep learning,” wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions (“habitats”) within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology. Virtually every patient with cancer is radiologically imaged. The field of radiomics combines quantitative analysis of these images with machine learning to improve diagnosis, prognosis, prediction, and therapy monitoring. This review describes radiomics including novel artificial intelligence methods, and introduces the practice of defining subregions, or “habitats,” within tumors.
登录
查看更多内容
前列腺多参数磁共振成像 - 基础教程。
影响因子：
2
作者：
Cabarrus MC;Westphalen AC
通讯作者：
Westphalen AC
分子机制和血管生成的临床应用。
影响因子：
64.8
作者：
Carmeliet, Peter;Jain, Rakesh K.
通讯作者：
Jain, Rakesh K.
DOI：
10.1002/hep.27877
发表时间：
2015-09
期刊：
Hepatology (Baltimore, Md.)
影响因子：
--
作者：
Banerjee S;Wang DS;Kim HJ;Sirlin CB;Chan MG;Korn RL;Rutman AM;Siripongsakun S;Lu D;Imanbayev G;Kuo MD
通讯作者：
Kuo MD
影响因子：
3.3
作者：
Carano, RAD;Ross, AL;Van Bruggen, N
通讯作者：
Van Bruggen, N
影响因子：
3.3
作者：
Barck, Kai H.;Willis, Brandon;Carano, Richard A. D.
通讯作者：
Carano, Richard A. D.