论著摘要 |【CT】精准影像组学:在影像组学框架中使用特征工程和深度学习方法预测寿命(双语版)

2017-10-16 11:18:21 admin 0

Precision Radiology: Predicting longevity Using Feature Engineering and Deep Learning Methods in a Radiomics Framework.

发表日期: 2017.05.10   来源: Scientific Reports, 2017, 7(1): 1648.

作者

Luke Oakden-Rayner1,2, Gustavo Carneiro3, Taryn Bessen1, Jacinto C. Nascimento4, Andrew P.Bradley5 , Lyle J. Palmer2

作者介绍:

1. Department of Radiology, Royal Adelaide Hospital, North Terrace, Adelaide, SA, 5000, Australia.

2. School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.

3.School of Computer Science, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.

4. Instituto Superior Técnico, Lisbon, Portugal.

5. School of Information Technology and Electrical Engineering, The University of Queensland, Building 78, St Lucia QLD 4067, Queensland, Australia. Correspondence and requests for materials should be addressed to L.O. (email:lukeoakdenrayner@gmail.com)

 

摘要

精准医疗方法依赖于获得由遗传风险与环境暴露的联合作用于个体患者健康状况的准确了解。目前,这种方法受限于缺乏有效和高效的非侵入性医学测试来界定与个体健康相关的表型变异的全部区域。这些了解对于改善早期干预,更好的治疗决策以及改善慢性病流行状态的不断恶化至关重要。我们提出概念验证实验,证明如何使用计算机图像分析技术,获取常规断层CT成像来预测患者寿命,以代表整体个人健康和疾病状态。尽管受限于适度的数据集和使用现成的机器学习方法,但我们的结果在预测寿命上可与以前使用的“手动”临床方法相提并论。这项工作表明,影像组学技术可用于提取与流行病学和临床研究中最广泛使用的结果之一死亡率相关的生物标志物,并且使用卷积神经网络进行深度学习可有效地应用于影像组学研究。计算机图像分析应用于常规收集医学图像为加强精准医疗举措提供了巨大的可能性。

Abstract

Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous ‘manual’ clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research – mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives.

 

阅读原文:DOI:10.1038/s41598-017-01931-w


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