论著摘要 |【AI-CT】通过进化深度放射组学测序仪发现放射影像,用于病理证实的肺癌检测(双语版)

2018-01-12 10:41:56 admin 0
标签:  人工智能 AI 影像组学 肺癌 病理学 放射组学

Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection.

发表日期: 2017.10.06   来源:J Med Imaging (Bellingham). 2017 Oct;4(4):041305.

作者:

Shafiee MJ1, Chung AG1, Khalvati F2, Haider MA2, Wong A2.

作者介绍:

1. University of Waterloo, Vision and Image Processing Research Group, Waterloo, Canada.

2. University of Toronto, Sunnybrook Research Institute, Department of Medical Imaging, Toronto, Canada.

摘要

虽然肺癌是男性和女性第二大诊断的癌症,但足够早期的诊断在患者生存率方面是至关重要的。基于成像技术或放射成像的检测方法已经被开发出来从而帮助诊断人员,但在很大程度上依赖于手工制作的特征,这些特征可能未完全包含癌组织和健康组织之间的差异。最近,引入了发现放射成像的概念,其中从易于获得的成像数据发现自定义抽象特征。我们提出了一种基于进化深度智能的进化深度放射成像序列发现方法。由于患者隐私问题和操作性人工智能的想法,深度进化的放射成像测序发现方法有机地演变出越来越多的有效的深度放射成像测序器,其产生了多代的显着的同时更加紧凑但类似的描述性放射成像序列。因此,该框架提高了运行效率,并使诊断能够在放射科医生的计算机上本地运行,同时保持检测精度。我们通过目前提出的进化深层放射性测序仪发现框架评估了深度进化放射性测序仪(EDRS),同时采用目前最先进的放射影像驱动和发现放射性方法,这些方法使用临床肺CT数据和来自LIDC-IDRI的病理学证实的诊断数据集。最终将这两种方法进行对比。相对于以前的放射性方法,EDRS显示出改善的敏感性(93.42%),特异性(82.39%)和诊断准确度(88.78%)。

Abstact

While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features that may not fully encapsulate the differences between cancerous and healthy tissue. Recently, the concept of discovery radiomics was introduced, where custom abstract features are discovered from readily available imaging data. We propose an evolutionary deep radiomic sequencer discovery approach based on evolutionary deep intelligence. Motivated by patient privacy concerns and the idea of operational artificial intelligence, the evolutionary deep radiomic sequencer discovery approach organically evolves increasingly more efficient deep radiomic sequencers that produce significantly more compact yet similarly descriptive radiomic sequences over multiple generations. As a result, this framework improves operational efficiency and enables diagnosis to be run locally at the radiologist's computer while maintaining detection accuracy. We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically proven diagnostic data from the LIDC-IDRI dataset. The EDRS shows improved sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%) relative to previous radiomics approaches.

关键词:

发现放射组学;深度进化智能;深度进化放射组学测序仪;肺癌;放射组学测序

Keywords:

discovery radiomics; evolutionary deep intelligence; evolved deep radiomic sequencer; lung cancer; radiomic sequencing

阅读原文:PMID: 29021990  PMCID: PMC5629455 [Available on 2018-10-06]  DOI: 10.1117/1.JMI.4.4.041305


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