University of Miami Miller School of Medicine, Miami, FL, 33136, USA.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
Sci Rep. 2017 Aug 29;7(1):9746. doi: 10.1038/s41598-017-09932-5.
Tumor heterogeneity can be elucidated by mapping subregions of the lesion with differential imaging characteristics, called habitats. Dynamic Contrast Enhanced (DCE-)MRI can depict the tumor microenvironments by identifying areas with variable perfusion and vascular permeability, since individual tumor habitats vary in the rate and magnitude of the contrast uptake and washout. Of particular interest is identifying areas of hypoxia, characterized by inadequate perfusion and hyper-permeable vasculature. An automatic procedure for delineation of tumor habitats from DCE-MRI was developed as a two-part process involving: (1) statistical testing in order to determine the number of the underlying habitats; and (2) an unsupervised pattern recognition technique to recover the temporal contrast patterns and locations of the associated habitats. The technique is examined on simulated data and DCE-MRI, obtained from prostate and brain pre-clinical cancer models, as well as clinical data from sarcoma and prostate cancer patients. The procedure successfully identified habitats previously associated with well-perfused, hypoxic and/or necrotic tumor compartments. Given the association of tumor hypoxia with more aggressive tumor phenotypes, the obtained in vivo information could impact management of cancer patients considerably.
肿瘤异质性可以通过绘制具有不同成像特征的病变亚区(称为生境)来阐明。动态对比增强(DCE-)MRI 可以通过识别具有不同灌注和血管通透性的区域来描绘肿瘤微环境,因为单个肿瘤生境在对比摄取和洗脱的速度和幅度上存在差异。特别感兴趣的是识别缺氧区域,其特征是灌注不足和血管通透性增加。从 DCE-MRI 中自动描绘肿瘤生境的方法是作为一个两部分的过程开发的,包括:(1)统计检验,以确定潜在生境的数量;(2)一种无监督的模式识别技术,以恢复相关生境的时间对比模式和位置。该技术在模拟数据和来自前列腺和脑临床前癌症模型的 DCE-MRI 以及肉瘤和前列腺癌患者的临床数据上进行了检查。该方法成功地识别了先前与灌注良好、缺氧和/或坏死肿瘤区室相关的生境。鉴于肿瘤缺氧与更具侵袭性的肿瘤表型相关,因此获得的体内信息可能会对癌症患者的治疗产生重大影响。