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首页> 外文学位 >Lung cancer: An evaluation of volumetric histopathological architecture with correlation to computed tomography.
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Lung cancer: An evaluation of volumetric histopathological architecture with correlation to computed tomography.

机译：肺癌：与计算机断层扫描相关的容积组织病理学结构评估。

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摘要

Over 190,000 Americans die every year from lung cancer, making it the number one cause of death from cancer in the United States of America. Lung cancer has maintained the same low five year survival rate, of 13-15%, over the last thirty years. There is therefore desperate need for improvement in diagnostic and therapeutic techniques for lung cancer. Multidetector computed tomography (MDCT) is being increasingly used for lung cancer detection and characterization. While national lung cancer screening trials have shown MDCT to be effective in detecting even very small lung nodules, the characterization achievable through this modality is poor. The majority of non-small cell lung cancer nodules are histologically heterogeneous and consist of malignant tumor cells, necrosis, stromal tissue, and inflammation; however, the extent of this heterogeneity is unknown. Geometric and tissue density heterogeneity are underutilized in MDCT representations of lung tumors for distinguishing between malignant and benign nodules because there has been no thorough investigation into the correlation between radiographic heterogeneity and corresponding histological content in 3D. To understand and to make more effective this lung cancer characterization by MDCT, two vital steps must be taken. Firstly, an understanding of the 3D structure and content of tissue types that constitute a lung nodule must be established. Secondly, this knowledge must then be used to assess how nodule tissue content corresponds to the heterogeneity apparent in MDCT data, impacting diagnosis, planning biopsy procedures and nodule change analysis.;In this study we have developed a process model for establishing a direct correlation between histopathology and non-destructive radiological imaging. We provide the 3D structural and pathological detail of lung cancer nodules and surrounding tissues using a purpose built Large Image Microscope Array (LIMA). This information served as the basis for registration of MDCT images of the human nodule before and after resection, computed micro-tomography (micro-CT) detail and histopathology.
机译：每年有190,000美国人死于肺癌，成为美国死于癌症的第一大死因。在过去的30年中，肺癌的五年生存率一直保持在13-15％的低水平。因此，迫切需要改进肺癌的诊断和治疗技术。多探测器计算机断层扫描（MDCT）越来越多地用于肺癌的检测和表征。尽管国家肺癌筛查试验表明MDCT可以有效检测甚至很小的肺结节，但通过这种方式可获得的表征很差。大多数非小细胞肺癌结节在组织学上是异质的，由恶性肿瘤细胞，坏死，间质组织和炎症组成。但是，这种异质性的程度尚不清楚。肺肿瘤的MDCT表示法未充分利用几何和组织密度异质性来区分恶性结节和良性结节，因为尚未对3D影像学异质性和相应的组织学内容之间的相关性进行深入研究。要了解MDCT并使其更有效地表征肺癌，必须采取两个关键步骤。首先，必须建立对构成肺结节的3D结构和组织类型内容的理解。其次，然后必须使用此知识来评估结节组织含量如何对应于MDCT数据中明显的异质性，影响诊断，规划活检程序和结节变化分析。；在这项研究中，我们开发了一种过程模型，用于建立两者之间的直接相关性。组织病理学和无损放射影像学检查。我们使用专门构建的大图像显微镜阵列（LIMA）提供了肺癌结节和周围组织的3D结构和病理学细节。该信息为切除之前，之后的人结节的MDCT图像，计算机断层扫描（micro-CT）细节和组织病理学的配准奠定了基础。

著录项

  • 作者

    de Ryk, Jessica Corinne.;

  • 作者单位

    The University of Iowa.;

  • 授予单位 The University of Iowa.;
  • 学科 Engineering Biomedical.;Health Sciences Oncology.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 156 p.
  • 总页数 156
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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