Precise Correspondence of 3D Pathology with Radiological Features in Lung Nodules

描述（由申请人提供）：每年有超过34万美国人死于肺部疾病，使其成为美国的第三大杀手。其中一种肺部疾病——肺癌——在过去30年中一直保持着同样低的生存率，为13-15%。这表明迫切需要改进肺癌的诊断和治疗技术。多排计算机断层扫描（MDCT）越来越多地用于肺癌的检测、评估和使用3D图像的生长跟踪。为了扩大这一方法并使其更有效，必须采取两个重要步骤。首先，必须对肺结节内组织类型的三维结构和内容进行评估。其次，这些知识必须用于评估结节组织含量如何与MDCT数据中明显的异质性相对应。这样就可以在多层螺旋ct水平上对肺癌进行鉴别。大多数非小细胞肺癌肿瘤在组织学上是异质性的，由恶性肿瘤细胞、坏死肿瘤细胞、成纤维基质组织和炎症组成。几何和组织密度不均一性在肺肿瘤的MDCT表现中没有被充分利用来区分恶性和良性结节，因为目前还没有深入研究放射学不均一性与相应的三维组织学内容之间的关系。在这项研究中，我们将使用专门建造的大型图像显微镜阵列（LIMA）提供肺癌结节和周围组织的三维结构和病理细节。这些信息将与结节切除前后的MDCT图像、计算机微断层扫描（micro- CT）细节和组织病理学相结合。在生成这些多模态数据集时，将有可能改进当前的MDCT计算机辅助诊断策略。使用MDCT计算机辅助诊断算法提供更具体诊断的能力将具有重大的临床影响。确定一个结节是恶性的，以及确定癌变结节的比例，将有助于制定更有效的患者治疗计划，提高生长跟踪的可靠性，并降低成本，避免进一步的调查程序。此外，其他与肺癌相关的成像方式，如正电子发射断层扫描和显微内窥镜检查，将发现3D信息对其临床发展至关重要。

DESCRIPTION (provided by applicant): Over 340,000 Americans die every year from lung disease, making it the number three killer in America. One of these lung diseases - lung cancer - has maintained the same low survival rate, of 13-15%, over the last thirty years. This demonstrates the desperate need for improvement in diagnostic and therapeutic techniques for lung cancer. Multi-row detector computed tomography (MDCT) is being increasingly used for lung cancer detection, evaluation and growth tracking using 3D images. To extend and make more effective this methodology, two vital steps must be taken. Firstly, an evaluation of the three-dimensional (3D) structure and content of tissue types within lung nodules must be established. Secondly, this knowledge must then be used to assess how nodule tissue content corresponds to the heterogeneity apparent in MDCT data. This will then allow discrimination of lung cancer at the MDCT level. The majority of non-small cell lung cancer tumors are histologically heterogeneous, and consist of malignant tumor cells, necrotic tumor cells, fibroblastic stromal tissue, and inflammation. Geometric and tissue density heterogeneity are under utilized in MDCT representations of lung tumors for distinguishing between malignant and benign nodules because there has been no thorough investigation into the correlation between radiographical heterogeneity and corresponding histological content in 3D. In the proposed study we will 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 will be registered with MDCT images of the nodule before and after resection, computed micro-tomography (micro- CT) detail and histopathology. In generating these multi-modality datasets, it will be possible to improve the current MDCT computer aided diagnostic strategies. The capacity to provide a more specific diagnosis using MDCT computer aided diagnosis algorithms would have a significant clinical impact. To determine that a nodule is malignant and also to determine the proportion of that nodule that is cancerous would allow for more effective patient treatment plans, greater reliability of growth tracking and reduced cost in avoidance of further investigative procedures. Further, other imaging modalities relevant to lung cancer, such as positron emission tomography and micro-endoscopy, will find the 3D information vital to their clinical development.