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

描述（由申请人提供）：每年有超过34万美国人死于肺病，使其成为美国的第三大杀手。这些肺部疾病之一-肺癌-在过去的三十年里一直保持着13- 15%的低生存率。这表明迫切需要改进肺癌的诊断和治疗技术。多排探测器计算机断层扫描（MDCT）正越来越多地用于肺癌检测，评估和生长跟踪使用3D图像。为了推广这一方法并使其更加有效，必须采取两个重要步骤。首先，必须建立肺结节内组织类型的三维（3D）结构和内容的评价。其次，必须使用这些知识来评估结节组织内容如何对应于MDCT数据中明显的异质性。这将允许在MDCT水平上区分肺癌。大多数非小细胞肺癌肿瘤在组织学上是异质的，并且由恶性肿瘤细胞、坏死肿瘤细胞、成纤维细胞间质组织和炎症组成。几何和组织密度异质性在肺部肿瘤的MDCT表现中用于区分恶性和良性结节的利用不足，因为尚未对放射学异质性与3D中相应组织学内容之间的相关性进行彻底研究。在拟议的研究中，我们将提供肺癌结节和周围组织的三维结构和病理细节，使用专门建造的大型图像显微镜阵列（LIMA）。该信息将与切除前后结节的MDCT图像、计算机微断层扫描（micro-CT）细节和组织病理学进行配准。在生成这些多模态数据集时，将有可能改进当前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特异性诊断的能力将具有显著的临床影响。确定结节是恶性的并且还确定结节是癌性的比例将允许更有效的患者治疗计划、生长跟踪的更大可靠性以及避免进一步调查程序的降低的成本。此外，与肺癌相关的其他成像方式，如正电子发射断层扫描和显微内窥镜，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.