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

肺结节 3D 病理学与放射学特征的精确对应

• 批准号: 7900344
• 负责人: Joel N Kline
• 金额: $ 35.01万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7900344
• 关键词: Adenocarcinoma; Affect; Algorithms; American; Americas; Benign; Cancer Detection; Cancerous; Clinical; Collection; Computer Assisted; Computer-Assisted Diagnosis; Consent; Cytology; Data; Data Collection; Data Set; Development; Diagnosis; Diagnostic; Dimensions; Discrimination; Endoscopy; Evaluation; Excision; Exclusion Criteria; Frozen Sections; Generations; Growth; Heterogeneity; Histocompatibility Testing; Histologic; Histology; Histopathology; Image; Inflammation; Investigation; Knowledge; Lobectomy; Lung Neoplasms; Lung diseases; Lung nodule; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Methodology; Microscope; Modality; Necrosis; Needles; Nodule; Non-Small-Cell Lung Carcinoma; Operative Surgical Procedures; Pathology; Patients; Perfusion; Positron-Emission Tomography; Procedures; Process; Resected; Risk Factors; Sampling; Scanning; Slide; Small Cell Carcinoma; Sputum; Squamous cell carcinoma; Staging; Survival Rate; System; Techniques; Therapeutic; Three-Dimensional Image; Time; Tissue Fixation; Tissues; X-Ray Computed Tomography; base; clinical care; cost; density; detector; imaging modality; improved; inclusion criteria; lung lobe; neoplastic cell; sample fixation; three dimensional structure; tomography; treatment planning; tumor

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.

描述(申请人提供)：每年有超过34万美国人死于肺部疾病，成为美国的第三大杀手。其中一种肺部疾病-肺癌-在过去30年里一直保持着13%-15%的低存活率。这表明肺癌的诊断和治疗技术亟需改进。多排探测器计算机断层扫描(MDCT)正越来越多地用于肺癌的检测、评估和使用3D图像进行生长跟踪。为了推广这一方法并使其更加有效，必须采取两个至关重要的步骤。首先，必须对肺结节内组织类型的三维(3D)结构和内容进行评估。其次，必须利用这一知识来评估结节组织内容物如何与MDCT数据中明显的异质性相对应。这将允许在MDCT水平上对肺癌进行歧视。大多数非小细胞肺癌肿瘤在组织上是异质性的，由恶性肿瘤细胞、坏死性肿瘤细胞、成纤维细胞间质组织和炎症组成。在肺部肿瘤的MDCT表现中，几何和组织密度的不均一性在区分良恶性结节方面没有得到充分的利用，因为目前还没有深入的研究来研究3D中放射异质性与相应的组织学内容之间的相关性。在拟议的研究中，我们将使用专门建造的大型图像显微镜阵列(LIMA)提供肺癌结节及其周围组织的3D结构和病理细节。这些信息将与切除前和切除后的结节的MDCT图像、计算机显微CT(Micro-CT)细节和组织病理学进行配准。在生成这些多模式数据集的过程中，将有可能改进目前的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的诊断的能力将对临床产生重大影响。确定一个结节是恶性的，并确定该结节癌变的比例，将有助于制定更有效的患者治疗计划，更可靠地跟踪生长情况，并减少避免进一步调查程序的费用。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。