The Use of 68Ga-DOTATOC and 18F-FDG PET/CT Imaging in the Evaluation of Newly Dia

项目摘要/摘要 在退伍军人和普通美国人中，肺癌是癌症死亡的主要原因。 人口。2004年，估计有173,700名美国人被诊断出患有肺癌； 这些人，大约164,440人(95%)最终将死于他们的疾病。尽管进行了数十年的研究， 肺癌的预后仍然令人沮丧，所有患者的总五年存活率都很低。 16%的阶段。尽管如此，如果及早发现肺癌是可以治愈的，并有5年的存活期。 对于高达70%的第一阶段，确立早期诊断和明确治疗的重要性。 普通人群罹患肺癌和支气管癌的终生风险 美国人口中，男性和女性的比例分别为1/12和1/16。男性患者的相对风险 使用退伍军人医院的人数大约是普通人群的两倍。虽然相对的是 由于美国的年龄结构，与年龄相关的肺癌发病率正在下降 人口方面，美国新诊断的肺癌总数预计将增加 从2010年的约22.5万人增加到2050年的约41万人。显然，有一个很棒的 社会需要，特别是在退伍军人群体中，及早改善和提高成本效益 肺癌的诊断和对肿瘤生物学的新见解，可用于 新的治疗方法。 目前的肺癌诊断方法包括使用“正电子发射计算机断层扫描”(通常 与集成的CT扫描仪结合成PET/CT)，极大地提高了我们的能力 鉴别肺结节良恶性，提高肺部分期 癌症。然而，目前临床上的PET/CT，根据放射性形式的分布 葡萄糖，受到假阳性(FP)和假阴性(FN)结果的限制。用于诊断 不确定的肺结节，FN结果通常见于直径小于1厘米或 对于侵袭性相对较弱的癌症(例如典型的类癌， 分化腺癌、细支气管肺泡癌(BAC)和一些转移癌)。 这些FN结果延迟了诊断，而通过CT观察结节，为可能的诊断提供了时间 转移的发生和潜在的治疗方法被遗漏。FN结果在肺癌分期中的作用 患者在他/她的阶段治疗不当，对预后产生不利影响 浪费医疗保健费用。FP结果在感染性/炎症性病变中很常见，如 无钙化或钙化程度较低的肉芽肿，导致不必要的手术或活检， 痛苦的、有时甚至是危险的手术也会浪费医疗费用。 我们假设，结合使用标准的系列PET/CT扫描的结果 放射性糖(“18F-FDG”)与名为68Ga标记的实验性PET扫描剂 DOTATOC将减少FP和FN检查的次数，提高对非 对肺结节的侵入性诊断和肺癌的分期，并可能提供新的 对肿瘤生物学的洞察，可能会提出新的治疗方法。我们还将使用最先进的 增强了自动肿瘤体积计算和呼吸“门控”功能，以改善 这些测量的准确性。最后，我们将把这些成像数据与血清相结合 蛋白质组分析和其他患者信息，以开发一个新的多变量模型 肺癌的诊断。

Project Summary/Abstract Lung cancer is the leading cause of cancer death in Veterans and in the general U.S. population. In 2004, an estimated 173,700 Americans were diagnosed with lung cancer; of these, about 164,440 (95%) will ultimately die of their disease. Despite decades of research, the prognosis for lung cancer remains dismal, with an overall five-year survival combining all stages of 16%. Nonetheless, lung cancer can be cured if detected early, with a 5 year survival for Stage 1 of up to 70%, establishing the importance of early diagnosis and definitive treatment. The lifetime risk for developing cancer of the lung and bronchus for the general population in the US is 1 in 12 for men and 1 in 16 for women. Relative risk for male patients using VA hospitals is approximately twice that of the general population. Though the relative age-related incidence of lung cancer is declining, because of the age demographics of the US population, the total number of newly diagnosed lung cancers in the US is expected to increase from about 225,000 in the year 2010 to about 410,000 in the year 2050. Clearly there is a great societal need, especially within the Veteran population, to improve early and cost-effective diagnosis of lung cancer and to gain new insights into tumor biology that can be exploited for novel treatments. Current lung cancer diagnostic methods include the use of "PET" scans (typically combined with an integrated CT scanner into a PET/CT), which has greatly improved our ability to discriminate between benign and malignant lung nodules and to improve the staging of lung cancer. However, current clinical PET/CT, based on the distribution of a radioactive form of glucose, is limited by both false positive (FP) and false negative (FN) results. For diagnosis of indeterminate lung nodules, FN results are common for nodules less than 1 cm in diameter or for cancers that are relatively less aggressive than most (e.g. typical carcinoid tumor, well- differentiated adenocarcinoma, bronchioloalveolar carcinoma (BAC), and some metastases). These FN results delay diagnosis while the nodule is watched via CT, allowing time for possible metastases to occur and a potential cure to be missed. FN results in staging lung cancer result in the patient being treated inappropriately for his/her stage, adversely affecting outcome and wasting healthcare dollars. FP results are common with infectious/inflammatory lesions, such as non-calcified or poorly calcified granulomas, leading to unnecessary surgery or biopsies, painful and sometimes dangerous procedures that also waste healthcare dollars. We hypothesize that combining results from serial PET/CT scans usingthe standard radioactive sugar ("18F-FDG) with an experimental PET scanning agent called 68Ga-labeled DOTATOC will reduce the number of FP and FN examinations, improving accuracy for non- invasive diagnosis of lung nodules and for staging of lung cancer, and possibly to provide new insights into tumor biology that might suggest novel treatments. We will also use state-of-the-art enhancements for automatic tumor volume calculation and respiratory "gating" to improve the accuracy of these measurements. Finally we will combine these imaging data with a serum proteomic analysis and other patient information to develop a new multivariate model for the diagnosis of lung cancer.