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

描述(由申请人提供)： 项目摘要/摘要肺癌是导致退伍军人和普通美国人癌症死亡的主要原因。2004年，估计有173,700名美国人被诊断出患有肺癌；其中约164,440人(95%)最终将死于肺癌。尽管进行了几十年的研究，肺癌的预后仍然令人沮丧，所有阶段的总五年生存率为16%。尽管如此，如果及早发现肺癌是可以治愈的，第一期肺癌的5年存活率高达70%，确立了早期诊断和明确治疗的重要性。在美国普通人群中，患肺癌和支气管癌的终生风险男性为1/12，女性为1/16。使用退伍军人医院的男性患者的相对风险大约是普通人群的两倍。尽管与年龄相关的肺癌相对发病率正在下降，但由于美国人口的年龄结构，美国新诊断的肺癌总数预计将从2010年的约22.5万人增加到2050年的约41万人。显然，特别是在退伍军人群体中，有一种巨大的社会需求，即提高肺癌的早期诊断和成本效益，并对可用于新疗法的肿瘤生物学获得新的见解。目前的肺癌诊断方法包括使用“PET”扫描(通常与集成的CT扫描仪结合成PET/CT)，这极大地提高了我们区分良恶性肺结节的能力，并改善了肺癌的分期。然而，目前的临床PET/CT基于葡萄糖放射性形式的分布，受到假阳性(FP)和假阴性(FN)结果的限制。对于不确定的肺结节，FN结果通常见于直径小于1厘米的结节或相对较低侵袭性的癌症(如典型类癌、高分化腺癌、细支气管肺泡癌(BAC)和一些转移瘤)。当通过CT观察结节时，这些FN结果延迟了诊断，从而为可能的转移发生提供了时间，并错过了潜在的治愈。分期肺癌的FN结果会导致患者在他/她的阶段得到不适当的治疗，对预后产生不利影响，并浪费医疗保健资金。FP结果在感染性/炎症性病变中很常见，如非钙化或低钙化的肉芽肿，导致不必要的手术或活组织检查，痛苦的，有时甚至是危险的手术，也浪费了医疗保健费用。我们假设，将使用标准放射性糖(“18F-FDG”)的系列PET/CT扫描结果与名为68Ga标记的DOTATOC的实验性PET扫描剂相结合，将减少FP和FN检查的次数，提高非侵入性诊断肺结节和肺癌分期的准确性，并可能为肿瘤生物学提供新的见解，可能建议新的治疗方法。我们还将使用最先进的增强技术来自动计算肿瘤体积和呼吸“门控”，以提高这些测量的准确性。最后，我们将把这些成像数据与血清蛋白质组分析和其他患者信息结合起来，开发一个新的用于肺癌诊断的多变量模型。

DESCRIPTION (provided by applicant): 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.