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.
Project Narrative Lung cancer is the leading cause of cancer death in Veterans and in the general population. Despite decades of research and the spending of billions of tax dollars, the overall five year survival is about 15%. The only proven way to improve survival is early diagnosis and surgical removal, when survival can increase to 70%, but this is difficult since small cancers are hard to diagnose. In this proposed study we hope to use standard PET/CT scanning plus an experimental PET/CT scan using a new PET isotope to help us improve the diagnosis of lung cancer at an earlier stage and to improve our ability to determine how far the cancer has spread. We will also combine these PET/CT scan results with new blood tests for even better accuracy and to see if these results will teach us new insights into lung cancer biology that might suggest new ways to treat lung cancer.