Lung cancer is the leading cause of cancer death in the United States, accounting for over 160,000 deaths each year. The best predictor of survival following surgical resection is the presence or absence of metastatic disease in regional lymph nodes. However, even early stage patients believed to be negative for nodal disease (N0), exhibit a 40% overall recurrence rate. Retrospective analysis has demonstrated that a significant number of early stage lung cancer patients harbor """"""""occult"""""""" metastases when histologically scrutinized as is done for sentinel lymph nodes (SLNs) in breast cancer and melanoma, and these patients demonstrate poorer survival and an increased risk of recurrence. The identification and excision of SLNs for lung cancer would permit the histologic scrutiny necessary to detect micrometastatic disease and allow accurate correlation of micrometastatic disease with the risk of lung cancer recurrence. Unfortunately, previous SLN mapping techniques have been severely limited in lung cancer due to formidable technical difficulties, making SLN mapping essentially unavailable for patients with lung cancer. As such, it remains unknown if SLN analysis can improve clinical outcomes. We have developed an optical imaging platform that utilizes invisible near-infrared (NIR) fluorescent light combined with color video, to provide the surgeon with real-time, intraoperative image guidance that permits accurate SLN mapping and resection to be performed. Extensive optimization and successful pre-clinical validation in large animals has lead to the need for clinical feasibility studies, especially with regard to safety and efficacy of the device and contrast agent combination. IRB approval has been obtained to conduct a clinical trial with the FDA- approved NIR fluorophore indocyanine green (ICG) and we describe a systematic plan to assess safety and efficacy of the device and fluorophore during human lung cancer surgery. The overall goal of this study is to maximize the detection of tumor-specific SLN via image-guidance during operative resection of early lung cancer, with the future goal of improving outcomes for the estimated 15,000 early lung cancer patients with metastatic nodal disease missed each year by current methods.
Specific Aim I is focused on optimizing ICG injection technique, fluorophore concentration and excitation light fluence rate during SLN mapping.
Specific Aim 2 will determine whether NIR fluorescence imaging can improve identification and analysis of SLN over current lymphadenectomy.
Specific Aim 3 will determine if NIR image-guided SLN excision enhances detection of metastatic disease and whether the presence of micrometastatic disease is a predictor of disease recurrence. Completion of these specific aims will result in translation of a novel optical imaging technology from pre-clinical to clinical use, will ensure that future clinical studies utilizing NIR fluorescence in lung cancer are conducted safely using optimized imaging parameters, and begin to assess the therapeutic benefit of SLN analysis in patients with early stage lung cancer. ? ? The clinical evaluation of the feasibility, safety and efficacy of novel near-infrared (NIR) guidance for sentinel lymph node (SLN) dissection for early lung cancer in an intraoperative setting has the potential to change the current standard-of-care for patients with surgically resectable lung cancer. NIR fluorescent imaging represents a significant advance in SLN identification for lung cancer patients and promises to substantially improve the detection of the occult metastatic disease currently missed in local lymph nodes of an estimated 15,000 patients with potentially curable lung cancer each year in the US. Given that previously undetected micrometastatic disease results in a significant decrease in survival and a nearly 3 fold increase in recurrence, accurate and safe identification of a subset of early lung cancer patients that could potentially benefit from adjuvant therapy, is of vital importance in the ongoing battle against lung cancer. ? ? ?
