Lung cancer is the leading cause of cancer death in the western world. Recent efforts on screening high risk patients result in many lung cancer patients being detected in early stage. However, the current cure rate of early stage non-small cell lung cancer (NSCLC) patients by surgical resection remains poor, as >50% patients develop metastatic recurrence and die of the disease. This is thought to be due to a propensity for NSCLC to develop early micro-metastases. Clinical trials have demonstrated that post-surgical chemotherapy produced a modest overall 5.4% improvement in 5-year survival. This suggests opportunity to develop strategies to improve adjuvant chemotherapy results. Here we propose to develop a novel personalizable, image-enabled nanoplatform, "PorphyHDL", to improve the therapeutic specificity and efficacy for NSCLC. "PorphyHDL" is a <30nm nanoparticle not only closely mimics HDL-like spherical structure and function (advantageous for drug delivery with its unique targeting pathway and favorable pharmacokinetics profile), but also allows direct radiolabeling with 64Cu via its porphyrin-lipid building block to enable faithful tracking of the fate of drug delivery using PET imaging. This is a multidisciplinary proposal that will test novel approach and using patient-derived XG models for increasing the efficacy of chemotherapy against NSCLC, with the ultimately goal of increasing the cure rate for this disease. In this context, imaging can be used to quantify biomarkers (SBRI and FR) that could select patients and simultaneously provide image-based confirmation and quantification of the delivered drug, providing better specificity, reduced toxicity, and improved therapeutic index for the therapeutic strategy.