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Early diagnosis and treatment of lung cancer are of significant importance. In this paper, a system-theoretic method is introduced to analyze the diagnosis-to-treatment process for lung cancer patients who receive surgical resections. The complex care delivery process is decomposed into a collection of serial processes, each consisting of combinations of various tests and procedures. Closed formulas are derived to estimate the mean and coefficient of variation of waiting time during the diagnosis-to-surgery process. Simple indicators based on the data collected on the clinic/hospital floor are derived to identify the bottlenecks, i.e., the waiting times that impede the whole delivery process in the strongest manner. In addition, by approximating waiting times using Gamma distributions, an algorithm is introduced to evaluate the waiting-time performance, i.e., the probability to finish the diagnosis-to-surgery process within a desired or given time interval. Finally, a case study at Baptist Memorial Health System is introduced to illustrate the applicability of the method and provide recommendations for improvement. Note to Practitioners —Lung cancer is the primary cause of cancer deaths in the U.S. It has a very low five-year survival rate, and only a small percentage of lung cancer cases are diagnosed at an early stage. Particularly, the diagnosis-to-treatment process is a long, complex procedure in which patients experience substantial delays. Thus, reducing diagnosis-to-treatment time is critical, as prolonged waiting times may lead to advanced cancer stage and/or decreased survival rates. In this paper, to analyze the diagnosis-to-treatment process for lung cancer patients who receive surgical resections, a novel analytical method is introduced. First, five critical steps in diagnosis-to-surgery process are considered: 1) chest X-ray and/or CT scan; 2) diagnostic biopsy; 3) noninvasive staging; 4) invasive staging; and 5) surgery. Then, we decompose the complex care delivery process into multiple serial processes, where each process represents a unique sequence of diagnosis steps that patients may go through. To evaluate the system performance, formulas to calculate the mean and variability of waiting time during the diagnosis-to-surgery process are derived. An approximation algorithm is developed to evaluate the probability to finish the diagnosis-to-surgery process within a desired or given time interval, referred to as waiting-time performance. In addition, to identify the bottleneck waiting time whose improvement will lead to the largest reduction in overall waiting time, we present simple indicators based on the data collected on the clinic/hospital floor. Finally, we introduce a case study at Baptist Memorial Hospital. It is shown that the steps between chest X-ray and/or CT scan and diagnostic biopsy and the steps between noninvasive staging and surgery are the most critical ones. Such a method provides a quantitative tool for the analysis and improvement of lung cancer diagnosis-to-surgery process.