Dual Primary Cancer Patients With Lung Cancer as a Second Primary Malignancy: A Population-Based Study

Background Research on patients with lung cancer as a second primary malignancy (LCSPM) remains limited. This study aims to determine the clinical characteristics, prognosis, and temporal relationship of other cancers to lung cancer in these patients. Methods This study retrospectively analyzed 3465 patients with dual primary cancers from the 5253 patients with LCSPM retrieved from the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015. Results 2285 eligible patients were further analyzed in this study cohort with 59.3% of 1-year OS, 34.7% of 3-year OS, and 25.2% of 5-year OS. The most common first primary cancer (FPC) in dual primary cancer patients with LCSPM was prostate cancer, followed by female breast cancer and urinary bladder cancer. In the entire study population, the median interval between the two primary malignancies was 21 months (range: 3.5-52 months). Age, sex, FPC location, surgery, stage, and histology of lung cancer were regarded as independent prognostic factors for these patients. The prognosis of patients with urinary bladder cancer as FPC was the worst in the univariate (p = 0.024) and multivariate (p < 0.001) Cox analyses. Lung cancer-directed surgery could significantly improve long-term survival (HR = 0.22, p < 0.001). Additionally, the C-index of the established nomogram with acceptable calibration curves was 0.760 (95% CI: 0.744-0.776) in the training cohort and was 0.759 (95% CI: 0.737-0.781) in the validation cohort, showing an ideal model discrimination ability. The corresponding decision curve analysis (DCA) indicated the nomogram had relatively ideal clinical utility. Conclusions Cancer patients still have the risk of developing a new primary lung cancer. Close, lifelong follow-up is recommended for all these patients. Early detection for surgical treatment will significantly improve the prognosis of dual primary cancer patients with LCSPM. The nomogram developed to predict 1-, 3-, and 5-year OS rates has relatively good performance.