A Nomogram to Predict Recurrence and Survival of High-Risk Patients Undergoing Sublobar Resection for Lung Cancer: An Analysis of a Multicenter Prospective Study (ACOSOG Z4032)

Background Individualized prediction of outcomes may help with therapy decisions for patients with non-small cell lung cancer. We developed a nomogram by analyzing 17 clinical factors and outcomes from a randomized study of sublobar resection for non-small cell lung cancer in high-risk operable patients. The study compared sublobar resection alone with sublobar resection with brachytherapy. There were no differences in primary and secondary outcomes between the study arms, and they were therefore combined for this analysis. Methods The clinical factors of interest (considered as continuous variables) were assessed in a univariate Cox proportional hazards model for significance at the 0.10 level for their impact on overall survival (OS), local recurrence-free survival (LRFS), and any recurrence-free survival (RFS). The final multivariable model was developed using a stepwise model selection. Results Of 212 patients, 173 had complete data on all 17 risk factors. Median follow-up was 4.94 years (range, 0.04 to 6.22). The 5-year OS, LRFS, and RFS were 58.4%, 53.2%, and 47.4%, respectively. Age, baseline percent diffusing capacity of lung for carbon monoxide, and maximum tumor diameter were significant predictors for OS, LRFS, and RFS in the multivariable model. Nomograms were subsequently developed for predicting 5-year OS, LRFS, and RFS. Conclusions Age, baseline percent diffusing capacity of lung for carbon monoxide, and maximum tumor diameter significantly predicted outcomes after sublobar resection. Such nomograms may be helpful for treatment planning in early stage non-small cell lung cancer and to guide future studies.