Establishment of a survival predictive model for patients with two synchronous multiple primary lung cancers: a multicenter cohort analysis.

Abstract

Background: The prognostic predictors of the synchronous multiple primary lung cancer (SMPLC) still remain unclear, and there is a lack of studies on the prognosis of SMPLC patients excluding those with multifocal ground-glass/lepidic (GG/L) nodules. The aim of this study is to develop an effective model for predicting survival of SMPLC patients.

Methods: In this multicenter cohort study, a total of 831 SMPLC patients presenting for lung cancer resection from January 2004 to January 2018 at five institutions were included for developing and validating a nomogram model. Specifically, 499 patients from the Cancer Hospital, Chinese Academy of Medical Sciences, and Beijing Chao-Yang Hospital, Capital Medical University were served as the training cohort. A total of 332 patients from The Third Xiangya Hospital of Central South University, the First Affiliated Hospital of University of Science and Technology of China, and Beijing Liangxiang Hospital were served as the external validation cohort. The nomogram model was compared with the Tumor Node Metastasis (TNM) system for the overall survival. The C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the model performance. A user-friendly website for SMPLC survival probability calculation was also provided for a better understanding of prognosis of patients with resected SMPLC.

Results: A total of seven independent risk factors were selected by conducting a multivariate analysis on the training set. Further, a nomogram model was developed with these factors. Both the internal and external validations exhibited good discrimination (C-index: internal, 0.827; external, 0.784). The NRI and IDI of this model were 0.33 and 0.21, respectively. The survival rates for 1-year, 3-year, and 5-year were consistent with the actual observed values. A set of cutoff values were determined by grouping the patients into three different groups. For each group, we should expect a significant distinction between survival curves.

Conclusions: The novel nomogram model enables accurate survival risk stratification of patients with resected SMPLC and may assist in decision-making that is conducive to patients with SMPLC at high risk.