Combined Neat Model for the Prognosis of Postoperative Stage III-N2 Non-small Cell Lung Cancer

Background: Lung cancer serum tumor markers including carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1), and carbohydrate antigen 125(CA125) as prognostic predictors is controversial. Therefore, this study aimed to evaluate the association between these markers and the survival of patients with postoperative stage III-N2 non-small cell lung cancer (NSCLC).

Methods: We enrolled 1011 patients with pathologically confirmed stage III-N2 NSCLC who underwent resection and whose pretreatment serum tumor marker levels were available. Patients were categorized according to their serum levels into low-, medium-, and high-risk groups. Overall survival (OS), progression-free survival (PFS), local regional relapse-free survival (LRFS), and distant metastasis-free survival (DMFS) were calculated from the date of resection. Their association with each serum tumor marker was assessed using the log-rank test.

Results: Abnormal CEA levels were associated with worse five-year OS, PFS and DMFS; abnormal CYFRA21-1 levels were associated with worse five-year OS and LRFS; and abnormal CA125 levels were associated with worse five-year OS, PFS, LRFS and DMFS. Among the risk groups, there were significant differences in five-year OS, PFS, LRFS and DMFS (P = 0.000). In propensity score matching analysis, the model also achieved prognostic significance for all four survival classifications (P = 0.001-0.004) among the three risk groups.

Conclusions: The combined model achieved prognostic significance for all survival outcome types. The serum tumor markers tested are useful prognostic predictors for postoperative NSCLC patients but not for all survival outcomes. The combination of the three indices is more reliable in predicting all four of the survival outcomes.

Key Points: SIGNIFICANT FINDINGS OF THE STUDY: Serum CEA, CYFRA21-1, and CA125 levels can be used as prognostic factors of postoperative N2 non-small cell lung cancer patients but not for all survival outcomes, suggesting that combinative detection of all three indices would be more reliable.

What This Study Adds: Our model utilizes available technology, with conventional cutoff values, inexpensive costs, and simple mathematics methods and, thus, can be feasibly employed by clinicians or oncologists.