Lymph nodes metastatic burden as a prognosticator for advanced non-small cell lung cancer: a real-world study

Abstract

Background: Current nodal classification inadequately predicts outcomes for advanced non-small cell lung cancer (NSCLC) patients. We investigated whether metastatic lymph node characteristics could improve prognostic accuracy.

Methods: We retrospectively analyzed 339 patients with advanced NSCLC who received immunotherapy as first-line treatment. Lymph node imaging was performed using computed tomography (CT), and the X-tile software was employed to determine optimal cutoff values for lymph node size and number. Prognostic factors were assessed using Kaplan-Meier survival curves and multivariate Cox regression analysis. The predictive accuracy of various N-staging was evaluated through time-dependent receiver operating characteristic (ROC) curves.

Results: The optimal cutoff values for lymph node size and number were 1.60 cm and 3, respectively. Kaplan-Meier analysis indicated that size, number, and fusion of metastatic lymph nodes were associated with worse overall survival (OS) in advanced NSCLC patients {hazard ratio (HR) [95% confidence interval (CI)]: 2.179 (1.432-3.316), 1.859 (1.226-2.821), and 3.635 (1.796-7.358)}. Multivariate Cox regression analysis identified lymph node size [HR (95% CI): 6.21 (1.19-32.25)] and fusion [HR (95% CI): 3.20 (1.32-7.75)] as independent prognostic factors for OS. Incorporating lymph node size into the conventional N-staging system improved prognostic accuracy, with a 3-year area under the curve (AUC) of 0.651 (95% CI: 0.535-0.767).

Conclusions: Lymph node size serves as a valuable indicator of tumor invasion and can enhance the existing N-staging system for more accurate prognosis prediction for more accurate prognosis prediction in advanced NSCLC.

Keywords: Lymph node staging; immunotherapy; metastatic burden; non-small cell lung cancer (NSCLC); prognosis.

Figure 1

Flowchart of patient selection. From an initial 3,426 advanced lung cancer patients (Jinling Hospital, 2018–2023), 339 met the inclusion criteria (stage III/IV NSCLC, first-line immunotherapy + HRCT) after excluding ineligible cases (e.g., EGFR/ALK mutations, prior surgery, or incomplete data). HRCT, high-resolution computed tomography; NSCLC, non-small cell lung cancer.

Figure 2

The optimal cut-off values of SLNs (A) and NLNs (B) were determined using the X-tile program (N=238, training cohort). Grey shows inverse association, while blue indicates direct association for OS. NLNs, the number of metastatic lymph nodes >1.00 cm; OS, overall survival; SLNs, size of lymph nodes.

Figure 3

Kaplan-Meier analysis of OS (N=80, validation cohort). (A) OS according to the SLNs; (B) OS according to the NLNs; (C) OS according to LNs fusion status. P values and HR (95% CI) were obtained from the log-rank test using R software. CI, confidence interval; HR, hazard ratio; LNs, lymph nodes; LNs fusion, multiple swollen lymph nodes adhere to each other; NLNs, the number of metastatic lymph nodes >1.00 cm; OS, overall survival; SLNs, size of lymph nodes.

Figure 4

Univariate and multivariate Cox regression analysis for OS (N=80, validation cohort). Group 1: univariate Cox regression analysis; Group 2: multivariate Cox regression analysis. CI, confidence interval; HR, hazard ratio; LNs, lymph nodes; LNs fusion, multiple swollen lymph nodes adhere to each other; NLNs, the number of metastatic lymph nodes >1.00 cm; OS, overall survival; SLNs, size of lymph nodes.

Figure 5

Time-dependent ROC curves. Fusion.N.stage: N staging based on fusion and anatomical location; Location.N.stage: N staging based on anatomical location and mediastinal metastasis stations; Size.N.stage: N staging based on size and anatomical location. AUC, area under the curve; CI, confidence interval; ROC, receiver operating characteristic.