BMC pulmonary medicine · 2024
Number of involved nodal stations predicts survival in small cell lung cancer.
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Zhang Han, Jiang Cong, Bian Dongliang, Zhang Jing, Zhu Yuming, Dai Jie, Jiang Gening
Аннотация
BACKGROUND: In small cell lung cancer (SCLC), the pathological N category is identical to it in non-small cell lung cancer (NSCLC) and remains unchanged over a decade. Here we verified the discriminability of number of involved nodal stations (nS) in SCLC and compared its efficacy in predicting survival with currently used pathological nodal (pN) staging.
METHODS: We retrospectively analyzed the patients who received operations and were pathologically diagnosed as SCLC at Shanghai Pulmonary Hospital between 2009 and 2019. X-tile software was adopted to determine optimal cut-off values for nS groups. Kaplan-Meier method and Cox regression analysis were used to compare survival between different groups. Decision curve analysis (DCA) was employed to evaluate the standardized net benefit.
RESULTS: A total of 369 patients were included. The median number of sampled stations was 6 (range 3-11), and the median number of positive stations was 1 (range 0-7). The optimal cutoff for nS groups was: nS0 (no station involved), nS1-2 (one or two stations involved), and nS ≥ 3 (three or more stations involved). Overall survival (OS) and relapse-free survival (RFS) were statistically different among all adjacent categories within the nS classification (p < 0.001, for both OS and RFS between each two subgroups), but survival curves for subgroups in pN overlapped (OS, p = 0.067; RFS, p = 0.068, pN2 vs. pN1). After adjusting for other confounders, nS was a prognostic indicator for OS and RFS. The DCA revealed that nS had improved predictive capability than pN.
CONCLUSIONS: Our cohort study demonstrated that the nS might serve as a superior indicator to predict survival than pN in SCLC and was worth considering in the future definition of the N category.
METHODS: We retrospectively analyzed the patients who received operations and were pathologically diagnosed as SCLC at Shanghai Pulmonary Hospital between 2009 and 2019. X-tile software was adopted to determine optimal cut-off values for nS groups. Kaplan-Meier method and Cox regression analysis were used to compare survival between different groups. Decision curve analysis (DCA) was employed to evaluate the standardized net benefit.
RESULTS: A total of 369 patients were included. The median number of sampled stations was 6 (range 3-11), and the median number of positive stations was 1 (range 0-7). The optimal cutoff for nS groups was: nS0 (no station involved), nS1-2 (one or two stations involved), and nS ≥ 3 (three or more stations involved). Overall survival (OS) and relapse-free survival (RFS) were statistically different among all adjacent categories within the nS classification (p < 0.001, for both OS and RFS between each two subgroups), but survival curves for subgroups in pN overlapped (OS, p = 0.067; RFS, p = 0.068, pN2 vs. pN1). After adjusting for other confounders, nS was a prognostic indicator for OS and RFS. The DCA revealed that nS had improved predictive capability than pN.
CONCLUSIONS: Our cohort study demonstrated that the nS might serve as a superior indicator to predict survival than pN in SCLC and was worth considering in the future definition of the N category.