The Addition of Peripheral Blood Inflammatory Indexes to Nomogram Improves the Predictive Accuracy of Survival in Limited-Stage Small Cell Lung Cancer Patients

Abstract

Background: Accumulated evidence for systemic inflammation response in several solid tumors prompts a possibility of prediction of patients' prognosis in a more accessible and valuable manner. However, the prognostic value of peripheral blood inflammatory markers in limited-stage small cell lung cancer (LS-SCLC) remains unclear. Therefore, we investigated the prognostic values of pretreatment inflammatory indexes in LS-SCLC patients.

Methods: We retrospectively identified 334 patients with LS-SCLC and collected their pretreatment serum levels of neutrophil, platelet, lymphocyte, leukocyte, hemoglobin, and albumin, then neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic inflammation index (SII) were calculated. Patients were dichotomized as low-Risk or high-Risk group based on their corresponding cutoff values. Univariate and multivariate analyses were conducted with a Cox proportional hazards model. The least absolute shrinkage and selection operator (LASSO)-Cox regression analysis was performed to construct the inflammation-related prognostic scoring system named Risk for OS. Nomograms were established to provide prognostic information, allowing for more individualized prediction of survival.

Results: Higher pretreatment platelet, lymphocyte, and albumin were indicators of favorable overall survival (OS), whereas higher NLR and SII were accompanied by inferior OS. The prognosis of patients with high Risk was significantly worse than that with low Risk in both the training group and the validation group (both p < 0.001). Comparable area under the curve (AUC) values between the training group and the validation group were observed, yielding 1-, 3-, and 5-year OS rates of 67.3% vs. 69.2%, 66.8% vs. 69.5%, and 66.7% vs. 71.4%, respectively. Multivariate analyses revealed that Risk [hazard ratio (HR) = 0.551, p < 0.001] was an independent negative prognostic indicator for OS, which was further verified in the validation set. The addition of Risk to nomogram (C-index = 0.643) harbored improved predictive accuracy for OS when compared with that of clinical factors alone (C-index = 0.606); the AUC values of 1-, 3-, and 5-year OS rates were 71.7% vs. 66.4%, 73.5% vs. 66.6%, and 71.9% vs. 65.6%, respectively.

Conclusions: Pretreatment peripheral blood inflammatory indexes may be a noninvasive serum biomarker for poor prognosis in LS-SCLC. The addition of Risk to the nomogram model could serve as a more powerful, economical, and practical method to predict survival for patients with LS-SCLC.

Keywords: biomarker; immunity; inflammation; limited-stage small cell lung cancer; nomogram.

Figure 1

Construction of the Risk by the least absolute shrinkage and selection operator (LASSO) model in the training cohort. (A) The LASSO-Cox regression model was used to generate the prognostic scoring system named Risk. (B) Ten-fold cross-validation for tuning parameter selection in the LASSO model via minimum criteria and 1-SE criteria. Herein, a value λ = 0.014 with log (λ) = -4.262 was selected by minimum criteria.

Figure 2

Kaplan–Meier survival analyses of Risk and Risk performance in time-dependent receiver operating characteristic (ROC) curves in (A) training and (B) validation cohorts.

Figure 3

Cox regression risk score distribution, prognostic relationship, and heatmap of the dichotomous data of the inflammatory components in Risk from (A) training and (B) validation cohorts. NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, Systemic Inflammation Index.

Figure 4

Nomograms for predicting the 1-, 3-, 5-year survival probability in limited-stage small cell lung cancer patients. (A) Nomograms locate each patient’s value on each variable axis, draw a line straight upward to the “Point” axis to determine the number of points received for each variable value, sum the scores achieved for each covariate, and locate the gross score on the “Total Points,” then delineate a line straight down to determine the probability of 1-, 3-, 5-year survival. (B) The receiver operating characteristic (ROC) curve analyses revealed the accuracy of prognosis in two nomogram models.

Figure 5

Calibration curves revealed the prediction effects of the nomogram graphs using (A) clinical variables and (B) Risk and clinical variables. (C) Decision curve analyses showed the net clinical benefits of the two nomograms.