Predictive value of systemic immune-inflammation index for pathological complete response in patients receiving neoadjuvant immunochemotherapy for locally advanced esophageal cancer

Abstract

Objectives: Neoadjuvant immunochemotherapy (nICT) has been confirmed with promising pathological complete response (pCR) among locally advanced esophageal squamous cell carcinoma (ESCC). However, there were still no reliable and accurate predictors to predict the treatment response. This study aimed to explore the predictive value of inflammatory and nutritional parameters.

Methods: Patients with ESCC who underwent radical surgery after nICT between January 2020 and April 2022 were included in the study. First, the least absolute shrinkage and selection operator regression (LASSO) logistic regression analysis was used to screen independent inflammatory and nutritional parameters. Secondly, univariate and multivariate logistic regression were used to screen and predict independent risk factors for pCR. Thirdly, a nomogram was constructed based on the independent predictive factors, and 30% of the included population was randomly selected as the validation cohort. We used the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve to evaluate the nomogram model.

Results: A total of 97 ESCC patients were screened for analysis, with 20 patients with pCR (20.32%). Only the systemic immune-inflammation index (SII) was screened after LASSO-logistic regression when λ was 0.06. The cut-off value of SII was 921.80 with an area under curve (AUC) value of 0.62. We defined SII > 921.80 as high SII and SII ≦ 921.80 as low SII. Further, the univariate and multivariate analysis further determined SII(OR = 3.94, 95%CI:1.26-12.42, P = 0.02) and clinical stage(OR = 0.35, 95%CI:0.12-0.98, P = 0.05) were independent predictive factors of pCR. One novel nomogram was established with an AUC value of 0.72 in the training cohort and 0.82 in the validation cohort. The Brier score of the calibration curve was 0.13. The calibration curve showed good agreement between the predicted results and the actual results in both the training cohort and the validation cohort. Compared with the clinical stage, the DCA confirmed a better clinical value of the nomogram model in both the training cohort and the validation cohort.

Conclusions: High pretreatment SII and early clinical stage were independently associated with pCR among ESCC receiving nICT. We further established and validated one novel nomogram model to effectively predict pCR among ESCC after nICT.

Keywords: esophageal squamous cell carcinoma; neoadjuvant immunochemotherapy; nomogram model; pathological complete response; systemic immune-inflammatory index.

Figure 1

Receiver operating characteristic (ROC) of the following 16 predictive factors: HALP, SIRI, SII, PNI, PLR, LMR, NLR, BMI, PLT, Hb, MONO, LY, NEUT, WBC, cholesterol, and Alb.

Figure 2

(A) regression analysis of influence factors based on Lasso for variable selection; (B) cross-validation of the regression model.

Figure 3

Nomogram model to predict pCR among ESCC patients receiving nICT.

Figure 4

(A) receiver operating characteristic (ROC) in the training cohort; (B) ROC in the validation cohort.

Figure 5

(A) Calibration curve in the training cohort; (B) calibration curve in the validation cohort.

Figure 6

(A) Decision curve analysis (DCA) in the training cohort; (B) DCA in the validation cohort. The model 1 stands for clinical stage, and the model 2 stands for combination of clinical stage and SII.