ObjectiveTo investigate the effect and predictive value of systemic inflammatory markers on pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) for locally advanced breast cancer (LABC). MethodsThe clinicopathologic data of female patients with LABC who received NACT and radical surgical resection in the Department of Breast Surgery, Affiliated Hospital of Southwest Medical University from February 2019 to February 2022 were retrospectively analyzed. The factors affecting pCR after NACT were analyzed by the multivariate logistic regression and the prediction model was established. The efficiency of the prediction model was evaluated by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). ResultsA total of 98 patients were gathered, of which 29 obtained pCR, with a pCR rate of 29.6%. The multivariate analysis of binary logistic regression showed that the patients with non-menopausal status, negative estrogen receptor (ER), chemotherapy+targeted therapy, and systemic immune-inflammation index (SII) <532.70 (optimal critical value) were more likely to obtain pCR after NACT (P<0.05). The prediction model was established according to logistic regression analysis: Logit (P)=0.697–2.974×(menopausal status)–1.932×(ER status)+3.277×(chemotherapy regimen)–2.652×(SII). The AUC (95%CI) of the prediction model was 0.914 (0.840, 0.961), P<0.001. ConclusionsIt is not found that other inflammatory indicators such as neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio are associated with pCR after NACT. But SII is an important predictor of pCR after NACT for LABC and has a good predictive efficiency.
ObjectiveTo explore the predictive value of the pre-treatment systemic immune-inflammation index (SII) for major pathological response (MPR) after neoadjuvant immunochemotherapy (nICT) in esophageal cancer, and to construct a clinical prediction model combined with relevant clinical characteristics. Methods Retrospective collection of clinical data from patients with locally advanced esophageal cancer who received nICT followed by radical surgery at the First People's Hospital of Jining from January 2022 to June 2023. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of pre-treatment SII and neutrophil-lymphocyte ratio (NLR) for the efficacy of nICT in esophageal cancer. The optimal cut-off value was determined based on the maximum Youden index. Further, univariate and multivariate logistic regression analyses were employed to identify predictors for MPR after nICT in esophageal cancer and to construct a nomogram model. The model was evaluated using the area under the ROC curve (AUC), and internal validation was conducted using the Bootstrap method. ResultsA total of 63 patients were included, with 38 males and 25 females, and a median age of 67 (49-79) years. The ROC curve indicated that the optimal cut-off value for pre-treatment SII was 521.7, with an AUC of 0.701 [95%CI (0.564, 0.838)] for predicting MPR after nICT in esophageal cancer. The ROC curve showed that the optimal cut-off value for pre-treatment NLR was 2.32, with an AUC of 0.681 [95%CI (0.544, 0.818)]. Multivariate logistic regression analysis results revealed cT stage [OR=0.232, 95%CI (0.071, 0.759), P=0.016] and SII [OR=5.477, 95%CI (1.584, 18.939), P<0.001] as independent predictors for MPR after nICT in esophageal cancer. Based on the multivariate logistic regression results, a clinical prediction model was constructed, with an AUC of 0.789 on the ROC curve. The calibration plot showed a good agreement between the prediction curve and the ideal curve. ConclusionPre-treatment SII can serve as an independent predictive indicator for MPR in patients with esophageal cancer after nICT. The clinical model, established in combination with cT stage, can better predict the efficacy of nICT in esophageal cancer.