探讨治疗前全身免疫炎症指数(SII)对乳腺癌新辅助化疗(NAC)后病理完全缓解(pCR)的预测价值，并结合相关临床病理特征构建临床预测模型。

回顾性收集2019年1月至2022年4月武汉大学中南医院甲状腺乳腺外科收治的157例接受NAC的乳腺癌患者临床资料。利用受试者操作特征(ROC)曲线评价SII对乳腺癌NAC后pCR的预测价值，同时根据约登指数的最大值确定其最佳临界值。进一步采用单因素、多因素Logistic回归分析乳腺癌患者临床病理特征与NAC后pCR的关系，同时构建临床预测模型。制作ROC曲线评价该模型，并采用Bootstrap法进行内部验证。

ROC曲线显示治疗前SII最佳临界值为418.92，预测乳腺癌NAC后pCR的曲线下面积(AUC)为0.737(95%CI：0.657~0.818)。多因素Logistic回归分析结果显示组织学分级(OR＝0.095, 95%CI：0.024~0.292, P＝0.001)、肿瘤大小(OR＝0.091, 95%CI：0.019~0.333, P＝0.001)、ER(OR＝0.104, 95%CI：0.026~0.348，P＝0.001)，HER-2(OR＝2.962, 95%CI：1.206~7.511, P＝0.019)及SII(OR＝0.149，95%CI：0.059~0.350，P<0.001)是乳腺癌NAC后pCR的独立预测因素。根据多因素Logistic回归结果，构建临床预测模型，其ROC曲线的AUC为0.868(95%CI: 0.813~0.920)。校准图显示，预测曲线与理想曲线贴合良好，预测值与实际值之间符合度的平均绝对误差为0.035。

治疗前SII可作为乳腺癌患者NAC后pCR的独立预测指标，同时结合组织学分级、肿瘤大小、ER和HER-2等临床病理特征建立的临床模型能更好地预测腺癌NAC疗效。

To explore the prediction value of pre-treatment systemic immune-inflammation index (SII) for pathological complete response(pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC), and to establish a clinical prediction model based on SII and other relevant clinicopathological characteristics.

We retrospectively collected the clinical data of 157 breast cancer patients undergoing NAC in the Department of Thyroid and Breast Surgery, Zhongnan Hospital, Wuhan University from January 2019 to April 2022. The predictive value of SII for pCR after NAC was evaluated by receiver operating characteristic (ROC) curve, and the cut-off value was determined according to the maximum Youden index. Univariate and multivariate logistic regression analyses were used to analyze the relationship between clinicopathological characteristics and pCR after NAC in breast cancer patients. Meanwhile, a clinical prediction model was established. The ROC curve was made to evaluate the model and the Bootstrap method was used for internal verification.

ROC curve analysis showed that the optimal cut-off value of pre-treatment SII was 418.92, the area under the curve (AUC) for predicting the pCR of breast cancer after NAC was 0.737(95%CI: 0.657-0.818). Multivariate logistic regression analysis showed that histological grade (OR=0.095, 95%CI: 0.024-0.292, P=0.001), tumor size(OR=0.091, 95%CI: 0.019-0.333, P=0.001), ER (OR=0.104, 95%CI: 0.026-0.348, P=0.001), HER-2 (OR=2.962, 95%CI: 1.206-7.511, P=0.019) and SII(OR=0.149, 95%CI: 0.059-0.350, P<0.001) were independent predictive factors of pCR after NAC in breast cancer patients. According to the results of multivariate logistic regression, a clinical prediction model was constructed, and the AUC of the ROC curve was 0.868 (95%CI: 0.813-0.920). The calibration plot shows that the prediction curve was close to the ideal curve, and the mean absolute error of the agreement between the predicted value and the actual value was 0.035.

Pre-treatment SII can be used as an independent factor predicting the pCR of breast cancer after NAC. Meanwhile, the clinical prediction model based on the clinicopathological characteristics (histological grade, tumor size, ER, and HER-2 and SII) can effectively predict the NAC response in breast cancer patients.