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全身免疫炎症指数在预测乳腺癌新辅助治疗后病理完全缓解中的作用及相关预测模型的建立。

The role of systemic immune-inflammation index in predicting pathological complete response of breast cancer after neoadjuvant therapy and the establishment of related predictive model.

作者信息

Zhang Ziyue, Zeng Yixuan, Liu Wenbo

机构信息

Faculty of Medicine, Debrecen University, Debrecen, Hungary.

Faculty of Medicine, University of Bonn, Bonn, Germany.

出版信息

Front Oncol. 2024 Nov 1;14:1437140. doi: 10.3389/fonc.2024.1437140. eCollection 2024.

Abstract

OBJECTIVE

To investigate the role of systemic immune-inflammation index (SII) in complete pathological response (pCR) of breast cancer patients after neoadjuvant chemotherapy, and to establish and validate a nomogram for predicting pCR.

METHODS

Breast cancer patients were selected from the First Affiliated Hospital of Xi'an Jiaotong University from January 2020 to December 2023. The optimal cut-off value of SII was calculated via ROC curve. The correlation between SII and clinicopathological characteristics was analyzed by Chi-square test. Logistic regression analysis was performed to evaluate the factors that might affect pCR. Based on the results of Logistic regression analysis, a nomogram for predicting pCR was established and validated.

RESULTS

A total of 112 breast cancer patients were included in this study. 33.04% of the patients achieved pCR after neoadjuvant therapy. Chi-square test showed that SII was significantly correlated with pCR (P=0.001). Logistic regression analysis suggested that Ki-67 (P=0.039), therapy cycle (P<0.001), CEA (P=0.025) and SII (P=0.019) were independent predictors of pCR after neoadjuvant chemotherapy. A nomogram based on Ki-67, therapy cycle, CEA and SII showed a good predictive ability.

CONCLUSION

Ki-67, therapy cycle, CEA and SII were independent predictors of pCR of breast cancer after neoadjuvant chemotherapy. The nomogram based on the above positive factors showed a good predictive ability.

摘要

目的

探讨全身免疫炎症指数(SII)在乳腺癌患者新辅助化疗后完全病理缓解(pCR)中的作用,并建立和验证预测pCR的列线图。

方法

选取2020年1月至2023年12月在西安交通大学第一附属医院就诊的乳腺癌患者。通过受试者工作特征(ROC)曲线计算SII的最佳截断值。采用卡方检验分析SII与临床病理特征之间的相关性。进行逻辑回归分析以评估可能影响pCR的因素。基于逻辑回归分析结果,建立并验证预测pCR的列线图。

结果

本研究共纳入112例乳腺癌患者。33.04%的患者在新辅助治疗后达到pCR。卡方检验显示SII与pCR显著相关(P = 0.001)。逻辑回归分析表明,Ki-67(P = 0.039)、治疗周期(P < 0.001)、癌胚抗原(CEA)(P = 0.025)和SII(P = 0.019)是新辅助化疗后pCR的独立预测因素。基于Ki-67、治疗周期、CEA和SII的列线图显示出良好的预测能力。

结论

Ki-67、治疗周期、CEA和SII是乳腺癌新辅助化疗后pCR的独立预测因素。基于上述阳性因素的列线图显示出良好的预测能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdb6/11564179/96f95e5529c7/fonc-14-1437140-g001.jpg

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