Clinicopathological features in predicting pCR of NAC for breast cancer based on Logistic regression and Nomogram
10.3760/cma.j.cn.115807-20200302-00054
- VernacularTitle:基于Logistic回归和Nomogram图评价临床病理特征预测乳腺癌新辅助化疗病理完全缓解的临床价值
- Author:
Aizhai XIANG
;
Tianhan ZHOU
;
Jinwang DING
;
Keyi WANG
;
Liuqing YE
- From:
Chinese Journal of Endocrine Surgery
2021;15(2):122-127
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the predictive value of the clinicopathological features of breast cancer for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) and to establish a predictive model based on the clinicopathological features.Methods:Clinicopathological data collected from 182 patients who underwent NAC and surgical treatment in Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine from Jan. 2013 to Dec. 2019 were retrospectively analyzed. The univariate and multivariate analysis were used to analyze the relationship between clinicopathological features and pCR after neoadjuvant chemotherapy. The predictive value in predicting the efficacy of NAC was evaluated, the receiver operating characteristic (ROC) curve and Nomogram prediction model were constructed.Results:Multivariate Logistic regression analysis showed that progesterone receptor (PR) , human epidermal growth factor 2 (HER2) and platelet distribution width (PDW) were independent predictors of pCR after NAC for breast cancer. The area under the curve (AUC) of model for predicting efficacy of NAC was 0.810 (95% CI:0.745-0.864) and the sensitivity and specificity was 68.75% and 82.67% respectively when the Jordan Index is at its maximum. Conclusion:ER-, HER2+ and PDW≤13.4% show better efficacy of NAC. The Nomogram model based on them can accurately predict the efficacy of NAC and can provide a reference for the selection of treatment options in clinical diagnosis and treatment.
