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李曼曼, 徐斌, 邵营波, 刘慧. 不同雌激素受体状态下化疗周期数对乳腺癌新辅助化疗病理完全缓解率的影响[J]. 肿瘤防治研究, 2017, 44(1): 38-41. DOI: 10.3971/j.issn.1000-8578.2017.01.008
引用本文: 李曼曼, 徐斌, 邵营波, 刘慧. 不同雌激素受体状态下化疗周期数对乳腺癌新辅助化疗病理完全缓解率的影响[J]. 肿瘤防治研究, 2017, 44(1): 38-41. DOI: 10.3971/j.issn.1000-8578.2017.01.008
LI Manman, XU Bin, SHAO Yingbo, LIU Hui. Effect of Neoadjuvant Chemotherapy Cycles on Pathologic Complete Response Rate Under Different Estrogen Receptor Status of Breast Cancer[J]. Cancer Research on Prevention and Treatment, 2017, 44(1): 38-41. DOI: 10.3971/j.issn.1000-8578.2017.01.008
Citation: LI Manman, XU Bin, SHAO Yingbo, LIU Hui. Effect of Neoadjuvant Chemotherapy Cycles on Pathologic Complete Response Rate Under Different Estrogen Receptor Status of Breast Cancer[J]. Cancer Research on Prevention and Treatment, 2017, 44(1): 38-41. DOI: 10.3971/j.issn.1000-8578.2017.01.008

不同雌激素受体状态下化疗周期数对乳腺癌新辅助化疗病理完全缓解率的影响

Effect of Neoadjuvant Chemotherapy Cycles on Pathologic Complete Response Rate Under Different Estrogen Receptor Status of Breast Cancer

  • 摘要:
    目的 分析不同雌激素受体(estrogen receptor, ER)状态下化疗周期数对乳腺癌新辅助化疗病理完全缓解(pathologic complete response, pCR)率的影响。
    方法 回顾性分析430例乳腺癌新辅助化疗患者的完整病例资料,根据ER状态分为ER+组和ER-组,新辅助化疗周期分为3~4周期和6~8周期。采用χ2检验分析不同ER状态化疗周期数与pCR率相关性,采用多因素Logistic回归分析不同ER状态pCR的独立预测因素。
    结果 430例患者中pCR共103例(24.0%),ER+组6~8周期化疗相比3~4周期化疗使pCR率增加(χ2=7.924, P < =0.005),而ER-组pCR率增加不显著(P < =0.893)。多因素Logistic回归分析提示化疗周期数(P < =0.009)、靶向治疗(P < =0.007)、原发肿瘤大小(P < =0.000)是pCR的独立预测因素。对ER状态分层分析提示,仅在ER+组化疗周期数是pCR的独立预测因素(95%CI: 0.175~0.784, P < =0.009)。
    结论 6~8周期化疗相比3~4周期化疗可显著提高ER+新辅助化疗患者pCR率,但未显著提高ER-患者的pCR率。

     

    Abstract:
    Objective To investigate the correlation between pathologic complete response (pCR) rate and neoadjuvant chemotherapy cycles under different estrogen receptor (ER) status of breast cancer.
    Methods We retrospectively analyzed the complete clinical data of 430 breast cancer patients in the Tumor Hospital Affiliated to Zhengzhou University from April 1st, 2012 to March 30th, 2016. According to the ER status, the patients were divided into ER+ and ER-groups, and neoadjuvant chemotherapy cycles were divided into 3-4 and 6-8 cycles. The pCR rate of different neoadjuvant chemotherapy cycles were analyzed by χ2 test. The predictors of pCR rate were analyzed using multivariate logistic regression analysis.
    Results Of 430 patients, the pCR rate were 24.0%(103/430). Compared with 3-4 chemotherapy cycles, the pCR rate was increased in 6-8 cycles in ER+ group, and the difference was statistically significant (χ2=7.924, P < =0.005), while the increased pCR rate was not statistically significant in ER-group (χ2=0.018, P < =0.893). Multivariate logistic regression analysis showed that the chemotherapy cycles (P < =0.009), targeted therapy (P < =0.007), primary tumor size (P < =0.000) were the independent predictors of pCR rate in all patients. The stratified analysis of ER status showed that the chemotherapy cycles was an independent predictor of pCR rate only in ER+ patients (95%CI: 0.175-0.784, P < =0.009).
    Conclusion Compared with 3-4 chemotherapy cycles, 6-8 cycles of chemotherapy can significantly improve the pCR rate in ER+ patients, but no significantly increased pCR rate in ER-patients.

     

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