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400016 重庆,重庆医科大学附属第一医院内分泌乳腺外科
[涂刚,E-mail:tugang68@126.com TU Gang, E-mail: tugang68@126.com
收稿:2020-07-15,
修回:2020-08-09,
纸质出版:2020-12-15
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唐铃丰, 舒秀洁, 严萍, 等. 雌激素受体阳性型乳腺癌新辅助化疗效果的影响因素及疗效预测[J]. 第三军医大学学报, 2020,43(23):2341-2349.
Lingfeng TANG, Xiujie SHU, Ping YAN, et al. Influencing factors and efficacy prediction of neoadjuvant chemotherapy for ER positive breast cancer[J]. Journal of Third Military Medical University, 2020, 43(23): 2341-2349.
唐铃丰, 舒秀洁, 严萍, 等. 雌激素受体阳性型乳腺癌新辅助化疗效果的影响因素及疗效预测[J]. 第三军医大学学报, 2020,43(23):2341-2349. DOI: 10.16016/j.1000-5404.202007121.
Lingfeng TANG, Xiujie SHU, Ping YAN, et al. Influencing factors and efficacy prediction of neoadjuvant chemotherapy for ER positive breast cancer[J]. Journal of Third Military Medical University, 2020, 43(23): 2341-2349. DOI: 10.16016/j.1000-5404.202007121.
目的
2
探讨雌激素受体(estrogen receptor,ER)阳性型乳腺癌新辅助化疗(neoadjuvant chemotherapy,NAC)疗效的影响因素并建立NAC疗效预测模型。
方法
2
纳入2015-2018年本科收治的321例接受NAC治疗的ER阳性型乳腺癌患者。参照实体肿瘤的疗效评价标准,将NAC后临床达完全缓解(complete response,CR)和部分缓解(partial response,PR)的患者作为有效组,而疾病稳定(stable disease,SD)和疾病进展(progressive disease,PD)作为无效组。回顾性分析两组患者不同临床病理差异,采用单因素及多因素二元Logistic回归分析所纳入的全部影响因素并筛选出有意义的预测因素,通过R语言构建出ER阳性型乳腺癌患者NAC疗效及化疗后病理完全缓解(pathologic complete response,pCR)率的预测模型,并通过受试者工作特征(receiver operating characteristic,ROC)曲线及校正曲线评价预测模型的预测能力。
结果
2
ER阳性型乳腺癌患者NAC总体有效率为75.39%,pCR率为15.26%。NAC有效组与无效组在年龄、月经状态、肿瘤最大径、腋窝淋巴结是否转移(clinical node stage,cN)、肿块血供、ER的表达、Ki-67的表达及化疗方案方面差异均有统计学意义(
P
<
0.05)。Logistic分析结果显示cN、肿块血供、ER与Ki-67的表达水平与ER阳性型乳腺癌NAC疗效有关(
P
<
0.05),而NAC后能否达pCR与肿瘤最大径、肿块血供、ER与Ki-67的表达水平有关(
P
<
0.05)。将有意义的预测因素构建预测ER阳性型乳腺癌患者NAC疗效及pCR的nomogram图。ER阳性型乳腺癌患者NAC有效性的预测模型ROC曲线下面积为0.785以及截点值为(0.696,0.760),同时校正曲线与标准参考线重合度很高;ER阳性型乳腺癌患者NAC后pCR率预测模型ROC曲线下面积为0.829以及截点值为(0.673,0.857),校正曲线个别值与标准参考线略有偏差。提示两个预测模型都具有较好的预测能力。
结论
2
肿块血供、cN、ER与Ki-67表达水平是影响ER阳性乳腺癌NAC疗效的独立因素,腋下淋巴癌转移、ER低表达、Ki-67高表达以及肿块血供良好的患者接受NAC获益率最高;而肿瘤最大径、肿块血供、ER与Ki-67表达水平是影响ER阳性乳腺癌NAC后pCR的独立因素,肿瘤较小、ER低表达、Ki-67高表达以及肿块血供良好的患者NAC后达pCR概率最高。构建的预测模型可初步评估ER阳性型乳腺癌患者能否从NAC获益以及达pCR的概率。
Objective
2
To investigate the influencing factors of neoadjuvant chemotherapy (NAC) in estrogen receptor (ER) positive breast cancer and establish a prediction model of NAC efficacy.
Methods
2
A total of 321 ER positive breast cancer patients who received NAC treatment for 4 cycles followed by surgical treatment in our department from 2015 to 2018 were enrolled in this study. According to the efficacy evaluation criteria of solid tumors
the patients who achieved complete response (CR) or partial response (PR) after NAC were assigned into effective group
while those with stable disease (SD) and progressive disease (PD) into ineffective group. The differences of clinical and pathological parameters were retrospectively analyzed and compared between the 2 groups. Univariate and multivariate logistic regression analyses were applied to screen out the significant influencing factors. R Language was employed to establish the prediction models of NAC efficacy and pathological CR (pCR) rate of ER positive breast cancer patients. Then the prediction ability of the prediction models was evaluated by receiver operating characteristic (ROC) curve and correction curve.
Results
2
The overall effective rate of NAC and pCR were 75.39% and 15.26%
respectively
in the patients with ER positive breast cancer. Significant differences were seen in age
menstrual status
maximum tumor diameter
clinical node stage (cN)
tumor blood supply
and ER and Ki-67 expression levels and chemotherapy regimen between the 2 groups (
P
<
0.05). Logistic analysis showed that cN
tumor blood supply
ER and Ki-67 expression levels were related to the efficacy of NAC (
P
<
0.05)
while the rate of pCR after NAC was related to the maximal tumor diameter
tumor blood supply
and ER and Ki-67 expression levels (
P
<
0.05). The nomograms of predicting NAC efficacy and pCR in ER positive breast cancer patients were constructed with significant inde
pendent influencing factors. The area under ROC curve (AUC) of NAC Effectiveness Prediction Model for ER positive breast cancer patients was 0.785
and the cut-off value was (0.696
0.760)
and with calibration curve coinciding well with the reference standard. The AUC of pCR prediction model was 0.829 and the cut-off value was (0.673
0.857)
and the 2 curves were slightly biased.
Conclusion
2
Tumor blood supply
cN
ER and Ki-67 expression levels are the independent factors of NAC efficacy in ER positive breast cancer patients. The patients with axillary lymph node metastasis
low ER expression
high Ki-67 expression and good blood supply of tumor can achieve the best efficacy of NAC. Tumor maximal diameter
tumor blood supply
ER and Ki-67 expression levels are independent factors of pCR after NAC. The patients with small tumor
low expression of ER
high expression of Ki-67 and good blood supply of tumor have the highest probability of pCR after NAC. The established nomogram can primarily evaluate the benefit and probability of pCR in these patients.
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