HER2阳性乳腺癌新辅助化疗联合双靶疗效的列线图预测模型建立

Establishment of a Nomogram Prediction Model for Neoadjuvant Chemotherapy Combined with Dual-Target Efficacy in HER2-Positive Breast Cancer

摘要: 目的：研究HER2阳性乳腺癌接受新辅助化疗联合双靶治疗疗效的相关影响因素，同时建立预测模型，旨在提高患者新辅助治疗疗效预测的有效性和准确性，从而为临床诊疗方案的选择提供参考。方法：回顾性分析2021年1月1日~2022年12月31日接受新辅助化疗联合双靶并完成手术的146例HER2阳性乳腺癌患者的临床病理资料，根据MP分级系统和RCB系统分为pCR组和非pCR组。采用单因素Logistic回归，将有统计学意义和可能有临床意义的指标纳入LASSO回归。根据LASSO回归筛选出的变量以列线图形式构建预测模型，然后采用受试者工作特性曲线(receiver operating characteristic curve, ROC)评价模型的预测效能，使用校准曲线评价模型的准确性。采用DCA评价模型的临床应用价值。最后在验证集中对模型进行内部验证。结果：采用单因素Logistic筛选出了ER状态、PR状态、中性粒细胞与淋巴细胞比值(NLR)及四周期后RECIST1.1四个预测变量，将单因素分析中具有统计学意义的4个特征因素纳入到LASSO回归，根据LASSO分析结果，将具有统计学意义的自变量构建模型及列线图，训练集和验证集的ROC曲线下面积(area under curve, AUC)分别为0.853和0.741，内部验证显示列线图具有较好的预测能力。结论：ER状态、PR状态、NLR及四周期后RECIST1.1是影响HER2阳性乳腺癌患者接受新辅助治疗后达到pCR的独立预测因素。基于以上因素构建的列线图模型对新辅助治疗后pCR有较好的预测效能。

Abstract: Purpose: To study the factors influencing the efficacy of neoadjuvant chemotherapy combined with dual-target therapy for HER2-positive breast cancer, and to establish a prediction model, aiming to improve the effectiveness and accuracy of predicting the efficacy of neoadjuvant therapy in patients, thereby providing information for the selection of clinical diagnosis and treatment plans. Methods: Retrospectively analyzed the clinicopathological data of 146 HER2-positive breast cancer patients who received neoadjuvant chemotherapy combined with dual target and completed surgery from January 1, 2021 to December 31, 2022, and were divided into pCR group and non-pCR group according to the MP grading system and RCB system. Single-factor logistic regression was used, and indicators with statistical significance and possible clinical significance were included in LASSO regression. A prediction model was constructed in the form of a nomogram based on the variables selected by LASSO regression, and then the receiver operating characteristic curve (ROC) was used to evaluate the prediction performance of the model, and the calibration curve was used to evaluate the accuracy of the model. DCA was used to evaluate the clinical application value of the model. Finally, the model is internally validated in the validation set. Result: Single-factor Logistic regression was initially used to identify four predictive variables: ER status, PR status, neutrophil-to-lymphocyte ratio (NLR), and RECIST1.1 after four cycles. The four significant factors from the single-factor analysis were then incorporated into LASSO regression. Based on the results of the LASSO analysis, statistically significant independent variables were used to construct predictive models and nomograms. The areas under the ROC curve (AUC) for the training set and validation set were 0.853 and 0.741, respectively. Internal validation demonstrated the nomogram’s strong predictive ability. Conclusion: ER, PR, NLR and RECIST1.1 after four cycles are independent predictive factors for patients with HER2-positive breast cancer to achieve pCR after neoadjuvant therapy. The nomogram model constructed based on the above factors has good predictive performance for pCR after neoadjuvant treatment.