三阴性乳腺癌新辅助化疗后病理完全缓解的影响因素及预测模型构建

The factors affecting pathological complete response of triple negative breast cancer patients after neoadjuvant chemotherapy and the construction of related model

目的分析接受新辅助化疗的三阴性乳腺癌(TNBC)患者病理完全缓解(pCR)的影响因素,并构建临床预测模型。方法收集2018年5月-2021年5月空军军医大学西京医院收治的348例接受新辅助化疗的TNBC患者(作为建模集),以及2018年5月-2021年5月西安市第三医院收治的69例接受新辅助化疗的TNBC患者(作为验证集)。分析并比较建模集与验证集的临床病理特征,在建模集中采用LASSO回归模型分析TNBC患者新辅助化疗后pCR的独立影响因素,并构建列线图模型。采用Bootstrap法对模型进行内部验证;采用受试者工作特征(ROC)曲线评估模型的区分度,校准曲线评估模型的准确性,临床决策曲线分析(DCA)评价模型的临床获益和应用价值。结果建模集与验证集的手术术式和T分期比较差异有统计学意义(P<0.05)。LASSO回归模型分析结果显示,T分期、N分期、是否使用铂类药物及临床疗效评估是TNBC患者新辅助化疗后pCR的独立影响因素(P<0.05),将这些因素纳入并构建列线图预测模型。建模集中列线图模型预测TNBC患者新辅助化疗后pCR率的曲线下面积(AUC)为0.811(95%CI 0.763~0.859),验证集中为0.801(95%CI 0.727~0.928)。Bootstrap法内部验证显示一致性指数(C-index)为0.79,表明模型在建模集与验证集中均具有良好的区分度。校准曲线显示,列线图模型的预测生存率与实际生存率接近;DCA显示列线图模型的临床获益及应用价值较高。结论列线图模型能准确预测TNBC患者新辅助化疗后的pCR率,可为临床诊疗提供指导依据。

Objective To analyze the factors affecting pathological complete response(pCR)of triple-negative breast cancer(TNBC)patients after neoadjuvant chemotherapy,and construct a nomogram to forecast the pCR rate.Methods The clinical and pathological data of 348 TNBC patients who received neoadjuvant chemotherapy in the Air Force Medical University-Affiliated Xijing Hospital from May 2018 to May 2021 were collected and set as modeling set.The clinical and pathological data of 69 TNBC patients who received neoadjuvant chemotherapy in the Xi'an No.3 Hospital from May 2018 to May 2021 were collected and set as validation set.The clinical and pathological characteristics were compared between the modeling set and the validation set.In the modeling set,the independent risk factors of pCR in TNBC patients after neoadjuvant chemotherapy were screened by LASSO regression model analysis,and the nomogram model was constructed.Internal validation of the model was conducted using Bootstrap method,and the discrimination of the model was assessed by receiver operating characteristic(ROC)curve.The accuracy of the model was evaluated by the calibration curve and the clinical benefits and application value of the model were evaluated by clinical decision curve analysis(DCA).Results There were significant differences in surgical method and T stage between the patients in modeling set and validation set(P<0.05).The results of analysis of LASSO regression model showed that T stage,N stage,the use of platinum drugs and clinical efficacy evaluation were independent risk factors of pCR in TNBC patients after neoadjuvant chemotherapy(P<0.05).Based on the above variables,the nomogram models were constructed.In modeling set,area under curve(AUC)was 0.811(95%CI 0.763-0.859);in validation set,AUC was 0.801(95%CI 0.727-0.928).The Bootstrap method showed the C-index for internal validation was 0.79,indicating the model has good discrimination in both the modeling and validation sets.The calibration curve analysis showed that model predicted pCR rates had a good consistency with the actual observed values,and the DCA showed that model can bring clinical benefit.Conclusion The nomogram can accurately predict the pCR rates of TNBC patients after neoadjuvant chemotherapy and provide scientific basis for clinical diagnosis and treatment.