炎症标志物与T1期乳腺癌预后因素分析及预测模型构建

Analysis and Prediction Model Construction of Inflammatory Biomarkers and Prognostic Factors in T1 Stage Breast Cancer

目的探讨影响T1期乳腺癌预后的炎症标志物及相关临床因素并构建预测模型。方法回顾性分析2013年1月至2023年7月就诊于川北医学院附属医院333例T1期乳腺癌患者,依据患者是否出现疾病复发,分为复发组(41例),未复发组(292例),并收集术前1周患者外周血白细胞、血小板、中性粒细胞、单核细胞、淋巴细胞,并计算中性粒细胞淋巴细胞比值(NLR)、衍生中性粒细胞淋巴细胞比值(DNLR)、血小板淋巴细胞比率值(PLR)和淋巴细胞单核细胞比值(LMR)。分析这些炎症标志物和患者的临床病理变量与无病生存期(DFS)的关系,并开发了一种用于预测T1期乳腺癌复发的列线图。通过受试者工作特征(ROC)曲线评价模型的临床获益率,利用一致性指数(C-index)评价模型区分度、通过校准曲线评价模型校准能力、通过DCA决策曲线和临床影响曲线评估模型决策能力和临床应用价值。结果333例T1期乳腺癌患者复发41例(12.3%)。单因素分析显示PLR(OR 2.88,95%CI 1.24~6.68,P=0.01);NLR(OR 0.47,95%CI 0.20~1.12,P=0.09),LMR(OR 0.40,95%CI 0.19~0.84,P=0.01),DNLR(OR 0.46,95%CI 0.19~1.08,P=0.07)差异有统计学意义。多因素分析显示只有PLR(OR 2.52,95%CI 1.01~6.31,P<0.05)仍被确定为预后的独立预测因素。因此我们提出的包含PLR的诺模图对预测T1期乳腺癌复发具有较好的预测精度,列线图AUC 0.738,95%CI 0.654~0.821,模型与校准预测曲线贴合良好。结论包含PLR的列线图可以准确地预测T1期乳腺癌患者的个体化复发概率,可供临床参考。

Objective To investigate inflammatory markers and associated clinical factors that impact the prognosis of T1 stage breast cancer and develop a prediction model.Methods From January 2013 to July 2023,retrospective analysis of 333 patients with stage T1 breast cancer who attended the Affiliated Hospital of North Sichuan Medical College were classified into recurrence group(n=41)and non-recurrence group(n=292)based on whether the patients showed disease recurrence or not.Patients'peripheral blood leukocytes,platelets,neutrophils,monocytes,and lymphocytes were collected in the first week before surgery.And neutrophils-to-lymphocytes ratios(NLR),derived neutrophil-to-lymphocyte ratio(DNLR),platelets-to-lymphocytes ratio(PLR)and lymphocytes-to-monocytes ratios(LMR).We analyzed the relationship between these inflammatory markers and patients'clinicopathological variables and disease-free survival(DFS),and developed a column-line diagram for predicting recurrence of stage T1 breast cancer.Using receiver operating characteristic(ROC)curve analysis to calculate the area under the curve(AUC),clinical value of the model was evaluated.Additionally,the model's discrimination,calibration,decision curve analysis(DCA),clinical impact curve were evaluated to assess its ability to differentiate,calibrate,make decisions,and provide clinical value.Results 333 patients with stage T1 breast cancer had 41 recurrences(12.31%).Univariate analysis showed that PLR(OR 2.88,95%CI 1.24~6.68,P=0.01),NLR(OR 0.47,95%CI 0.20~1.12,P=0.09),LMR(OR 0.40,95%CI 0.19~0.84,P=0.01),DNLR(OR 0.46,95%CI 0.19~1.08,P=0.07).The multifactorial analysis revealed that PLR(OR 2.52,95%CI 1.01~6.31,P<0.05)remained a significant independent predictor of prognosis.Therefore,our proposed nomogram including PLR had good predictive accuracy for predicting recurrence of stage T1 breast cancer,with a column-line plot AUC 0.738,95%CI 0.654~0.821,and the model fit the calibrated prediction curve well.Conclusion Nomogram including PLR could accurately predict the individualised probability of recurrence in patients with stage T1 breast cancer,which could be used for clinical reference.