局部进展期直肠癌新辅助放化疗后病理完全缓解预测模型的建立与验证

刘恒; 陈亚利; 金成武; 邹燕蕾

doi:10.12019/j.issn.1671-5144.202409011

局部进展期直肠癌新辅助放化疗后病理完全缓解预测模型的建立与验证

Establishment and Validation of a Predictive Model for Pathological Complete Response After Neoadjuvant Radiotherapy for Locally Advanced Rectal Cancer

摘要

摘要:
目的建立局部进展期直肠癌（locally advanced rectal cancer，LARC）新辅助放化疗后病理完全缓解预测模型的建立，并对模型进行验证。
方法选择2022年1月至2024年3月在成都市第五人民医院行新辅助放化疗治疗的241例LARC患者进行回顾性分析。收集可能影响患者疗效的相关因素，根据疗效评价结果将患者分为病理完全缓解组与非病理完全缓解组，比较2组临床病理特征、影像学指标及实验室指标，以套索回归（least absolute shrinkage and selection operator，LASSO）筛选潜在变量后行多因素Logistic回归建立模型，以列线图对结果进行可视化，并对模型进行验证。
结果本研究纳入的241例LARC患者中有60例（24.90%）患者病理完全缓解。LASSO回归基础上行多因素Logistic回归分析结果显示：病理类型、治疗方案、壁外血管侵犯、收缩期峰值血流速度（peak systolic velocity，PSV）变化率、中性粒细胞与淋巴细胞比值（neutrophil to lymphocyte ratio，NLR）、癌胚抗原（carcinoembryonic antigen，CEA）为LARC新辅助放化疗后病理完全缓解的独立性相关因素（P<0.05）。模型验证结果显示：受试者工作特征曲线（receiver operating characteristic curve，ROC）曲线下面积为0.836，95%可信区间（confidence interval，CI）为（0.773~0.899）；模型曲线与理想模型曲线基本拟合成对角线。临床有效性分析结果显示当预测概率阈值0.18~0.85时使用本研究模型预测LARC新辅助放化疗后病理完全缓解的净获益最高。
结论 LARC患者新辅助放化疗后病理完全缓解受病理类型、治疗方案、壁外血管侵犯等因素的影响，根据影响因素建立的列线图模型用于预测LARC患者新辅助放化疗后病理完全缓解具有较高准确度与区分度。

Abstract:
Objective To establish the establishment of a predictive model for pathological complete response after neoadjuvant radiotherapy for locally advanced rectal cancer (LARC) and to validate the model.
Methods 241 patients with LARC treated with neoadjuvant radiotherapy at The Fifth People's Hospital of Chengdu from January 2022 to March 2024 were selected for retrospective analysis. Relevant factors that may affect the efficacy of the patients were collected, and the patients were divided into pathological complete response group and non-pathological complete response group according to the results of efficacy evaluation, comparing the clinicopathological characteristics, imaging indexes and laboratory indexes of the 2 groups, and the multifactorial Logistic regression was performed to establish a model after screening the potential variables by least absolute shrinkage and selection operator (LASSO) regression, and the results were visualized by Nomogram, and the model was verified.
Results Of the 241 LARC patients included in this study, 60 (24.90%) had complete pathological response. Multifactorial logistic regression analysis based on LASSO regression showed that pathological type, treatment regimen, extra-mural vascular invasion, rate of change of peak systolic velocity (PSV), neutrophil to lymphocyte ratio (NLR), and carcinoembryonic antigen (CEA) were the independent correlates of complete pathological response after neoadjuvant radiotherapy for LARC (P<0.05). The results of model validation showed that the area under the ROC curve was 0.836 with a 95% confidence interval (CI) of (0.773~0.899); the model curve was basically fitted to the ideal model curve as a diagonal. The results of clinical validity analysis showed the highest net benefit of predicting pathological complete response after neoadjuvant radiotherapy for LARC using the model of this study when the predictive probability threshold was 0.18~0.85.
Conclusion Pathological complete response after neoadjuvant radiotherapy in patients with LARC is influenced by the type of pathology, treatment regimen, and extra-mural vascular invasion, etc. The Nomogram model based on the influencing factors is used to predict pathological complete response after neoadjuvant radiotherapy in patients with LARC with a high degree of accuracy and discrimination.

HTML全文

参考文献(20)

施引文献

资源附件(0)