一种预测新辅助化疗三阴乳腺癌病理完全缓解的新型免疫炎症营养评分的开发与验证：一项双中心研究

Development and Validation of a New Immune-Inflammatory-Nutritional Score to Predict Pathological Complete Response in Triple-Negative Breast Cancer Undergoing Neoadjuvant Chemotherapy: A Two-Center Study.

作者信息

Wang Shuai, Song Yuting, Ding Jiajun, Li Mengxuan, Wang Yidi, Bai Yujie, Zi Haoyi, Sun Jianing, Fan Cong, Chen He, Luo Ting, Wang Ting

机构信息

Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, People's Republic of China.

Institute of Breast Health Medicine, Department of Medical Oncology, Cancer Center, Breast Center, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.

出版信息

J Inflamm Res. 2025 Jul 16;18:9365-9378. doi: 10.2147/JIR.S526429. eCollection 2025.

DOI:10.2147/JIR.S526429

PMID:40687145

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12276740/

Abstract

PURPOSE

To construct a novel immune-inflammatory-nutritional (IIN) score based on peripheral blood biomarkers related to inflammation, immunity, and nutrition, and to predict the efficacy of neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC).

PATIENTS AND METHODS

We retrospectively selected 431 patients with TNBC from Xijing Hospital, and then randomly divided the patients into a training set and an internal validation set in a ratio of 7:3. An external validation set was included with 154 patients selected from West China Hospital of Sichuan University. In the training set, patients were divided into the pathological complete response (pCR) group and the non-pathological complete response group. Univariate logistic regression analysis and LASSO regression analysis were used to select biomarkers that affect the efficacy of NAC in TNBC patients and to construct the IIN score. A nomogram model was constructed based on the IIN score and clinical pathological characteristics to predict whether TNBC patients could achieve pCR after NAC before treatment. The predictive performance and clinical application value of the nomogram model were assessed using the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis, and confusion matrix.

RESULTS

Through LASSO regression analysis, 6 biomarkers were ultimately identified to construct the scoring system. A nomogram model was constructed based on the IIN score and clinical pathological characteristics, and the ROC curve showed the areas under the curve to be 0.827, 0.786, and 0.754 in the training set, internal validation set, and external validation set, respectively. Calibration curves, decision curves, and confusion matrices all demonstrated that the nomogram model exhibited robust predictive performance and holds certain clinical application value.

CONCLUSION

The nomogram model based on the IIN score offers high predictive performance and can accurately predict the efficacy of NAC in TNBC patients before treatment, highlighting its clinical application potential.

摘要

目的

基于与炎症、免疫和营养相关的外周血生物标志物构建一种新型免疫-炎症-营养（IIN）评分，并预测三阴性乳腺癌（TNBC）患者新辅助化疗（NAC）的疗效。

患者和方法

我们回顾性选取了西京医院的431例TNBC患者，然后按照7:3的比例将患者随机分为训练集和内部验证集。纳入了从四川大学华西医院选取的154例患者作为外部验证集。在训练集中，将患者分为病理完全缓解（pCR）组和非病理完全缓解组。采用单因素逻辑回归分析和LASSO回归分析来选择影响TNBC患者NAC疗效的生物标志物，并构建IIN评分。基于IIN评分和临床病理特征构建列线图模型，以预测TNBC患者在治疗前接受NAC后是否能达到pCR。使用受试者工作特征（ROC）曲线、校准曲线、决策曲线分析和混淆矩阵评估列线图模型的预测性能和临床应用价值。