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预测新辅助化疗后乳腺癌病理完全缓解概率的列线图的开发与验证:一项回顾性队列研究

Development and Validation of a Nomogram to Predict the Probability of Breast Cancer Pathologic Complete Response after Neoadjuvant Chemotherapy: A Retrospective Cohort Study.

作者信息

Li Yijun, Zhang Jian, Wang Bin, Zhang Huimin, He Jianjun, Wang Ke

机构信息

Department of Breast Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

出版信息

Front Surg. 2022 Jun 9;9:878255. doi: 10.3389/fsurg.2022.878255. eCollection 2022.

Abstract

BACKGROUND

The methods used to predict the pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) have some limitations. In this study, we aimed to develop a nomogram to predict breast cancer pCR after NAC based on convenient and economical multi-system hematological indicators and clinical characteristics.

MATERIALS AND METHODS

Patients diagnosed from July 2017 to July 2019 served as the training group ( = 114), and patients diagnosed in from July 2019 to July 2021 served as the validation group ( = 102). A nomogram was developed according to eight indices, including body mass index, platelet distribution width, monocyte count, albumin, cystatin C, phosphorus, hemoglobin, and D-dimer, which were determined by multivariate logistic regression. Internal and external validation curves are used to calibrate the nomogram.

RESULTS

The area under the receiver operating characteristic curve was 0.942 (95% confidence interval 0.892-0.992), and the concordance index indicated that the nomogram had good discrimination. The Hosmer-Lemeshow test and calibration curve showed that the model was well-calibrated.

CONCLUSION

The nomogram developed in this study can help clinicians accurately predict the possibility of patients achieving the pCR after NAC. This information can be used to decide the most effective treatment strategies for patients.

摘要

背景

用于预测新辅助化疗(NAC)后病理完全缓解(pCR)的方法存在一定局限性。在本研究中,我们旨在基于便捷且经济的多系统血液学指标和临床特征,开发一种列线图以预测NAC后乳腺癌的pCR。

材料与方法

将2017年7月至2019年7月诊断的患者作为训练组(n = 114),将2019年7月至2021年7月诊断的患者作为验证组(n = 102)。根据包括体重指数、血小板分布宽度、单核细胞计数、白蛋白、胱抑素C、磷、血红蛋白和D - 二聚体在内的八个指标开发列线图,这些指标通过多因素逻辑回归确定。使用内部和外部验证曲线对列线图进行校准。

结果

受试者工作特征曲线下面积为0.942(95%置信区间0.892 - 0.992),一致性指数表明列线图具有良好的区分度。Hosmer - Lemeshow检验和校准曲线表明模型校准良好。

结论

本研究开发的列线图可帮助临床医生准确预测患者在NAC后达到pCR的可能性。该信息可用于为患者确定最有效的治疗策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22ee/9218360/2301c80d33ab/fsurg-09-878255-g001.jpg

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