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基于网络的新型模型预测乳腺癌浸润性微乳头状癌患者的预后价值:真实世界数据回顾性队列研究。

A web-based novel model for predicting prognostic value in patients with invasive micropapillary carcinoma in breast cancer: a real-world data retrospective cohort study.

机构信息

Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.

The 1St School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, Zhejiang, China.

出版信息

Updates Surg. 2023 Oct;75(7):1997-2004. doi: 10.1007/s13304-023-01530-7. Epub 2023 May 24.

Abstract

Invasive micropapillary carcinoma (IMPC) accounts for less than 2% of all invasive breast cancers and usually associates with poor survival, so we investigated the prognostic factors for IMPC using a large population-based database and designed a web-based novel model. Clinicopathological prognostic factors were evaluated using the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox regression analysis was performed to evaluate the prognostic value of variables on the overall survival. A web-based nomogram was finally constructed to predict the survival probability. The model was validated in an external dataset. A web-based model, combined with age, radiation, clinical stage, and hormone receptor (HR) immunochemistry status four prognostic factors, was constructed. The C-index (0.714, 95% CI 0.683-0.741), calibration curves, and decision curves showed that this model was superior in prediction. By determining the cut-off values, high-risk group and low-risk group were divided. The Kaplan-Meier survival curves showed that these two groups had significantly different survival rates (P < 0.0001). The result of C-index, calibration curves, and Kaplan-Meier survival curves were consistent in the validation cohort. The novel nomogram with four risk factors resulted in accurate prognostic prediction for IMPC.

摘要

浸润性微乳头状癌(IMPC)占所有浸润性乳腺癌的比例不到 2%,通常与较差的生存相关,因此我们使用大型基于人群的数据库研究了 IMPC 的预后因素,并设计了一个基于网络的新型模型。使用监测、流行病学和最终结果(SEER)数据库评估临床病理预后因素。使用多变量 Cox 回归分析评估变量对总生存的预后价值。最后构建了一个基于网络的列线图来预测生存概率。该模型在外部数据集进行了验证。构建了一个基于网络的模型,结合年龄、放疗、临床分期和激素受体(HR)免疫化学状态四个预后因素。C 指数(0.714,95%CI 0.683-0.741)、校准曲线和决策曲线表明该模型具有更好的预测能力。通过确定截断值,将高危组和低危组进行划分。Kaplan-Meier 生存曲线表明,这两组的生存率有显著差异(P<0.0001)。验证队列中的 C 指数、校准曲线和 Kaplan-Meier 生存曲线的结果一致。该新型列线图具有四个风险因素,可准确预测 IMPC 的预后。

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