利用监测、流行病学和最终结果 (SEER) 数据库建立原发性乳腺弥漫性大 B 细胞淋巴瘤成人患者总生存期的预测模型。

IF 2.5 4区 医学 Q3 ONCOLOGY
Yishuai Liu, Haifeng Han, Hong Wei, Xinlong Wang, Zhaotang Luan, Kun Jiang
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引用次数: 0

摘要

目的我们的目的是确定关键的临床特征,从而建立一个准确的网络预测模型,用于估计原发性乳腺弥漫大B细胞淋巴瘤(PB-DLBCL)成人患者的总生存期(OS):我们首先纳入了所有从监测、流行病学和最终结果数据库中检索到的具有可用协变量的 PB-DLBCL 病例。我们依次采用单变量和多变量 Cox 回归方法来确定与预后独立相关的预测因子,然后将所有通过这些检验的预测因子构建成一个提名图,用于预测患者的 3 年、5 年和 10 年生存率。C指数和接收者操作特征曲线(ROC)用于评估预测的辨别力,校准曲线用于估计校准:共纳入了PB-DLBCL成人患者(中位年龄为69岁,四分位距[IQR]为57-79岁),其中466例(70%)被随机分配到开发队列中,其余病例被收集用于验证。利用已确定的三个独立预测因素(即年龄、分期和辐射),开发并验证了预测 OS 的精确提名图。我们的提名图的C指数都相对可接受,开发组和验证组的C指数分别为0.74(95% CI:0.71-0.78)和0.72(95% CI:0.70-0.75)。校准曲线还能准确预测所有病例中 PB-DLBCL 的预后。此外,ROC 曲线显示,与任何单一变量相比,我们的提名图具有更强的预测能力。为了直观地展示这一预测模型,我们在预后提名图的基础上开发了一种便捷的网络工具:结论:针对PB-DLBCL患者,我们开发并验证了一种更方便、更准确的基于网络的预测模型。外部评估和验证需要进一步的独立研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Modelling of Overall Survival in Adult Patients with Primary Diffuse Large B-cell Lymphoma of the Breast Using the Surveillance, Epidemiology, and End Results (SEER) Database.

Objective: We aimed to identify critical clinical features to develop an accurate webbased prediction model for estimating the overall survival (OS) of primary breast diffuse large Bcell lymphoma (PB-DLBCL) adult patients.

Methods: We first included all PB-DLBCL cases with available covariates retrieved from the Surveillance, Epidemiology, and End Results database. We sequentially performed univariate and multivariate Cox regression approaches to identify the predictors independently associated with prognosis, and all the predictors that passed these tests were then constructed to build a nomogram for predicting 3-, 5-, and 10-year survival rates of patients. The C-index and the receiver operating characteristic curve (ROC) were used to evaluate the prediction discrimination, and the calibration curve was applied to estimate the calibration.

Results: A total of PB-DLBCL adult patients were included (median age was 69 with the interquartile range [IQR] of 57-79 years), of which 466 (70%) were randomly allocated to the development cohort, and the remaining cases were collected for validation. Using three identified independent predictors (i.e., age, stage, and radiation), an accurate nomogram for predicting OS was developed and validated. The C-indices of our nomogram were both relatively acceptable, with 0.74 (95% CI: 0.71-0.78) and 0.72 (95% CI: 0.70-0.75) for the development and validation cohorts, respectively. The calibration curves also accurately predicted the prognosis of PB-DLBCL in all cases. In addition, ROC curves showed our nomogram to possess superior predictive ability compared to any single variable. To visually present this prediction model, a convenient webbased tool was implemented based on our prognostic nomogram.

Conclusion: For patients with PB-DLBCL, a more convenient and accurate web-based prediction model was developed and validated, which showed relatively good performances in both discrimination and calibration during model development and validation. External evaluation and validation are warranted by further independent studies.

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来源期刊
CiteScore
4.50
自引率
7.10%
发文量
55
审稿时长
3 months
期刊介绍: Aims & Scope Recent Patents on Anti-Cancer Drug Discovery publishes review and research articles that reflect or deal with studies in relation to a patent, application of reported patents in a study, discussion of comparison of results regarding application of a given patent, etc., and also guest edited thematic issues on recent patents in the field of anti-cancer drug discovery e.g. on novel bioactive compounds, analogs, targets & predictive biomarkers & drug efficacy biomarkers. The journal also publishes book reviews of eBooks and books on anti-cancer drug discovery. A selection of important and recent patents on anti-cancer drug discovery is also included in the journal. The journal is essential reading for all researchers involved in anti-cancer drug design and discovery. The journal also covers recent research (where patents have been registered) in fast emerging therapeutic areas/targets & therapeutic agents related to anti-cancer drug discovery.
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