Development and validation of nomogram prognostic model for predicting OS in patients with diffuse large B-cell lymphoma: a cohort study in China

作者全名："Li, Xiaosheng; Xu, Qianjie; Gao, Cuie; Yang, Zailin; Li, Jieping; Sun, Anlong; Wang, Ying; Lei, Haike"

作者地址："[Li, Xiaosheng; Gao, Cuie; Yang, Zailin; Li, Jieping; Sun, Anlong; Wang, Ying; Lei, Haike] Chongqing Univ Canc Hosp, Chongqing Canc Multi Om Big Data Applicat Engn Res, Chongqing 400030, Peoples R China; [Xu, Qianjie] Chongqing Med Univ, Sch Publ Hlth, Dept Hlth Stat, Chongqing 400016, Peoples R China"

通信作者："Lei, HK (通讯作者)，Chongqing Univ Canc Hosp, Chongqing Canc Multi Om Big Data Applicat Engn Res, Chongqing 400030, Peoples R China."

来源：ANNALS OF HEMATOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001060135300001

JCR分区：Q2

影响因子：3

年份：2023

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期号：　

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文献类型：Article; Early Access

关键词：Diffuse large b-cell lymphoma (DLBCL); Non-Hodgkin's lymphoma; Nomogram model; Prognosis; Overall survival

摘要："This study comprehensively incorporates pathological parameters and novel clinical prognostic factors from the international prognostic index (IPI) to develop a nomogram prognostic model for overall survival in patients with diffuse large B-cell lymphoma (DLBCL). The aim is to facilitate personalized treatment and management strategies. This study enrolled a total of 783 cases for analysis. LASSO regression and stepwise multivariate COX regression were employed to identify significant variables and build a nomogram model. The calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were utilized to assess the model's performance and effectiveness. Additionally, the time-dependent concordance index (C-index) and time-dependent area under the ROC curve (AUC) were computed to validate the model's stability across different time points. The study utilized 8 selected clinical features as predictors to develop a nomogram model for predicting the overall survival of DLBCL patients. The model exhibited robust generalization ability with an AUC exceeding 0.7 at 1, 3, and 5 years. The calibration curve displayed evenly distributed points on both sides of the diagonal, and the slopes of the three calibration curves were close to 1 and statistically significant, indicating high prediction accuracy of the model. Furthermore, the model demonstrated valuable clinical significance and holds the potential for widespread adoption in clinical practice. The novel prognostic model developed for DLBCL patients incorporates readily accessible clinical parameters, resulting in significantly enhanced prediction accuracy and performance. Moreover, the study's use of a continuous general cohort, as opposed to clinical trials, makes it more representative of the broader lymphoma patient population, thus increasing its applicability in routine clinical care."

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