Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China.
Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, 400016, China.
Ann Hematol. 2023 Dec;102(12):3465-3475. doi: 10.1007/s00277-023-05418-9. Epub 2023 Aug 24.
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.
本研究综合纳入了病理参数和国际预后指数（IPI）中的新的临床预后因素，旨在为弥漫性大 B 细胞淋巴瘤（DLBCL）患者的总生存开发一个列线图预后模型，以促进个体化治疗和管理策略。本研究共纳入 783 例患者进行分析。采用 LASSO 回归和逐步多因素 COX 回归筛选出显著变量，并构建列线图模型。通过校准曲线、受试者工作特征（ROC）曲线和决策曲线分析（DCA）曲线评估模型的性能和效果。此外，计算时间依赖性一致性指数（C-index）和时间依赖性 ROC 曲线下面积（AUC）以验证模型在不同时间点的稳定性。本研究利用 8 个选定的临床特征作为预测因子，开发了一个用于预测 DLBCL 患者总生存的列线图模型。该模型在 1、3 和 5 年的 AUC 均超过 0.7，具有较强的泛化能力。校准曲线显示对角线两侧的点均匀分布，三条校准曲线的斜率接近 1，且具有统计学意义，表明模型具有较高的预测准确性。此外，该模型具有重要的临床意义，有望在临床实践中广泛应用。该研究为 DLBCL 患者开发的新预后模型纳入了易于获取的临床参数，显著提高了预测准确性和性能。此外，该研究使用连续的一般队列，而不是临床试验，使研究结果更能代表更广泛的淋巴瘤患者群体，从而增加了其在常规临床护理中的适用性。
