Establishment and evaluation of a nomogram for predicting the survival outcomes of patients with diffuse large B-cell lymphoma based on International Prognostic Index scores and clinical indicators

基于国际预后指数评分和临床指标,建立并评估用于预测弥漫性大B细胞淋巴瘤患者生存结局的列线图

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Abstract

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive lymphoma, treatment outcomes of patients vary greatly. The current International Prognostic Index (IPI) is not enough to distinguish patients with poor prognosis, and genetic testing is very expensive, so a inexpensive risk prediction tool should be developed for clinicians to quickly identify the poor prognosis of DLBCL patients. METHODS: DLBCL patients (n=420; 18-80 years old) who received a combination of cyclophosphamide, adriamycin, vincristine, and prednisone (CHOP) with or without rituximab (R-CHOP) at our hospital between 2008 and 2017 were included in the study. Potential predictors of survival were determined by univariate and multivariate Cox regression analyses, and significant variables were used to construct predictive nomograms. The new prediction models were assessed using concordance indexes (C-indexes), calibration curves, and their clinical utility was assessed by decision curve analyses (DCAs). RESULTS: The 5-year overall survival (OS) rate was 70.62% and the 5-year progression-free survival (PFS) rate was 59.02%. The multivariate Cox analysis indicated that IPI, Ki-67, the lymphocyte/monocyte ratio, and first-line treatment with rituximab were significantly associated with survival. The C-index results indicated that a predictive model that included these variables had better discriminability for OS (0.73 vs. 0.67) and PFS (0.68 vs. 0.63) than the IPI-based model. The calibration plots showed good agreement with observations and nomogram predictions. The DCAs demonstrated the clinical value of the nomograms. CONCLUSIONS: Our study identified prognostic factors in patients who were newly diagnosed with DLBCL to construct an individualized risk prediction model, combined IPI with common clinical indicators. Our model might be a valuable tool that could be used to predict the prognosis of DLBCL patients who receive standard first-line treatment regimens. It enables clinicians to quickly identify some patients with possible poor prognosis and choose more active treatment for patients, such as chimeric antigen receptor T-cell (CART) Immunotherapy and other new drugs therapy, so as to prolong the PFS and OS of patients.

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