Improved method to stratify lymphoma patients with risk of secondary central nervous system involvement: A multicenter retrospective analysis. Issue 2 (26th September 2021)

• Record Type: Journal Article
• Authors: Lang, Mingxiao; Feng, Youqin; Meng, Xiangrui; Zhao, Jing; Song, Zheng; Qian, Zhengzi; Qiu, Lihua; Zhou, Shiyong; Liu, Xianming; Li, Lanfang; Yang, Haiyan; Song, Yuqin; Li, Wei; Zhang, Huilai
• Is Part Of: Hematological oncology. Volume 41:Issue 2(2023)
• Page Start: 239
• Page End: 247
• Publication Date: 2021-09-26
• Subjects: competing risk analysis -- diffused large B‐cell lymphoma -- nomogram -- risk factors -- SCNS involvement; Hematological oncology -- Periodicals; Hematology; Medical Oncology; 616.99418005
• DOI: 10.1002/hon.2928
• Languages: English
• ISSNs: 0278-0232

Abstract

Abstract: Secondary central nervous system (SCNS) involvement is an infrequent but universally fatal event in diffused large B‐cell lymphoma. The occurrence rate of SCNS involvement is approximately 5% but comes with a poor prognosis ever after. However, existing risk models to predict the incidence and prognosis of these patients with SCNS involvement lack both efficiency and accuracy. Controversy has also been reported regarding which risk factor may best identify the population with a high CNS relapse rate. In this study, we retrospectively analyzed 831 patients with diffused large B‐cell lymphoma, diagnosed between March 2008 and June 2018 in Tianjin Medical University Cancer Institute and Hospital, Beijing Cancer Hospital, and Cancer Hospital of The University of Chinese Academy of Science. Risk factors and nomogram were identified and established based on Fine and Gray's competing risk analysis. Among these patients, 55 (6.6%) of them eventually developed SCNS involvement. The 1‐ and 2‐year incidence for SCNS involvement were 3.9% and 4.7%, respectively. The median time from de novo diagnosis to CNS relapse was 8 months, and the median overall survival of these patients was 28 months. Considering the competing mortality before SCNS involvement, Fine and Gray's competing risk model was performed to analyze the characteristics related to SCNS involvement, and identified risk factors as the multiple extranodal involvements, elevated LDH and AMC level, and the involvement of breast, adrenal gland/kidney, pulmonary and bone. Corresponding factors were integrated into the competing nomogram for SCNS involvement ( c ‐index = 0.778). In conclusion, we present the first predictive nomogram to evaluate the risk to develop SCNS involvement in de novo DLBCL patients, which may help in both prognostic evaluation and clinical decision for this subgroup.