Qifan Xu, Yang Li, Beibei Xin, Chenyang Wang, Xinya Tao, Shihai Li, Hui Xiong, Xiaohua Zhou, Li Wang, Weili Zhao
{"title":"预测弥漫性大b细胞淋巴瘤早期化疗免疫治疗失败的国际预后指标。","authors":"Qifan Xu, Yang Li, Beibei Xin, Chenyang Wang, Xinya Tao, Shihai Li, Hui Xiong, Xiaohua Zhou, Li Wang, Weili Zhao","doi":"10.1007/s00277-025-06525-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong> Approximately 30-40% of diffuse large B-cell lymphoma (DLBCL) patients will develop relapse/refractory disease, who may benefit from novel therapies, such as CAR-T cell therapy. Thus, accurate identification of individuals at high risk of early chemoimmunotherapy failure (ECF) is crucial. Methods. Two prognostic models were developed to predict the ECF of DLBCL using clinical variables, namely the ECF-IPI-basic model (n = 1200) and the ECF-IPI-advance model (n = 699), respectively. 8 variables included age, gender, Ann Arbor stage, Hans classification, MYC and BCL2 double expression (DE), number of extranodal involvement sites, lactate dehydrogenase (LDH) and Eastern Cooperative Oncology Group performance status (ECOG PS) were considered to construct the basic model. The advanced model incorporated four additional biomarkers, interleukin-8 (IL-8), interleukin-2 receptor (IL-2R), β2-microglobulin (β2-MG), and D-dimer, totaling 12 predictive variables. Results. The ECF-IPI-basic model includes 5 variables, which was constructed with the formula of Age + Ann Arbor stage + DE (MYC and BCL2 double expression) + ECOG + LDH (lactate dehydrogenase). The ECF-IPI-advance model includes 7 variables, specifically, it was constructed with the formula of Age × Sex + Ann Arbor stage + DE + ECOG + LDH + IL-2R. Compared with the IPI score, greater discriminatory capacity was observed in both of the ECF-IPI-basic model (AUC, 0.768 vs. 0.701, p < 0.001) and the ECF-IPI-advance model (AUC, 0.824 vs. 0.724, p < 0.001) in identifying ECF. Conclusions. Overall, this study provides two potent ECF-IPI models that can effectively distinguish the patients with ECF from DLBCL, contributing to improve the prognosis of DLBCL.</p>","PeriodicalId":8068,"journal":{"name":"Annals of Hematology","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An international prognostic index to predict the early chemoimmunotherapy failure of diffuse large B-cell lymphoma.\",\"authors\":\"Qifan Xu, Yang Li, Beibei Xin, Chenyang Wang, Xinya Tao, Shihai Li, Hui Xiong, Xiaohua Zhou, Li Wang, Weili Zhao\",\"doi\":\"10.1007/s00277-025-06525-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong> Approximately 30-40% of diffuse large B-cell lymphoma (DLBCL) patients will develop relapse/refractory disease, who may benefit from novel therapies, such as CAR-T cell therapy. Thus, accurate identification of individuals at high risk of early chemoimmunotherapy failure (ECF) is crucial. Methods. Two prognostic models were developed to predict the ECF of DLBCL using clinical variables, namely the ECF-IPI-basic model (n = 1200) and the ECF-IPI-advance model (n = 699), respectively. 8 variables included age, gender, Ann Arbor stage, Hans classification, MYC and BCL2 double expression (DE), number of extranodal involvement sites, lactate dehydrogenase (LDH) and Eastern Cooperative Oncology Group performance status (ECOG PS) were considered to construct the basic model. The advanced model incorporated four additional biomarkers, interleukin-8 (IL-8), interleukin-2 receptor (IL-2R), β2-microglobulin (β2-MG), and D-dimer, totaling 12 predictive variables. Results. The ECF-IPI-basic model includes 5 variables, which was constructed with the formula of Age + Ann Arbor stage + DE (MYC and BCL2 double expression) + ECOG + LDH (lactate dehydrogenase). The ECF-IPI-advance model includes 7 variables, specifically, it was constructed with the formula of Age × Sex + Ann Arbor stage + DE + ECOG + LDH + IL-2R. Compared with the IPI score, greater discriminatory capacity was observed in both of the ECF-IPI-basic model (AUC, 0.768 vs. 0.701, p < 0.001) and the ECF-IPI-advance model (AUC, 0.824 vs. 0.724, p < 0.001) in identifying ECF. Conclusions. Overall, this study provides two potent ECF-IPI models that can effectively distinguish the patients with ECF from DLBCL, contributing to improve the prognosis of DLBCL.</p>\",\"PeriodicalId\":8068,\"journal\":{\"name\":\"Annals of Hematology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Hematology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00277-025-06525-5\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEMATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Hematology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00277-025-06525-5","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEMATOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景:大约30-40%的弥漫性大b细胞淋巴瘤(DLBCL)患者会复发/难治性疾病,他们可能受益于新的治疗方法,如CAR-T细胞治疗。因此,准确识别早期化疗免疫治疗失败(ECF)高风险个体至关重要。方法。采用临床变量建立了两种预测DLBCL ECF的预后模型,分别为ECF- ipi -基础模型(n = 1200)和ECF- ipi -推进模型(n = 699)。考虑年龄、性别、Ann Arbor分期、Hans分型、MYC和BCL2双表达(DE)、结外受病灶数量、乳酸脱氢酶(LDH)和东部肿瘤合作组织(Eastern Cooperative Oncology Group)表现状态(ECOG PS) 8个变量构建基本模型。先进的模型纳入了四个额外的生物标志物,白介素-8 (IL-8),白介素-2受体(IL-2R), β2-微球蛋白(β2-MG)和d -二聚体,共计12个预测变量。结果。ECF-IPI-basic模型包括5个变量,计算公式为Age + Ann Arbor分期+ DE (MYC和BCL2双表达)+ ECOG + LDH(乳酸脱氢酶)。ECF-IPI-advance模型包含7个变量,其构建公式为Age × Sex + Ann Arbor stage + DE + ECOG + LDH + IL-2R。与IPI评分相比,ecf -IPI-基本模型的歧视能力更大(AUC, 0.768 vs. 0.701, p
An international prognostic index to predict the early chemoimmunotherapy failure of diffuse large B-cell lymphoma.
Background: Approximately 30-40% of diffuse large B-cell lymphoma (DLBCL) patients will develop relapse/refractory disease, who may benefit from novel therapies, such as CAR-T cell therapy. Thus, accurate identification of individuals at high risk of early chemoimmunotherapy failure (ECF) is crucial. Methods. Two prognostic models were developed to predict the ECF of DLBCL using clinical variables, namely the ECF-IPI-basic model (n = 1200) and the ECF-IPI-advance model (n = 699), respectively. 8 variables included age, gender, Ann Arbor stage, Hans classification, MYC and BCL2 double expression (DE), number of extranodal involvement sites, lactate dehydrogenase (LDH) and Eastern Cooperative Oncology Group performance status (ECOG PS) were considered to construct the basic model. The advanced model incorporated four additional biomarkers, interleukin-8 (IL-8), interleukin-2 receptor (IL-2R), β2-microglobulin (β2-MG), and D-dimer, totaling 12 predictive variables. Results. The ECF-IPI-basic model includes 5 variables, which was constructed with the formula of Age + Ann Arbor stage + DE (MYC and BCL2 double expression) + ECOG + LDH (lactate dehydrogenase). The ECF-IPI-advance model includes 7 variables, specifically, it was constructed with the formula of Age × Sex + Ann Arbor stage + DE + ECOG + LDH + IL-2R. Compared with the IPI score, greater discriminatory capacity was observed in both of the ECF-IPI-basic model (AUC, 0.768 vs. 0.701, p < 0.001) and the ECF-IPI-advance model (AUC, 0.824 vs. 0.724, p < 0.001) in identifying ECF. Conclusions. Overall, this study provides two potent ECF-IPI models that can effectively distinguish the patients with ECF from DLBCL, contributing to improve the prognosis of DLBCL.
期刊介绍:
Annals of Hematology covers the whole spectrum of clinical and experimental hematology, hemostaseology, blood transfusion, and related aspects of medical oncology, including diagnosis and treatment of leukemias, lymphatic neoplasias and solid tumors, and transplantation of hematopoietic stem cells. Coverage includes general aspects of oncology, molecular biology and immunology as pertinent to problems of human blood disease. The journal is associated with the German Society for Hematology and Medical Oncology, and the Austrian Society for Hematology and Oncology.