Evidence map›Paper›PMID 40928668›Full record

ArticleAnnals of hematology2025

An international prognostic index to predict the early chemoimmunotherapy failure of diffuse large B-cell lymphoma.

Qifan Xu, Yang Li, Beibei Xin, Chenyang Wang, Xinya Tao, Shihai Li, Hui Xiong, Xiaohua Zhou, Li Wang, Weili Zhao

Abstract read
In one paragraph

Article in Annals of hematology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Qifan Xu *Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yang Li *Beijing International Center for Mathematical Research, Department of Biostatistics, Peking University, Beijing, China.
Beibei XinShanghai Rightongene Biotechnology Co., Ltd, Shanghai, China.
Chenyang WangShanghai Rightongene Biotechnology Co., Ltd, Shanghai, China.
Xinya TaoChongqing Research Institute of Big Data, Peking University, Chongqing, China.
Shihai LiChongqing Research Institute of Big Data, Peking University, Chongqing, China.
Hui XiongShanghai Rightongene Biotechnology Co., Ltd, Shanghai, China.
Xiaohua ZhouBeijing International Center for Mathematical Research, Department of Biostatistics, Peking University, Beijing, China. azhou@math.pku.edu.cn.
Li WangShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. dr_wangli@126.com.ORCID http://orcid.org/0009-0001-2807-5085
Weili ZhaoShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. zhao.weili@yahoo.com.

Funding

National Natural Science Foundation of China 81830007
6 · The paper itself

Abstract

backgroundApproximately 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.

Indexed as

Antineoplastic Combined Chemotherapy ProtocolsImmunotherapyLymphoma, Large B-Cell, DiffuseAdultAgedAged, 80 and overBiomarkers, TumorFemaleHumansMaleMiddle AgedPrognosisTreatment FailureBiomarkers, TumorDiffuse large B-cell lymphomaEarly chemoimmunotherapy failurePredict modelPrognosis

Identifiers

PMID40928668
PMCPMC12552252

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.