Evidence map›Paper›PMID 42803693›Full record

ArticleImmunity, inflammation and disease2026

Identification of Discriminatory Factors and Construction of a Nomogram for Differentiating IgG4-RD and DLBCL.

Yongkang Chen, Weimiao Li, Zhou Zhou, Jiqing Wu, Dongmei Zhou

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Article in Immunity, inflammation and disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yongkang ChenThe First Clinical College, Xuzhou Medical University, Xuzhou, China.
Weimiao LiThe First Clinical College, Xuzhou Medical University, Xuzhou, China.ORCID https://orcid.org/0009-0007-2366-7827
Zhou ZhouThe First Clinical College, Xuzhou Medical University, Xuzhou, China.
Jiqing WuThe First Clinical College, Xuzhou Medical University, Xuzhou, China.ORCID https://orcid.org/0009-0009-7209-6893
Dongmei ZhouRheumatology and Immunology Department, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.ORCID https://orcid.org/0000-0003-0163-8164

Funding

Clinical Research Program of the Affiliated Hospital of Xuzhou Medical University LCZX202413This study was supported by the Clinical Research Program of the Affiliated Hospital of Xuzhou Medical University LCZX202413
6 · The paper itself

Abstract

objectiveTo determine clinically useful indicators that distinguish IgG4-related disease (IgG4-RD) from diffuse large B-cell lymphoma (DLBCL) and to construct a predictive model that may assist early identification.

methodsWe retrospectively analyzed 19 patients with IgG4-RD and 37 with DLBCL who presented with lymphadenopathy and received treatment at the Affiliated Hospital of Xuzhou Medical University between January 2012 and October 2025. The distribution of continuous variables was checked before group comparisons. Data with an approximately normal distribution are expressed as mean ± standard deviation (mean ± SD) and were compared by independent-samples t-tests; skewed data are reported as median (interquartile range [IQR]) and were compared by the Mann-Whitney U test. Categorical variables are presented as counts and percentages (%) and were compared using the χ

resultsMultivariable logistic regression retained lymphocyte count, serum total protein, and prothrombin time (PT) as independent predictive factors. The model yielded an area under the receiver operating characteristic curve (AUC) of 0.91 (95% confidence interval [CI]: 0.82-0.99). Its calibration curve had a mean absolute error of 0.032. Across threshold probabilities from 0.2 to 1.0, decision curve analysis indicated greater net benefit than the "treat-all" and "treat-none" approaches.

conclusionIn this cohort, the predictive model showed potential to assist differentiation between IgG4-RD and DLBCL. Because the study was retrospective, single-center, and based on a limited sample, these findings should be regarded as preliminary and require confirmation in larger independent cohorts before clinical use.

Indexed as

Immunoglobulin G4-Related DiseaseLymphoma, Large B-Cell, DiffuseNomogramsAdultAgedDiagnosis, DifferentialFemaleHumansImmunoglobulin GMaleMiddle AgedRetrospective StudiesROC CurveImmunoglobulin Gdifferential diagnosisdiffuse large B‐cell lymphoma (DLBCL)IgG4‐related disease (IgG4‐RD)predictive nomogramretrospective analysis

Identifiers

PMID42803693
PMCPMC13618381

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