ArticleFrontiers in immunology2024
Real-world study and prognostic analysis of angioimmunoblastic T-cell lymphoma.
Article in Frontiers in immunology, 2024. 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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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.
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1 citing paper in PubMed.
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To analyze the clinical prognostic factors and treatments for angioimmunoblastic T-cell lymphoma (AITL) and develop a novel prognostic model specifically for AITL. Method: We retrospectively analyzed 231 patients with AITL from the First Affiliated Hospital of Zhengzhou University. Patients were enrolled between January 2014 and July 2023. The primary end points were overall survival (OS) and progression-free survival (PFS). Result: The patients' median age was 63 years, with 88.3% at an advanced stage (III/IV). The majority of patients (47.6%) received anthracycline-containing regimens, and there was no significant difference in survival compared with those treated with epigenetic-targeting and gemcitabine- containing regimens. The median PFS and OS were 6 and 17 months, respectively. In multivariate analysis, age >60 years, Eastern Cooperative Oncology Group performance status ≥2, elevated LDH, and splenomegaly were associated with inferior OS. Based on these four factors, a novel prognostic model (AITL model) was constructed that stratified patients into low-, intermediate-, and high-risk groups, with 2-year OS estimates of 63.6%, 42.1%, and 18.6%, respectively. Conclusion: Currently, there is no consensus on the optimal initial therapy for AITL, and the efficacy of anthracycline-containing regimens remains suboptimal. The novel model developed herein demonstrates predictive significance for both OS and PFS, and exhibits better stratification and discrimination capabilities.
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