Trial reportScientific reports2016
The distinct clinical features and prognosis of the CD10⁺MUM1⁺ and CD10⁻Bcl6⁻MUM1⁻ diffuse large B-cell lymphoma.
Trial report in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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.
Who cites it
16 citing papers in PubMed.
- Article
- Case Report: Epididymal NK/T-cell lymphoma and adrenal diffuse large B-cell lymphoma are misdiagnosed as tuberculosis: two case reports and literature review.Frontiers in oncology · 2025Article
- Primary adrenal diffuse large B-cell lymphoma with normal adrenal cortex function: A case report.World journal of clinical cases · 2022Article
- MUM1 Expression versus Hans Algorithm to Predict Prognosis in Indonesian Diffuse Large B-Cell Lymphoma Patients Receiving R-CHOP.Cancer management and research · 2022Article
- Role of Cell-Origin Profiling Using Immunohistochemistry to Predict the Survival of Patients with Diffuse Large B-Cell Lymphoma in Indonesia.Yonago acta medica · 2021Article
- Aggressive CD5-Positive Primary Bone Marrow Diffuse Large B-Cell Lymphoma with Leukemic Presentation.Case reports in hematology · 2021Article
- Biomarkers and novel therapeutic approaches for diffuse large B-cell lymphoma in the era of precision medicine.Oncotarget · 2020Review
- Review
- Treatment outcome and prognostic factors in PCNSL.Diagnostic pathology · 2019Article
- Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL.Journal of translational medicine · 2019Article
- Clinical Significance ofInternational journal of medical sciences · 2019Article
- New developments in the pathology of malignant lymphoma. A review of the literature published from January-April 2016.Journal of hematopathology · 2016Review
- Primary cardiac lymphoma: autopsy case report and literature review.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieReview
- Primary diffuse large B-cell lymphoma of the nasal bone and palate: An unusual clinical presentation.Journal of oral and maxillofacial pathology : JOMFPArticle
- High Ki67 proliferation index but not cell-of-origin subtypes is associated with shorter overall survival in diffuse large B-cell lymphoma.Avicenna journal of medicineArticle
- Correlation between Immunohistochemical Subtype and Clinicopathological Features in Patients with Diffuse Large B-cell Lymphoma.Current health sciences journalArticle
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using an immunohistochemistry (IHC) based method, diffuse large B-cell lymphoma (DLBCL) can be classified into germinal center B-cell (GCB) and non-GCB subtypes. However, the prognostic value of Hans algorithm was contradictory in the literature. Using IHC and fluorescence in situ hybridization, we analyzed the antibodies applied in Hans algorithm and other genetic factors in 601 DLBCL patients and prognostic value of Hans algorithm in 306 cases who were treated with chemoimmunotherapy. The results showed that patients with GCB subtype have better overall survival (OS) and progression-free survival (PFS) than non-GCB cases. However, to some extent, double positive (CD10(+)MUM1(+), DP) and triple negative (CD10(-)Bcl6(-)MUM(-), TN) showed different clinical characteristics and prognosis to others that were assigned to the same cell-of-origin group. The DP group showed similar OS (median OS: both not reached, P = 0.3650) and PFS (median PFS: 47.0 vs. 32.7 months, P = 0.0878) with the non-GCB group while the TN group showed similar OS (median OS: both not reached, P = 0.9278) and PFS (median PFS: both not reached, P = 0.9420) with the GCB group. In conclusion, Recognition of specific entities in Hans algorithm could help us to accurately predict outcome of the patients and choose the best clinical management for them.
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