Evidence map›Paper›PMID 42676952›Full record

ArticleHemaSphere2026

AI for prognostic assessment of diffuse large B-cell lymphoma using H&E whole-slide images.

Jonas Lippl, Sarah Reinke, Stefan Schrod, Lukas Wolfseher, Paul Hüttl, Michael Huttner, Annette M Staiger, Ilske Oschlies, Karoline Koch, Edward G Michaelis and 17 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in HemaSphere, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

27 authors.

Jonas LipplDepartment of Medical Bioinformatics University Medical Center Göttingen Göttingen Germany.ORCID https://orcid.org/0009-0009-9492-3255
Sarah ReinkeDepartment of Pathology, Hematopathology Section University Hospital Schleswig-Holstein, Campus Kiel Kiel Germany.
Stefan SchrodDepartment of Statistical Genomics and Systems Genetics European Molecular Biology Laboratory (EMBL) Heidelberg Germany.
Lukas WolfseherDepartment of Statistical Bioinformatics University of Regensburg Regensburg Germany.
Paul HüttlDepartment of Statistical Bioinformatics University of Regensburg Regensburg Germany.
Michael HuttnerDepartment of Statistical Bioinformatics University of Regensburg Regensburg Germany.
Annette M StaigerDepartment of Pathology Robert Bosch Hospital Stuttgart Stuttgart Germany.
Ilske OschliesDepartment of Pathology, Hematopathology Section University Hospital Schleswig-Holstein, Campus Kiel Kiel Germany.
Karoline KochDepartment of Pathology, Hematopathology Section University Hospital Schleswig-Holstein, Campus Kiel Kiel Germany.
Edward G MichaelisDepartment of Pathology, Hematopathology Section University Hospital Schleswig-Holstein, Campus Kiel Kiel Germany.
Julia RichterDepartment of Pathology, Hematopathology Section University Hospital Schleswig-Holstein, Campus Kiel Kiel Germany.
Hilka Rauert-WunderlichInstitute of Pathology University of Würzburg Würzburg Germany.
Nicole SeifertDepartment of Medical Bioinformatics University Medical Center Göttingen Göttingen Germany.
Robin KoschPeter L. Reichertz Institute for Medical Informatics TU Braunschweig Braunschweig Germany.
Norbert SchmitzMedical Clinic A, University Hospital Münster Münster Germany.
Marita ZiepertDepartment of Medical Informatics, Statistics and Epidemiology University of Leipzig Leipzig Germany.ORCID https://orcid.org/0000-0002-0834-8111
Lorenz ThurnerDepartment of Internal Medicine I University Hospital of Saarland Homburg Germany.ORCID https://orcid.org/0000-0002-8167-2989
Viola PoeschelDepartment of Internal Medicine I University Hospital of Saarland Homburg Germany.
Bertram GlaßDepartment of Hematology, Oncology, and Tumor Immunology Helios Klinikum Berlin-Buch Berlin Germany.
Lorenz TrümperDepartment of Hematology, Oncology Georg August University of Göttingen Göttingen Germany.
Gerhard HeldDepartment of Internal Medicine Westpfalz-Klinikum Kaiserslautern Germany.
David W ScottBC Cancer and University of British Columbia Vancouver British Columbia Canada.
Andreas RosenwaldInstitute of Pathology University of Würzburg Würzburg Germany.
German OttDepartment of Pathology Robert Bosch Hospital Stuttgart Stuttgart Germany.
Rainer SpangDepartment of Statistical Bioinformatics University of Regensburg Regensburg Germany.
Michael AltenbuchingerDepartment of Medical Bioinformatics University Medical Center Göttingen Göttingen Germany.
Wolfram KlapperDepartment of Pathology, Hematopathology Section University Hospital Schleswig-Holstein, Campus Kiel Kiel Germany.ORCID https://orcid.org/0000-0001-7208-4117

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prediction of the outcome of large B-cell lymphomas/high-grade B-cell lymphomas (DLBCLs/HGBCLs) is based on clinical parameters and molecular testing, for example, for rearrangements of MYC (MYC-R) and MYC-R in combination with BCL2 and BCL6 translocations (double/triple hit). However, the group of DLBCL/HGBCL with poor outcome is not confined to MYC-R lymphomas, and fluorescence in situ hybridization (FISH) testing for MYC-R misses several high-risk lymphomas. We aimed to understand if artificial intelligence (AI) trained to identify MYC-R will delineate a poor prognostic subgroup. We generated a collection of digital hematoxylin and eosin (H&E)-stained slides of DLBCL/HGBCL (

Identifiers

PMID42676952
PMCPMC13528628

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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.