Evidence map›Paper›PMID 42414604›Full record

ArticleLeukemia2026

Mantle cell lymphoma artificial intelligence prognostic index using hematoxylin and eosin histology.

Jonas Lippl, Sarah Reinke, Karoline Koch, Ilske Oschlies, Stefan Schrod, Lukas Wolfseher, Paul Hüttl, Michael Huttner, Silvia Beà, Elias Campo and 13 more

Abstract read
In one paragraph

Article in Leukemia, 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

23 authors.

Jonas Lippl *Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany.ORCID http://orcid.org/0009-0009-9492-3255
Sarah Reinke *Department of Pathology, Hematopathology Section, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.ORCID http://orcid.org/0000-0001-9729-4030
Karoline KochDepartment of Pathology, Hematopathology Section, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
Ilske OschliesDepartment of Pathology, Hematopathology Section, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.ORCID http://orcid.org/0000-0002-4003-6855
Stefan SchrodDepartment of Statistical genomics and systems genetics, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.ORCID http://orcid.org/0000-0001-9936-3984
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.
Silvia BeàInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.ORCID http://orcid.org/0000-0001-7192-2385
Elias CampoInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.ORCID http://orcid.org/0000-0001-9850-9793
Marco LadettoDepartment of Translational Medicine, Division of Hematology, University of Eastern Piedmont and SCDU Ematologia, Azienda Ospedaliera Santi Antonio e Biagio e Cesare Arrigo, Alessandria, Italy.
Sergio CogliattiInstitut of Pathology, Kantonsspital St.Gallen, St. Gallen, Switzerland.
Mats EhingerDivision of Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
Grzegorz RymkiewiczDepartment of Cancer Pathomorphology, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland.ORCID http://orcid.org/0000-0002-3478-8014
Valentina TabanelliDivisione di Diagnosi Emolinfopatologica, Haematopathology Division, European Institute of Oncology, Milano, Italy.
Hilka Rauert-WunderlichInstitute of Pathology, University of Würzburg, Würzburg, Germany.ORCID http://orcid.org/0000-0002-5535-2412
Andreas RosenwaldInstitute of Pathology, University of Würzburg, Würzburg, Germany.
Rainer SpangDepartment of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany.
Eva HosterInstitute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-0749-1389
Martin DreylingDepartment of Medicine III, LMU University Hospital, Munich, Germany.ORCID http://orcid.org/0000-0002-0358-5249
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. wklapper@path.uni-kiel.de.ORCID http://orcid.org/0000-0001-7208-4117
MULTIPLY project

Funding

Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 01KD2209ABundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 01KD2209CBundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 01KD2209DBundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 01KD2209EBundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 01KD2415ABundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 01KD2415BBundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 01KD2415DBundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 01KD2415E
6 · The paper itself

Abstract

The clinical course of Mantle Cell Lymphoma (MCL) varies between individual patients. Early detection of risk is crucial to assign MCL patients to novel treatment strategies. Most of the established biomarkers of outcome require specifically trained pathologists or molecular analysis. Here we introduce MAIPI (MCL Artificial Intelligence Prognostic Index), a deep learning algorithm trained only on Hematoxylin and Eosin (H&E) images of diagnostic biopsies of n = 428 MCL patients from clinical trials to assess prognosis. The capability of MAIPI to predict disease outcome was validated in an independent cohort of n = 140 patients treated with immunochemotherapy with and without ibrutinib. MAIPI selects areas of interest by itself and provides prognostic information independent of the MCL International Prognostic Index (MIPI) and Ki67 and without the need of molecular testing or expert pathologists evaluation.

Indexed as

Artificial IntelligenceEosine Yellowish-(YS)HematoxylinLymphoma, Mantle-CellFemaleHumansPrognosisEosine Yellowish-(YS)Hematoxylin

Identifiers

PMID42414604
PMCPMC13506322

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.