Evidence map›Paper›PMID 41476950›Full record

ArticleiScience2025

LINNDA: Lymphoma identification through neural network detection aid.

Paul Vincent Naser, Maximilian Fischer, Miriam Cindy Maurer, Kianush Karimian-Jazi, Chiraz Ben-Salah, Awais Akbar Bajwa, Martin Jakobs, Christine Jungk, Gerhard Jungwirth, Jessica Jesser and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

17 authors.

Paul Vincent NaserDepartment of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Maximilian FischerMedical Faculty, Heidelberg University, Grabengasse 1, 69117 Heidelberg, Germany.
Miriam Cindy MaurerDivision of Medical Image Computing, Germany, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
Kianush Karimian-JaziMedical Faculty, Heidelberg University, Grabengasse 1, 69117 Heidelberg, Germany.
Chiraz Ben-SalahMedical Faculty, Heidelberg University, Grabengasse 1, 69117 Heidelberg, Germany.
Awais Akbar BajwaDepartment of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Martin JakobsDepartment of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Christine JungkDepartment of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Gerhard JungwirthDepartment of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Jessica JesserMedical Faculty, Heidelberg University, Grabengasse 1, 69117 Heidelberg, Germany.
Manuel KaesDepartment of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Philip Dao TrongDepartment of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Martin BendszusMedical Faculty, Heidelberg University, Grabengasse 1, 69117 Heidelberg, Germany.
Klaus Maier-HeinDivision of Medical Image Computing, Germany, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
Sandro M KriegDepartment of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Jan-Oliver NeumannDepartment of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Peter NeherDivision of Medical Image Computing, Germany, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Preoperative differentiation between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is crucial for appropriate management and surgical planning. This study aims to evaluate the diagnostic performance of the AI-assisted workflow, LINNDA (lymphoma identification through neural network detection aid), in comparison to that of human raters. In total, ten clinicians independently reviewed 46 cases of GBM and PCNSL. The LINNDA workflow evaluated all 1,470 possible pairwise combinations. For each pair, whenever two clinicians disagreed, a DenseNet169 neural network was explicitly integrated as a third independent diagnostic opinion ("tie-breaker"). Integrating the AI-generated predictions improved overall accuracy to 89.9%, exceeding the expert consensus. We further established the superiority of our approach over a third human rater in another 5,108 possible combinatory scenarios. LINNDA has a negative predictive value of 97% for ruling out the diagnosis of PCNSL, providing a sound basis for clinical decision-making.

Indexed as

Artificial intelligenceCancer

Identifiers

PMID41476950
PMCPMC12752750

What OpenQuestion holds

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LicenceCC BY-NC-ND
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 OpenQuestion graph.