LINNDA: Lymphoma identification through neural network detection aid

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.

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Publication Details

Journal
iScience
Published
2025-11-22
DOI
https://doi.org/10.1016/j.isci.2025.114153
Citations
1
Primary Topic
Glioma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
1.63
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article

LINNDA: Lymphoma identification through neural network detection aid

Jan‐Oliver Neumann, Sandro M. Krieg, Philip Dao Trong, Awais Akbar Bajwa et al.
1 citations
iScience
Glioma Diagnosis and Treatment
1.63
article

LINNDA: Lymphoma identification through neural network detection aid

Jan‐Oliver Neumann, Sandro M. Krieg, Philip Dao Trong, Awais Akbar Bajwa, Jessica Jesser, Martin Bendszus, Klaus Maier‐Hein, Maximilian Fischer, Gerhard Jungwirth, Paul Naser, Christine Jungk, Peter Neher, Martin Jakobs, Kianush Karimian‐Jazi, Manuel Kaes, Chiraz Ben-Salah, M. Mäurer
article en
1 citations

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.

iScienceVol. 28(12)
German Cancer Research Center (DE), Heidelberg University (DE), University Hospital Heidelberg (DE), National Center for Tumor Diseases (DE), Universitätsmedizin Göttingen (DE), Deutsches Konsortium für Translationale Krebsforschung (DE)
Openalex Percentile: Top 12%
Glioma Diagnosis and Treatment
1.63
DOIOpen Access PDFOpenAlex
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