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...
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| Hauptverfasser: | , , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
19 December 2025
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| In: |
iScience
Year: 2025, Jahrgang: 28, Heft: 12, Pages: 1-15 |
| ISSN: | 2589-0042 |
| DOI: | 10.1016/j.isci.2025.114153 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.isci.2025.114153 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S2589004225024149 
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| Verfasserangaben: | 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, Manuel Kaes, Philip Dao Trong, Martin Bendszus, Klaus Maier-Hein, Sandro M. Krieg, Jan-Oliver Neumann, and Peter Neher |
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| 245 | 1 | 0 | |a LINNDA |b lymphoma identification through neural network detection aid |c 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, Manuel Kaes, Philip Dao Trong, Martin Bendszus, Klaus Maier-Hein, Sandro M. Krieg, Jan-Oliver Neumann, and Peter Neher |
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| 520 | |a 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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