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Related Experiment Video

Updated: Jan 7, 2026

Synthesis of Core-shell Lanthanide-doped Upconversion Nanocrystals for Cellular Applications
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LINNDA: Lymphoma identification through neural network detection aid.

Paul Vincent Naser1,2,3,4, Maximilian Fischer2,5,6, Miriam Cindy Maurer5,7

  • 1Department of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.

Iscience
|January 1, 2026
PubMed
Summary

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An AI tool called LINNDA improved preoperative diagnosis accuracy for primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) by acting as a tie-breaker between human experts, achieving 89.9% accuracy.

Area of Science:

  • Neuro-oncology
  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis

Background:

  • Accurate preoperative differentiation between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is critical for effective patient management and surgical strategy.
  • Distinguishing between PCNSL and GBM preoperatively presents a significant diagnostic challenge in neuro-oncology.

Purpose of the Study:

  • To assess the diagnostic performance of an AI-assisted workflow, LINNDA (lymphoma identification through neural network detection aid), in differentiating PCNSL from GBM.
  • To compare the diagnostic accuracy of the AI-assisted workflow against human expert consensus and individual raters.

Main Methods:

  • Ten clinicians independently reviewed 46 cases of GBM and PCNSL.
  • An AI workflow (LINNDA) integrated a DenseNet169 neural network as a "tie-breaker" when clinicians disagreed.
Keywords:
Artificial intelligenceCancer

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  • The AI's diagnostic performance was evaluated against human expert consensus and in comparison to a third human rater across numerous scenarios.

    • Main Results:

    • Integrating AI predictions into the diagnostic process improved overall accuracy to 89.9%, surpassing expert consensus.
    • The AI-assisted approach demonstrated superiority over a third human rater in 5,108 evaluated scenarios.
    • LINNDA achieved a negative predictive value of 97% for ruling out PCNSL, supporting its clinical utility.

    Conclusions:

    • The AI-assisted LINNDA workflow significantly enhances the accuracy of preoperative differentiation between PCNSL and GBM.
    • LINNDA provides a reliable tool for clinical decision-making, particularly in cases with diagnostic uncertainty.
    • This AI approach offers a valuable adjunct to human expertise in neuro-oncology diagnostics.