Evidence map›Paper›PMID 39673805›Full record

ArticleNeuro-oncology2025

Fast intraoperative detection of primary central nervous system lymphoma and differentiation from common central nervous system tumors using stimulated Raman histology and deep learning.

David Reinecke, Nader Maarouf, Andrew Smith, Daniel Alber, John Markert, Nicolas K Goff, Todd C Hollon, Asadur Chowdury, Cheng Jiang, Xinhai Hou and 13 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors.

David ReineckeDepartment of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.ORCID 0000-0002-3298-9517
Nader MaaroufDepartment of Neurosurgery, New York University Grossman School of Medicine, New York, New York, USA.
Andrew SmithDepartment of Neurosurgery, New York University Grossman School of Medicine, New York, New York, USA.
Daniel AlberDepartment of Neurosurgery, New York University Grossman School of Medicine, New York, New York, USA.
John MarkertHeersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Nicolas K GoffDepartment of Neurosurgery, University of Texas at Austin Dell Medical School, Austin, Texas, USA.
Todd C HollonMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA.
Asadur ChowduryMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA.
Cheng JiangMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA.
Xinhai HouMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA.
Anna-Katharina MeissnerDepartment of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Gina FürtjesDepartment of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Maximilian I RugeCenter for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.ORCID 0000-0003-0466-6646
Daniel RuessCenter for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Thomas StehleInstitute for Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Abdulkader Al-Shughri
Lisa I KörnerDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Georg WidhalmDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.ORCID 0000-0001-6014-0273
Thomas Roetzer-PejrimovskyComprehensive Center for Clinical Neurosciences & Mental Health, Medical University of Vienna, Vienna, Austria.ORCID 0000-0001-8875-7304
John G GolfinosDepartment of Neurosurgery, New York University Grossman School of Medicine, New York, New York, USA.ORCID 0000-0003-4221-0180
Matija SnuderlDepartment of Pathology, New York Grossman School of Medicine, New York, New York, USA.ORCID 0000-0003-0752-0917
Volker NeuschmeltingCenter for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Daniel A OrringerDepartment of Pathology, New York Grossman School of Medicine, New York, New York, USA.

Funding

Clinical Translation of Stimulated Raman HistologyR01CA226527 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ORRINGER, DANIEL · 2018 to 2025
$4.4M
German Research Foundation 521771484NCI NIH HHS R01 CA226527NIH HHS R01CA226527
6 · The paper itself

Abstract

backgroundAccurate intraoperative diagnosis is crucial for differentiating between primary central nervous system (CNS) lymphoma (PCNSL) and other CNS entities, guiding surgical decision-making, but represents significant challenges due to overlapping histomorphological features, time constraints, and differing treatment strategies. We combined stimulated Raman histology (SRH) with deep learning to address this challenge.

methodsWe imaged unprocessed, label-free tissue samples intraoperatively using a portable Raman scattering microscope, generating virtual H&E-like images within <3 min. We developed a deep learning pipeline called RapidLymphoma based on a self-supervised learning strategy to (1) detect PCNSL, (2) differentiate from other CNS entities, and (3) test the diagnostic performance in a prospective international multicenter cohort and 2 additional independent test cohorts. We trained on 54 000 SRH patch images sourced from surgical resections and stereotactic-guided biopsies, including various CNS neoplastic/nonneoplastic lesions. Training and test data were collected from 4 tertiary international medical centers. The final histopathological diagnosis served as ground truth.

resultsIn the prospective test cohort of PCNSL and non-PCNSL entities (n = 160), RapidLymphoma achieved an overall balanced accuracy of 97.81% ± 0.91, non-inferior to frozen section analysis in detecting PCNSL (100% vs. 77.77%). The additional test cohorts (n = 420, n = 59) reached balanced accuracy rates of 95.44% ± 0.74 and 95.57% ± 2.47 in differentiating IDH-wildtype diffuse gliomas and various brain metastasis from PCNSL. Visual heatmaps revealed RapidLymphoma's capabilities to detect class-specific histomorphological key features.

conclusionsRapidLymphoma proves reliable and valid for intraoperative PCNSL detection and differentiation from other CNS entities. It provides visual feedback within 3 min, enabling fast clinical decision-making and subsequent treatment strategy planning.

Indexed as

Central Nervous System NeoplasmsDeep LearningLymphomaSpectrum Analysis, RamanAdultAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedPrognosisProspective Studiesartificial intelligencebrain tumordeep learningintraoperative histopathologyoptical imagingprimary CNS lymphomastimulated Raman histology

Identifiers

PMID39673805
PMCPMC12187372

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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.