Evidence map›Paper›PMID 39252932›Full record

ArticlemedRxiv : the preprint server for health sciences2024

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

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

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors.

David ReineckeDepartment of Neurosurgery, New York University Grossman School of Medicine, New York, USA.ORCID 0000-0002-3298-9517
Nader MarooufDepartment of Neurosurgery, New York University Grossman School of Medicine, New York, USA.
Andrew SmithDepartment of Neurosurgery, New York University Grossman School of Medicine, New York, USA.
Daniel AlberDepartment of Neurosurgery, New York University Grossman School of Medicine, New York, USA.
John MarkertDepartment of Neurosurgery, New York University Grossman School of Medicine, New York, USA.
Nicolas K GoffDepartment of Neurosurgery, New York University Grossman School of Medicine, New York, USA.
Todd C HollonMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, USA.
Asadur ChowduryMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, USA.
Cheng JiangMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, USA.
Xinhai HouMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, 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.
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-ShughriInstitute for Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Lisa I KörnerDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Georg WidhalmDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Thomas Roetzer-PejrimovskyDivision of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria.
John G GolfinosDepartment of Neurosurgery, New York University Grossman School of Medicine, New York, USA.
Matija SnuderlDepartment of Pathology, New York Grossman School of Medicine, New York, USA.
Volker NeuschmeltingDepartment of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Daniel A OrringerDepartment of Neurosurgery, New York University Grossman School of Medicine, 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
NCI NIH HHS R01 CA226527
6 · The paper itself

Abstract

Accurate intraoperative diagnosis is crucial for differentiating between primary 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. We imaged unprocessed, label-free tissue samples intraoperatively using a portable Raman scattering microscope, generating virtual H&E-like images within less than three minutes. 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 two additional independent test cohorts. We trained on 54,000 SRH patch images sourced from surgical resections and stereotactic-guided biopsies, including various CNS tumor/non-tumor lesions. Training and test data were collected from four tertiary international medical centers. The final histopathological diagnosis served as ground-truth. In 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. 78.94%). 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. RapidLymphoma is valid and reliable in detecting PCNSL and differentiating from other CNS entities within three minutes, as well as visual feedback in an intraoperative setting. This leads to fast clinical decision-making and further treatment strategy planning.

Identifiers

PMID39252932
PMCPMC11383472

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LicenceCC BY-NC-ND
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Registered trials

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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.