Evidence map›Paper›PMID 35790792›Full record

ArticleScientific reports2022

Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning.

Max Blokker, Philip C de Witt Hamer, Pieter Wesseling, Marie Louise Groot, Mitko Veta

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
8.2field-weighted citation impact, top 3% of its field
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

17 citing papers in PubMed, 32 citations in OpenAlex.

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  14. Computer Vision in Digital Neuropathology.Advances in experimental medicine and biology · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Max BlokkerDepartment of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. m.blokker@vu.nl.ORCID 0000-0003-3489-5773
Philip C de Witt HamerDepartment of Neurosurgery, Amsterdam UMC location VU University Medical Center, Amsterdam, The Netherlands.
Pieter WesselingDepartment of Pathology, Amsterdam UMC location VU University Medical Center, Amsterdam, The Netherlands.
Marie Louise GrootDepartment of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0001-5360-6987
Mitko VetaMedical Image Analysis Group (IMAG/e), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Amsterdam UMC Location Vrije Universiteit Amsterdam · NLVrije Universiteit Amsterdam · NLEindhoven University of Technology · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Management of gliomas requires an invasive treatment strategy, including extensive surgical resection. The objective of the neurosurgeon is to maximize tumor removal while preserving healthy brain tissue. However, the lack of a clear tumor boundary hampers the neurosurgeon's ability to accurately detect and resect infiltrating tumor tissue. Nonlinear multiphoton microscopy, in particular higher harmonic generation, enables label-free imaging of excised brain tissue, revealing histological hallmarks within seconds. Here, we demonstrate a real-time deep learning-based pipeline for automated glioma image analysis, matching video-rate image acquisition. We used a custom noise detection scheme, and a fully-convolutional classification network, to achieve on average 79% binary accuracy, 0.77 AUC and 0.83 mean average precision compared to the consensus of three pathologists, on a preliminary dataset. We conclude that the combination of real-time imaging and image analysis shows great potential for intraoperative assessment of brain tissue during tumor surgery.

Indexed as

Deep LearningGliomaSecond Harmonic Generation MicroscopyHumansImage Processing, Computer-AssistedMicroscopy

Identifiers

PMID35790792
PMCPMC9256596
OpenAlexW4283808251

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.