Evidence map›Paper›PMID 38746683›Full record

ArticleFrontiers in oncology2024

Rapid identification of pediatric brain tumors with differential mobility spectrometry.

Ilkka Haapala, Anton Rauhameri, Meri Mäkelä, Markus Karjalainen, Anton Kontunen, Markus Mieskolainen, Hannu Haapasalo, Antti Roine, Niku Oksala, Antti Vehkaoja and 2 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

12 authors.

Ilkka Haapala *Department of Neurosurgery, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland.
Anton Rauhameri *Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Meri MäkeläTAYS Cancer Center, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland.
Markus KarjalainenTAYS Cancer Center, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland.
Anton KontunenFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Markus MieskolainenDepartment of Neurosurgery, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland.
Hannu HaapasaloTAYS Cancer Center, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland.
Antti RoineTAYS Cancer Center, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland.
Niku OksalaFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Antti VehkaojaFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Joonas HaapasaloDepartment of Neurosurgery, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland.
Kristiina NordforsFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Brain tumors are a major source of disease burden in pediatric population, with the most common tumor types being pilocytic astrocytoma, ependymoma and medulloblastoma. In every tumor entity, surgery is the cornerstone of treatment, but the importance of gross-total resection and the corresponding patient prognosis is highly variant. However, real-time identification of pediatric CNS malignancies based on the histology of the frozen sections alone is especially troublesome. We propose a novel method based on differential mobility spectrometry (DMS) analysis for rapid identification of pediatric brain tumors. Methods: We prospectively obtained tumor samples from 15 pediatric patients (5 pilocytic astrocytomas, 5 ependymomas and 5 medulloblastomas). The samples were cut into 36 smaller specimens that were analyzed with the DMS. Results: With linear discriminant analysis algorithm, a classification accuracy (CA) of 70% was reached. Additionally, a 75% CA was achieved in a pooled analysis of medulloblastoma vs. gliomas. Discussion: Our results show that the DMS is able to differentiate most common pediatric brain tumor samples, thus making it a promising additional instrument for real-time brain tumor diagnostics.

Indexed as

differential mobility spectrometryneuro-oncologypediatric brain tumorpediatric neuro-oncologyrapid diagnostics

Identifiers

PMID38746683
PMCPMC11091372

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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.