Evidence map›Paper›PMID 38672556›Full record

ArticleCancers2024

Human-Level Differentiation of Medulloblastoma from Pilocytic Astrocytoma: A Real-World Multicenter Pilot Study.

Benedikt Wiestler, Brigitte Bison, Lars Behrens, Stefanie Tüchert, Marie Metz, Michael Griessmair, Marcus Jakob, Paul-Gerhardt Schlegel, Vera Binder, Irene von Luettichau and 4 more

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.8field-weighted citation impact, top 16% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
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

14 authors at 5 institutions in 2 countries.

Benedikt WiestlerDepartment of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany.ORCID 0000-0002-2963-7772
Brigitte BisonStudy Groups on CNS Tumors Within the Bavarian Cancer Research Center (BZKF).ORCID 0000-0001-5849-8671
Lars BehrensStudy Groups on CNS Tumors Within the Bavarian Cancer Research Center (BZKF).
Stefanie TüchertStudy Groups on CNS Tumors Within the Bavarian Cancer Research Center (BZKF).ORCID 0000-0002-7952-1380
Marie MetzDepartment of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany.
Michael GriessmairDepartment of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany.
Marcus JakobStudy Groups on CNS Tumors Within the Bavarian Cancer Research Center (BZKF).
Paul-Gerhardt SchlegelStudy Groups on CNS Tumors Within the Bavarian Cancer Research Center (BZKF).
Vera BinderStudy Groups on CNS Tumors Within the Bavarian Cancer Research Center (BZKF).
Irene von LuettichauStudy Groups on CNS Tumors Within the Bavarian Cancer Research Center (BZKF).
Markus MetzlerStudy Groups on CNS Tumors Within the Bavarian Cancer Research Center (BZKF).ORCID 0000-0002-4523-1676
Pascal JohannStudy Groups on CNS Tumors Within the Bavarian Cancer Research Center (BZKF).
Peter HauStudy Groups on CNS Tumors Within the Bavarian Cancer Research Center (BZKF).ORCID 0000-0003-3894-5053
Michael FrühwaldStudy Groups on CNS Tumors Within the Bavarian Cancer Research Center (BZKF).ORCID 0000-0002-8237-1854
Cancer Research Center · USUniversity Hospital Augsburg · DEUniversitätsklinikum Erlangen · DEUniversitätsklinikum Würzburg · DEUniversity Hospital Regensburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medulloblastoma and pilocytic astrocytoma are the two most common pediatric brain tumors with overlapping imaging features. In this proof-of-concept study, we investigated using a deep learning classifier trained on a multicenter data set to differentiate these tumor types. We developed a patch-based 3D-DenseNet classifier, utilizing automated tumor segmentation. Given the heterogeneity of imaging data (and available sequences), we used all individually available preoperative imaging sequences to make the model robust to varying input. We compared the classifier to diagnostic assessments by five readers with varying experience in pediatric brain tumors. Overall, we included 195 preoperative MRIs from children with medulloblastoma (

Indexed as

artificial intelligencebraindeep learningMRIpediatric brain tumor

Identifiers

PMID38672556
PMCPMC11048511
OpenAlexW4394768845

What OpenQuestion holds

Textmetadata
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.