Evidence map›Paper›PMID 35326526›Full record

ReviewCancers2022

Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review.

Leon Jekel, Waverly R Brim, Marc von Reppert, Lawrence Staib, Gabriel Cassinelli Petersen, Sara Merkaj, Harry Subramanian, Tal Zeevi, Seyedmehdi Payabvash, Khaled Bousabarah and 10 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

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

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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

20 authors.

Leon JekelDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.ORCID 0000-0001-9954-3328
Waverly R BrimDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.
Marc von ReppertDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.ORCID 0000-0003-4623-7401
Lawrence StaibDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.ORCID 0000-0002-9516-5136
Gabriel Cassinelli PetersenDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.
Sara MerkajDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.ORCID 0000-0003-1147-4968
Harry SubramanianDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.
Tal ZeeviDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.
Seyedmehdi PayabvashDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.ORCID 0000-0003-4628-0370
Khaled BousabarahVisage Imaging GmbH, Lepsiusstraße 70, 12163 Berlin, Germany.
MingDe LinDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.
Jin CuiDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.ORCID 0000-0003-4638-6466
Alexandria BrackettHarvey Cushing/John Hay Whitney Medical Library, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
Amit MahajanDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.ORCID 0000-0002-6532-2209
Antonio OmuroDepartment of Neurology, Yale School of Medicine, 15 York St Ste LCI702, New Haven, CT 06510, USA.
Michele H JohnsonDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.
Veronica L ChiangDepartment of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA.
Ajay MalhotraDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.
Björn SchefflerDKFZ Division of Translational Neurooncology at the WTZ, German Cancer Consortium, DKTK Partner Site, University Hospital Essen, 45147 Essen, Germany.
Mariam S AboianDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.ORCID 0000-0002-4877-8271

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis.

Indexed as

artificial intelligencebrain metastasisglioblastomagliomamachine learningreporting quality assessmentsystematic review

Identifiers

PMID35326526
PMCPMC8946855

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.