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AJNR Awards, New Junior Editors, and more. Read the latest AJNR updates

Research ArticleBRAIN TUMOR IMAGING

Image-Based Search in Radiology: Identification of Brain Tumor Subtypes within Databases Using MRI-Based Radiomic Features

Marc von Reppert, Saahil Chadha, Klara Willms, Arman Avesta, Nazanin Maleki, Tal Zeevi, Jan Lost, Niklas Tillmanns, Leon Jekel, Sara Merkaj, MingDe Lin, Karl-Titus Hoffmann, Sanjay Aneja, Mariam S. Aboian and for the IBSR Consortium
American Journal of Neuroradiology June 2025, DOI: https://doi.org/10.3174/ajnr.A8805
Marc von Reppert
aFrom the University of Leipzig (M.v.R., K.W., K.-T.H., S.A.), Leipzig, Germany
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  • ORCID record for Marc von Reppert
Saahil Chadha
bDepartment of Radiology and Biomedical Imaging (S.C., A.A.), Yale School of Medicine, New Haven, Connecticut
cCenter for Outcomes Research and Evaluation (S.C.), Yale School of Medicine, New Haven, Connecticut
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Klara Willms
aFrom the University of Leipzig (M.v.R., K.W., K.-T.H., S.A.), Leipzig, Germany
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Arman Avesta
bDepartment of Radiology and Biomedical Imaging (S.C., A.A.), Yale School of Medicine, New Haven, Connecticut
dDepartment of Radiology (A.A.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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Nazanin Maleki
eDepartment of Radiology (N.M., M.S.A.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Tal Zeevi
fCenter for Translational Imaging Analysis and Machine Learning, Department of Radiology and Biomedical Imaging (T.Z.), Yale School of Medicine, New Haven, Connecticut
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Jan Lost
gDepartment of Neurosurgery (J.L.), Heinrich-Heine-University, Duesseldorf, Germany
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Niklas Tillmanns
hDepartment of Diagnostic and Interventional Radiology, Medical Faculty (N.T.), University Dusseldorf, Dusseldorf, Germany
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Leon Jekel
iUniversity of Duisburg-Essen (L.J.), Essen, Germany
jDKFZ Division of Translational Neuro-oncology at the WTZ, German Cancer Consortium, DKTK Partner Site (L.J.), University Hospital Essen, Essen, Germany
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Sara Merkaj
kUniversity of Ulm (S.M.), Ulm, Germany
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MingDe Lin
lVisage Imaging, Inc. (M.L.), San Diego, California
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Karl-Titus Hoffmann
aFrom the University of Leipzig (M.v.R., K.W., K.-T.H., S.A.), Leipzig, Germany
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Sanjay Aneja
aFrom the University of Leipzig (M.v.R., K.W., K.-T.H., S.A.), Leipzig, Germany
mDepartment of Therapeutic Radiology (S.A.), Yale School of Medicine, New Haven, Connecticut
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Mariam S. Aboian
eDepartment of Radiology (N.M., M.S.A.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Abstract

BACKGROUND AND PURPOSE: Existing neuroradiology reference materials do not cover the full range of primary brain tumor presentations, and text-based medical image search engines are limited by the lack of consistent structure in radiology reports. To address this, an image-based search approach is introduced here, leveraging an institutional database to find reference MRIs visually similar to presented query cases.

MATERIALS AND METHODS: Two hundred ninety-five patients (mean age and standard deviation, 51 ± 20 years) with primary brain tumors who underwent surgical and/or radiotherapeutic treatment between 2000 and 2021 were included in this retrospective study. Semiautomated convolutional neural network–based tumor segmentation was performed, and radiomic features were extracted. The data set was split into reference and query subsets, and dimensionality reduction was applied to cluster reference cases. Radiomic features extracted from each query case were projected onto the clustered reference cases, and nearest neighbors were retrieved. Retrieval performance was evaluated by using mean average precision at k, and the best-performing dimensionality reduction technique was identified. Expert readers independently rated visual similarity by using a 5-point Likert scale.

RESULTS: t-Distributed stochastic neighbor embedding with 6 components was the highest-performing dimensionality reduction technique, with mean average precision at 5 ranging from 78%–100% by tumor type. The top 5 retrieved reference cases showed high visual similarity Likert scores with corresponding query cases (76% ‘similar’ or ‘very similar’).

CONCLUSIONS: We introduce an image-based search method for exploring historical MR images of primary brain tumors and fetching reference cases closely resembling queried ones. Assessment involving comparison of tumor types and visual similarity Likert scoring by expert neuroradiologists validates the effectiveness of this method.

ABBREVIATIONS:

A/O
astrocytoma and oligodendroglioma WHO CNS grades 2–3
CNN
convolutional neural network
G/A
glioblastoma and astrocytoma WHO CNS grade 4
ICC
intraclass correlation coefficient
mAP@k
mean average precision at k
MEN
meningioma
PA
pilocytic astrocytoma
PCA
principal component analysis
PHATE
potential of heat-diffusion for affinity-based trajectory embedding
t-SNE
t-distributed stochastic neighbor embedding
T1CE
T1 contrast-enhanced
UMAP
uniform manifold approximation and projection
WHO
World Health Organization

Footnotes

  • Marc von Reppert and Saahil Chadha contributed equally.

  • A complete list of the authors in the IBSR Consortium appears at the end of this article.

  • This work was supported in part by a KL2 TR00186 grant from the National Center for Advancing Translational Sciences.

  • Disclosure forms provided by the authors are available with the full text and PDF of this article at www.ajnr.org.

  • © 2025 by American Journal of Neuroradiology
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Cite this article
Marc von Reppert, Saahil Chadha, Klara Willms, Arman Avesta, Nazanin Maleki, Tal Zeevi, Jan Lost, Niklas Tillmanns, Leon Jekel, Sara Merkaj, MingDe Lin, Karl-Titus Hoffmann, Sanjay Aneja, Mariam S. Aboian, for the IBSR Consortium
Image-Based Search in Radiology: Identification of Brain Tumor Subtypes within Databases Using MRI-Based Radiomic Features
American Journal of Neuroradiology Jun 2025, DOI: 10.3174/ajnr.A8805

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Brain Tumor Subtyping Image-Based search via MRI
Marc von Reppert, Saahil Chadha, Klara Willms, Arman Avesta, Nazanin Maleki, Tal Zeevi, Jan Lost, Niklas Tillmanns, Leon Jekel, Sara Merkaj, MingDe Lin, Karl-Titus Hoffmann, Sanjay Aneja, Mariam S. Aboian, for the IBSR Consortium
American Journal of Neuroradiology Jun 2025, DOI: 10.3174/ajnr.A8805
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Print ISSN: 0195-6108 Online ISSN: 1936-959X

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