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Exploring the value of peritumoral brain zone for classification of two malignant brain tumors based on the MRI interpretable models.

Overview

Authors: Endong Zhao1, Yun-Feng Yang2,3, Hao Zhang1, Yuan-Yuan Yang2,3, Yutong Shi4, Beibei Li5, Xuelin Song1, Shiyun Lou5, Chao Yang1
  1. Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116000 China
  2. Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083 China
  3. Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, 100049 China
  4. Department of Neurology, Tianjin Medical University General Hospital, Tianjin, 300070 China
  5. Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116000 China
Institutions: Dalian Medical University (China); Second Affiliated Hospital of Dalian Medical University (China); Chinese Academy of Sciences (China); Shanghai Institute of Technical Physics (China); University of Chinese Academy of Sciences (China); Tianjin Medical University General Hospital (China); Tianjin Medical University (China); First Affiliated Hospital of Dalian Medical University (China)
Journal: BMC medical imaging, volume 26, issue 1, article 433
Dates: received 24 November 2024; accepted 11 May 2026; published online 26 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12880-026-02434-9 · PMID 42192376 · PMCID PMC13540937 · OpenAlex W7162413597
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, fMRI & imaging
Keywords: Peritumoral brain zone, Radiomics, Interpretable model, Primary central nervous system lymphoma, Glioblastoma
MeSH: Brain Neoplasms*, Glioblastoma*, Image Interpretation, Computer-Assisted*, Lymphoma*, Magnetic Resonance Imaging*, Adult, Aged, Female, Humans, Male, Middle Aged, Radiomics, Retrospective Studies, ROC Curve, Sensitivity and Specificity (* major topic)
Topic: Brain Tumor Detection and Classification (Neurology, Neuroscience), according to OpenAlex
Funding: The Medical-Engineering Joint Fund between the First Affiliated Hospital of Dalian Medical University and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. ((Grant No. DMU-1&DICP UN202403), (Grant No. DMU-1&DICP UN202403))
Citations: not cited yet (Europe PMC); 45 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

OnekeyAI-Platform

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: github.com/OnekeyAI-Platform

The paper's code and data availability statement is in the Data section.

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: OnekeyAI-Platform
  • it says that the data are available on request

Read it in the paper: doi.org/10.1186/s12880-026-02434-9.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 15 MeSH terms, 1 funder, 42 references.

Cite

This paper

Zhao, E., Yang, Y.-F., Zhang, H., Yang, Y.-Y., Shi, Y., Li, B., Song, X., Lou, S., & Yang, C. (2026). Exploring the value of peritumoral brain zone for classification of two malignant brain tumors based on the MRI interpretable models. BMC medical imaging, 26(1), 433. https://doi.org/10.1186/s12880-026-02434-9

BibTeX

@article{zhao2026exploring,
author = {Zhao, Endong and Yang, Yun-Feng and Zhang, Hao and Yang, Yuan-Yuan and Shi, Yutong and Li, Beibei and Song, Xuelin and Lou, Shiyun and Yang, Chao},
title = {{Exploring the value of peritumoral brain zone for classification of two malignant brain tumors based on the MRI interpretable models}},
journal = {BMC medical imaging},
year = {2026},
month = may,
volume = {26},
number = {1},
pages = {433},
publisher = {BMC},
issn = {1471-2342},
doi = {10.1186/s12880-026-02434-9},
url = {https://doi.org/10.1186/s12880-026-02434-9},
pmid = {42192376},
pmcid = {PMC13540937}
}

RIS

TY - JOUR
AU - Zhao, Endong
AU - Yang, Yun-Feng
AU - Zhang, Hao
AU - Yang, Yuan-Yuan
AU - Shi, Yutong
AU - Li, Beibei
AU - Song, Xuelin
AU - Lou, Shiyun
AU - Yang, Chao
TI - Exploring the value of peritumoral brain zone for classification of two malignant brain tumors based on the MRI interpretable models
T2 - BMC medical imaging
J2 - BMC Med Imaging
PY - 2026
DA - 2026/05/26
VL - 26
IS - 1
SP - 433
SN - 1471-2342
PB - BMC
DO - 10.1186/s12880-026-02434-9
UR - https://doi.org/10.1186/s12880-026-02434-9
LA - en
ER -

CSL-JSON

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