OSCR

The explainability of radiomic-based machine learning models for brain glioma grading on amide proton transfer-weighted images.

Code ↔ Paper

1 match between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 1 match · it ties a paragraph to a whole file without given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Model explainability analysis ↔ shap_pi.py, the whole file · a weak match · score 0.55 · Permutation importance, SHAP, kernel, accuracy, models · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 44 lines · 1.7 KB · BSD-3-Clause · 1 match

  1. import pandas as pd
  2. import numpy as np
  3. import joblib
  4. import shap
  5. import matplotlib.pyplot as plt
  8. feature_Filepath = r''
  9. label_Filepath = r''
  10. model_path = r'l'
  11. data_path = r''
  13. feature_data = pd.read_csv(feature_Filepath)
  14. label_data = pd.read_csv(label_Filepath)
  15. feature_names = feature_data.columns.values.tolist()
  16. model = joblib.load(filename = model_path)
  17. X = joblib.load(filename = data_path)
  20. X_summary = shap.sample(X, 10)
  21. explainer = shap.KernelExplainer(model.predict_proba, X_summary)
  22. shap_values = explainer.shap_values(X)
  24. shap.force_plot(explainer.expected_value[1], shap_values[..., 1], X)
  25. shap.summary_plot(shap_values[..., 0], X, feature_names)
  26. ex = shap.Explainer(model.predict, X_summary)
  27. shap_values1 = ex(X)
  28. shap.plots.beeswarm(shap_values1, max_display=25)
  29. shap.plots.beeswarm(shap_values1, order=shap_values1.abs.max(0),show=True)
  30. shap.plots.heatmap(shap_values1[:100])
  31. shap.plots.waterfall(shap_values1[0], max_display=20)
  32. shap.summary_plot(shap_values1, X)
  33. shap.plots.bar(shap_values1, max_display=30)
  34. shap.plots.bar(shap_values1[0])
  35. clustering = shap.utils.hclust(X, label_data)
  36. shap.plots.bar(shap_values1, clustering=clustering)
  37. shap.plots.bar(shap_values1, clustering=clustering, clustering_cutoff=0.8, max_display=50)
  40. from alibi.explainers import PermutationImportance, plot_permutation_importance
  41. explainer_permutationimportance = PermutationImportance(predictor=model.predict, score_fns=['accuracy', 'f1'], feature_names=feature_names, verbose=True)
  42. explainer_pi = explainer_permutationimportance.explain(X=X, y=label_data.values)
  43. plot_permutation_importance(explainer_pi, n_cols=2, fig_kw={'figwidth': 14, 'figheight': 6});

shap_pi.py at commit 1946f19, under BSD-3-Clause · at the source

Overview

Authors: Xuan Gao1, Jing Wang1
  1. Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
Institutions: Wuhan Union Hospital (China); Huazhong University of Science and Technology (China)
Journal: Scientific reports, volume 16, issue 1, article 14539
Dates: received 9 September 2025; accepted 16 March 2026; published online 22 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-44963-x · PMID 41866415 · PMCID PMC13153237 · OpenAlex W7140040882
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), other condition (population)
Methods: Statistics, Machine learning, Connectivity
Keywords: Glioma, Grading, Machine learning, Explainability, Cancer, Computational biology and bioinformatics, Neurology, Neuroscience, Oncology
MeSH: Brain Neoplasms*, Glioma*, Machine Learning*, Magnetic Resonance Imaging*, Adult, Amides, Bayes Theorem, Female, Humans, Male, Middle Aged, Neoplasm Grading, Protons, Radiomics, Random Forest (* major topic)
Topic: Radiomics and Machine Learning in Medical Imaging (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (Grant No.82371945)
Citations: not cited yet (Europe PMC); 19 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

gitee.com/aaasss0636/xai

License: BSD-3-Clause
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 1946f196f0f3ad01c851a4b23ae4bb5664206968, 12 March 2026
Languages: Python (5)
Size: 8 files, 5 scripts
Software Heritage: not archived
Found in: the text, “Delineation of tumoral regions and extraction of”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (5 files), NumPy (3 files), SimpleITK (2 files), Matplotlib (1 file), PyRadiomics (1 file), SHAP (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
7 files
At the source: gitee.com/aaasss0636/xai

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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;
  • 5 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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.

Data availability statement

The paper has a 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 says that the data are available on request

Read it in the paper: doi.org/10.1038/s41598-026-44963-x.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 9 keywords, 15 MeSH terms, 1 funder, 16 references.

Cite

This paper

Gao, X., & Wang, J. (2026). The explainability of radiomic-based machine learning models for brain glioma grading on amide proton transfer-weighted images. Scientific reports, 16(1), 14539. https://doi.org/10.1038/s41598-026-44963-x

BibTeX

@article{gao2026explainability,
author = {Gao, Xuan and Wang, Jing},
title = {{The explainability of radiomic-based machine learning models for brain glioma grading on amide proton transfer-weighted images}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {14539},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-44963-x},
url = {https://doi.org/10.1038/s41598-026-44963-x},
pmid = {41866415},
pmcid = {PMC13153237}
}

RIS

TY - JOUR
AU - Gao, Xuan
AU - Wang, Jing
TI - The explainability of radiomic-based machine learning models for brain glioma grading on amide proton transfer-weighted images
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/03/22
VL - 16
IS - 1
SP - 14539
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-44963-x
UR - https://doi.org/10.1038/s41598-026-44963-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-44963-x",
"type": "article-journal",
"title": "The explainability of radiomic-based machine learning models for brain glioma grading on amide proton transfer-weighted images",
"container-title": "Scientific reports",
"author": [
{
"family": "Gao",
"given": "Xuan"
},
{
"family": "Wang",
"given": "Jing"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "14539",
"DOI": "10.1038/s41598-026-44963-x",
"PMID": "41866415",
"PMCID": "PMC13153237",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-44963-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
22
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1093/noajnl/vdag132 [code]
Spherical radiomics for radiogenomic assessment of glioblastoma heterogeneity.
Journal: Neuro-oncology advances
In common: PyRadiomics, SHAP, SimpleITK, 3 other tools, structural MRI / diffusion, other condition, 1 reference
[2] doi:10.1111/acel.70656 [code]
Deep Learning Predicts Hematopoietic Stem Cell Aging From 3D Chromatin Images.
Journal: Aging cell
In common: PyRadiomics, SHAP, SimpleITK, 3 other tools, 1 reference
[3] doi:10.1186/s12883-025-04420-0 [code]
Phenotypic stratification of Low-grade Glioma using multimodal MRI via outcome-weighted integrative clustering.
Journal: BMC neurology
In common: PyRadiomics, SHAP, SimpleITK, 3 other tools, structural MRI / diffusion, other condition
[4] doi:10.1186/s12883-025-04445-5 [code]
Preoperative prediction of brain invasion in meningiomas: a comparison of diffusion kurtosis imaging and diffusion tensor imaging.
Journal: BMC neurology
In common: PyRadiomics, SHAP, SimpleITK, 3 other tools, structural MRI / diffusion, other condition
[5] doi:10.3389/fnins.2023.1320296 [code]
Multiple diffusion metrics in differentiating solid glioma from brain inflammation.
Journal: Frontiers in neuroscience
In common: PyRadiomics, SHAP, SimpleITK, 3 other tools, structural MRI / diffusion, other condition
[6] doi:10.1155/2022/4667117 [code]
Multimodal MRI-Based Radiomic Nomogram for the Early Differentiation of Recurrence and Pseudoprogression of High-Grade Glioma.
Journal: BioMed research international
In common: PyRadiomics, SHAP, SimpleITK, 3 other tools, structural MRI / diffusion, other condition
[7] doi:10.3389/fonc.2026.1816015 [code]
Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain metastases.
Journal: Frontiers in oncology
In common: PyRadiomics, SHAP, SimpleITK, 3 other tools, structural MRI / diffusion, other condition
[8] doi:10.3389/fneur.2024.1474461 [code]
Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases.
Journal: Frontiers in neurology
In common: PyRadiomics, SHAP, SimpleITK, 3 other tools, structural MRI / diffusion, other condition
[9] doi:10.1016/j.nicl.2024.103707 [code]
Prediction of methylphenidate treatment response for ADHD using conventional and radiomics T1 and DTI features: Secondary analysis of a randomized clinical trial.
Journal: NeuroImage. Clinical
In common: PyRadiomics, SHAP, SimpleITK, 3 other tools, structural MRI / diffusion
[10] doi:10.3389/fpsyt.2024.1448145 [code]
Individualized multi-modal MRI biomarkers predict 1-year clinical outcome in first-episode drug-naïve schizophrenia patients.
Journal: Frontiers in psychiatry
In common: PyRadiomics, SHAP, SimpleITK, 3 other tools, structural MRI / diffusion

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Add the badge to the code's README

OSCR: the badge of this paper

If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown
<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.1038/s41598-026-44963-x.svg)](https://openscicode.org/badge/doi/10.1038/s41598-026-44963-x)
<!-- oscr-badge:end -->
reStructuredText
.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.1038/s41598-026-44963-x.svg
   :target: https://openscicode.org/badge/doi/10.1038/s41598-026-44963-x
   :alt: OSCR

.. oscr-badge:end
HTML
<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.1038/s41598-026-44963-x"><img src="https://openscicode.org/badge/doi/10.1038/s41598-026-44963-x.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.