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A dual space MRI radiomic network signature for risk stratification and subtyping of mild cognitive impairment.

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  1. [1] § STAR★Methods › Method details › Radiomic feature extraction ↔ Radiomics_R2SN.py, lines 8–25 · score 0.58 · Radiomic features, GLCM, GLDM, GLRLM, GLSZM, NGTDM

Paper

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The authors' code

Python · 160 lines · 4.9 KB · no license · 1 match

  1. import argparse
  2. import numpy as np
  3. import pandas as pd
  4. import SimpleITK as sitk
  5. from radiomics import featureextractor
  8. def make_extractor(bin_width=25.0, normalize=False, include_shape=True):
  9. settings = {"binWidth": bin_width}
  10. if normalize:
  11. settings["normalize"] = True
  13. ext = featureextractor.RadiomicsFeatureExtractor(**settings)
  14. ext.disableAllImageTypes()
  15. ext.enableImageTypeByName("Original")
  16. ext.disableAllFeatures()
  18. classes = ["firstorder", "glcm", "glrlm", "glszm", "gldm", "ngtdm"]
  19. if include_shape:
  20. classes.append("shape")
  22. for c in classes:
  23. ext.enableFeatureClassByName(c)
  25. return ext
  28. def clean_features(feats):
  29. out = {}
  30. for k, v in feats.items():
  31. if k.startswith("diagnostics_"):
  32. continue
  33. try:
  34. out[k] = float(v)
  35. except Exception:
  36. pass
  37. return out
  40. def same_space(a, b, tol=1e-6):
  41. return (
  42. a.GetSize() == b.GetSize()
  43. and np.allclose(a.GetSpacing(), b.GetSpacing(), atol=tol)
  44. and np.allclose(a.GetOrigin(), b.GetOrigin(), atol=tol)
  45. and np.allclose(a.GetDirection(), b.GetDirection(), atol=tol)
  46. )
  49. def get_binary_mask_from_label(label_img, label):
  50. mask = sitk.Equal(label_img, int(label))
  51. return sitk.Cast(mask, sitk.sitkUInt8)
  54. def extract_global_radiomics(image, global_mask, out_csv, bin_width=25.0, normalize=False):
  55. ext = make_extractor(bin_width=bin_width, normalize=normalize, include_shape=True)
  56. feats = clean_features(ext.execute(image, global_mask))
  57. df = pd.DataFrame([feats])
  58. df.to_csv(out_csv, index=False)
  59. return df
  62. def extract_regional_features(image, region_mask, bin_width=25.0, normalize=False, include_shape=False):
  63. ext = make_extractor(bin_width=bin_width, normalize=normalize, include_shape=include_shape)
  65. labels = np.unique(sitk.GetArrayViewFromImage(region_mask))
  66. labels = [int(x) for x in labels if x > 0]
  68. rows = []
  69. for label in labels:
  70. roi = get_binary_mask_from_label(region_mask, label)
  71. try:
  72. feats = clean_features(ext.execute(image, roi))
  73. feats["region_id"] = label
  74. rows.append(feats)
  75. except Exception:
  76. continue
  78. if not rows:
  79. raise RuntimeError("No valid regional features were extracted.")
  81. df = pd.DataFrame(rows).set_index("region_id").sort_index()
  82. df = df.apply(pd.to_numeric, errors="coerce")
  83. df = df.dropna(axis=1, how="all")
  84. df = df.fillna(df.median(numeric_only=True))
  85. return df
  88. def build_rrsn(regional_df, out_csv):
  89. x = regional_df.copy()
  91. # Z-score by feature
  92. x = (x - x.mean(axis=0)) / (x.std(axis=0, ddof=0) + 1e-12)
  93. x = x.fillna(0.0)
  95. if len(x) == 1:
  96. net = np.array([[1.0]])
  97. else:
  98. net = np.corrcoef(x.to_numpy(), rowvar=True)
  99. net = np.nan_to_num(net, nan=0.0)
  100. np.fill_diagonal(net, 1.0)
  102. net_df = pd.DataFrame(net, index=x.index, columns=x.index)
  103. net_df.index.name = "region_id"
  104. net_df.to_csv(out_csv)
  105. return net_df
  108. def main():
  109. parser = argparse.ArgumentParser()
  110. parser.add_argument("--image", required=True, help="Input image path")
  111. parser.add_argument("--region_mask", required=True, help="Region label mask path")
  112. parser.add_argument("--global_mask", default=None, help="Global ROI mask path")
  113. parser.add_argument("--out_radiomics", default="radiomics.csv", help="Output radiomics CSV")
  114. parser.add_argument("--out_rrsn", default="rrsn.csv", help="Output RRSN CSV")
  115. parser.add_argument("--bin_width", type=float, default=25.0, help="PyRadiomics binWidth")
  116. parser.add_argument("--normalize", action="store_true", help="Enable intensity normalization")
  117. parser.add_argument(
  118. "--include_shape_in_network",
  119. action="store_true",
  120. help="Include shape features in regional network",
  121. )
  122. args = parser.parse_args()
  124. image = sitk.ReadImage(args.image)
  125. region_mask = sitk.ReadImage(args.region_mask)
  127. if args.global_mask is None:
  128. global_mask = sitk.Cast(region_mask > 0, sitk.sitkUInt8)
  129. else:
  130. global_mask = sitk.ReadImage(args.global_mask)
  132. if not same_space(image, region_mask):
  133. raise ValueError("image and region_mask are not in the same space")
  134. if not same_space(image, global_mask):
  135. raise ValueError("image and global_mask are not in the same space")
  137. extract_global_radiomics(
  138. image=image,
  139. global_mask=global_mask,
  140. out_csv=args.out_radiomics,
  141. bin_width=args.bin_width,
  142. normalize=args.normalize,
  143. )
  145. regional_df = extract_regional_features(
  146. image=image,
  147. region_mask=region_mask,
  148. bin_width=args.bin_width,
  149. normalize=args.normalize,
  150. include_shape=args.include_shape_in_network,
  151. )
  153. build_rrsn(regional_df, args.out_rrsn)
  155. print(f"Saved: {args.out_radiomics}")
  156. print(f"Saved: {args.out_rrsn}")
  159. if __name__ == "__main__":
  160. main()

Radiomics_R2SN.py at commit be306cf, no license · at the source

Overview

Authors: Diaohan Xiong1, Mengjiao Liu1, Jiamei Xie1, Zefeng Liu1, Junping Wang1
ORCID iDs: Junping Wang
  1. Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
Institutions: Tianjin Medical University General Hospital (China); Tianjin Medical University (China)
Journal: iScience, volume 29, issue 7, article 116394
Dates: received 14 January 2026; accepted 28 May 2026; published online 12 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116394 · PMID 42325556 · PMCID PMC13276304 · OpenAlex W7164524996
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging
Keywords: applied sciences, biological sciences, cognitive neuroscience, computer science, health sciences, medical imaging, medical tests, medicine, natural sciences, network, neuroscience
Topic: Radiomics and Machine Learning in Medical Imaging (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Tianjin Municipal Science and Technology Program (25ZXWZSY00070)
Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

Mild cognitive impairment (MCI) is a clinically heterogeneous prodromal stage of Alzheimer’s disease (AD) in which accurate risk stratification could support earlier intervention and more efficient trial design. Using baseline T1-weighted MRI from the Alzheimer’s Disease Neuroimaging Initiative and the National Alzheimer’s Coordinating Center cohorts, we developed a dual-space anatomical radiomic-network signature that integrates regional radiomic features and radiomics similarity network metrics extracted in standard and native spaces. The signature improved prediction of MCI-to-AD conversion relative to regional volumetric and clinical models, achieved consistent external validation performance, and separated participants into high- and low-risk groups. The same feature set identified five imaging-defined MCI subtypes with distinct anatomical patterns, conversion risks, and clinical, APOE ε4, and cerebrospinal fluid biomarker profiles; fronto-parietal and parahippocampal-temporal subtypes showed the highest risk. These findings support structural MRI radiomic-network profiling as a complementary framework for individualized prognosis and cohort stratification in AD research.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

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

bearsyep/iscience_R2SN_radiomics

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: be306cf3c3599795f0a9a34a28e695184b86f4a0, 27 March 2026
Languages: Python (1)
Size: 1 file, 1 script
Software Heritage: not archived
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file), pandas (1 file), PyRadiomics (1 file), SimpleITK (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
1 file
At the source: github.com/bearsyep/iscience_R2SN_radiomics

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

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;
  • 1 script, 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 and code availability

This paper analyzes baseline T1-weighted structural MRI, demographic, clinical, genetic, cognitive, and biomarker data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the National Alzheimer’s Coordinating Center (NACC). ADNI and NACC data are available to qualified researchers upon application to the respective data repositories and are subject to their data-use agreements and governance requirements. The analysis code used in this study is publicly available at https://github.com/bearsyep/iscience_R2SN_radiomics. Additional information required to reanalyze the data reported in this paper is available from the lead contact upon reasonable request, subject to the restrictions of the source cohorts.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 11 keywords, 1 funder, 37 references.

Cite

This paper

Xiong, D., Liu, M., Xie, J., Liu, Z., & Wang, J. (2026). A dual space MRI radiomic network signature for risk stratification and subtyping of mild cognitive impairment. iScience, 29(7), 116394. https://doi.org/10.1016/j.isci.2026.116394

BibTeX

@article{xiong2026dual,
author = {Xiong, Diaohan and Liu, Mengjiao and Xie, Jiamei and Liu, Zefeng and Wang, Junping},
title = {{A dual space MRI radiomic network signature for risk stratification and subtyping of mild cognitive impairment}},
journal = {iScience},
year = {2026},
month = jun,
volume = {29},
number = {7},
pages = {116394},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116394},
url = {https://doi.org/10.1016/j.isci.2026.116394},
pmid = {42325556},
pmcid = {PMC13276304}
}

RIS

TY - JOUR
AU - Xiong, Diaohan
AU - Liu, Mengjiao
AU - Xie, Jiamei
AU - Liu, Zefeng
AU - Wang, Junping
TI - A dual space MRI radiomic network signature for risk stratification and subtyping of mild cognitive impairment
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/06/12
VL - 29
IS - 7
SP - 116394
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116394
UR - https://doi.org/10.1016/j.isci.2026.116394
LA - en
ER -

CSL-JSON

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"author": [
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"family": "Xiong",
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"date-parts": [
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