{"doi":"10.1007/s11060-025-05370-w","title":"Free water elimination tractometry reveals local and remote white matter alterations in diffuse gliomas","abstract":"PURPOSE: To apply free water elimination (FWE) tractometry to a real-world clinical imaging dataset to quantify pathology-specific patterns of white matter involvement and peritumoral tissue alterations in diffuse gliomas. METHODS: The University of California San Francisco Preoperative Diffuse Glioma MRI dataset was analyzed using FWE tractometry. Twenty major white matter tracts were reconstructed and each divided into 100 equidistant nodes. Direct tumor involvement was quantified across enhancing tumor, necrotic core, and edema regions. Remote white matter tissue properties were assessed through hemispheric asymmetry analysis of free water-corrected fractional anisotropy (FW-FA), mean diffusivity (FW-MD), and free water fraction (FWF) in non-tumor involved regions at standardized distances from radiological tumor margins. RESULTS: 459 patients with unilateral glioma were included (361 glioblastoma, 87 astrocytoma, 11 oligodendroglioma). Glioblastoma demonstrated greater direct white matter involvement in enhancing tumor and necrotic core compared to astrocytoma and oligodendroglioma (q < 0.001, q = 0.01, respectively). Beyond radiological tumor margins, glioblastoma and astrocytoma exhibited decreased FW-FA, while oligodendroglioma showed increased FW-FA (q = 0.008, q = 0.04, respectively). Distance-based analysis revealed that this effect was most prominent in the proximal peritumoral region and diminished with increasing distance from tumor margins. CONCLUSION: Using FWE tractometry on a large clinical repository, we identified distinct pathology-specific patterns of white matter alteration. Glioblastoma showed extensive direct involvement and peritumoral microstructural changes, while oligodendroglioma demonstrated relatively preserved white matter architecture near tumor margins. These patterns reflect expected biological differences and provide a reproducible framework for characterizing extent of white matter involvement, with potential applications in presurgical planning and understanding recurrence patterns.","journal":"Journal of Neuro-Oncology","year":2025,"id":584817,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":0.0,"corpus_rank":10062,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.681,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":25.0,"fair_percentile":40.966065423417916,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1475762,"name":"Kelly Chang","orcid":"0000-0002-4136-6688","position":1,"is_corresponding":false},{"id":383190,"name":"Marc Jaskir","orcid":"0000-0002-7458-4876","position":2,"is_corresponding":false},{"id":1161190,"name":"Kathryn A. Davis","orcid":"0000-0001-5830-6676","position":3,"is_corresponding":false},{"id":240420,"name":"Joel M. Stein","orcid":"0000-0002-0741-1780","position":4,"is_corresponding":false},{"id":644407,"name":"Nishant Sinha","orcid":"0000-0002-2090-4889","position":5,"is_corresponding":false},{"id":278421,"name":"Richard E. Phillips","orcid":"0000-0003-1531-3096","position":6,"is_corresponding":false},{"id":769028,"name":"Manuel Ferreira","orcid":"0000-0002-1720-7441","position":7,"is_corresponding":false},{"id":374967,"name":"Thomas J. Grabowski","orcid":"0000-0002-7425-6610","position":8,"is_corresponding":false},{"id":262625,"name":"Ariel Rokem","orcid":"0000-0003-0679-1985","position":9,"is_corresponding":false},{"id":1404181,"name":"Daniel J. Zhou","orcid":"0009-0008-7555-7918","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-19T02:59:16.166424Z","pmid":"41372668","pmcid":"PMC12696142","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":22.2222,"fair_a":25.0,"fair_i":0.0,"fair_r":33.3333,"fair_zscore":-0.374,"fair_rationale":{"fair_score":25.0,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":22.22,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"Results of tractometry analysis are available at: [https://figshare.com/articles/dataset/Free-water_elimination_tractometry_from_the_The_University_of_California_San_Francisco_Preoperative_Diffuse_Glioma_MRI_dataset/30402736]","grounded":false,"rationale":"The text provides a figshare URL rather than a DOI or other persistent-identifier scheme string for the study's own data. [downgraded to 'no' — no verifiable quote from the paper]","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"Results of tractometry analysis are available at: [https://figshare.com/articles/dataset/Free-water_elimination_tractometry_from_the_The_University_of_California_San_Francisco_Preoperative_Diffuse_Glioma_MRI_dataset/30402736]","grounded":false,"rationale":"The data are deposited in figshare, a repository listed in re3data. 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[downgraded to 'no' — no verifiable quote from the paper]","anchors":["FORCE11 Joint Declaration of Data Citation Principles (2014) — data should be cited as a first-","RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes","FsF-F3-01M — F-UJI: 'Metadata includes the identifier of the data it describes'"],"scored":true,"signal":null}]},"A":{"name":"Accessible","score":25.0,"criteria":[{"key":"a_data_openly_accessible","label":"Access route free of preconditions","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"Results of tractometry analysis are available at: [https://figshare.com/articles/dataset/Free-water_elimination_tractometry_from_the_The_University_of_California_San_Francisco_Preoperative_Diffuse_Glioma_MRI_dataset/30402736]","grounded":false,"rationale":"The data are offered via a public figshare URL with no stated precondition. 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[downgraded to 'no' — no verifiable quote from the paper]","anchors":["FsF-A1-01M — F-UJI: 'Metadata contains access level and access conditions of the data'","RDA-A1-01M — metadata contains information to enable the user to get access to the data","COAR Controlled Vocabularies — Access Rights v1.0 (open / embargoed / restricted / metadata-onl"],"scored":false,"signal":null},{"key":"a_controlled_access_for_sensitive","label":"Gatekeeper for sensitive data","kind":"llm","weight":0.5,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"The study's own data derived from a public dataset are not sensitive, and no gatekeeper is named for their access.","anchors":["NIH Genomic Data Sharing Policy (NOT-OD-14-124) — controlled-access via a Data Access Committee","RDA-A1.2-01D — 'Data is accessible through an access protocol that supports authentication and ","NIH DMS Policy Element 5 (NOT-OD-21-014) — Access, Distribution, or Reuse Considerations (conse"],"scored":false,"signal":null},{"key":"a_timeline_retention","label":"Availability timing & retention","kind":"llm","weight":0.5,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"The paper states only that the data are 'available at' a URL, with no mention of retention period or persistence commitment.","anchors":["NIH DMS Plan Element 4 (NOT-OD-21-014) — Data Preservation, Access, and Associated Timelines","NSTC Desirable Characteristics (2022), Organizational Infrastructure: 'Retention Policy'","RDA-A2-01M — 'Metadata is guaranteed to remain available after data is no longer available'"],"scored":false,"signal":null}]},"I":{"name":"Interoperable","score":0.0,"criteria":[{"key":"i_open_nonproprietary_format","label":"Open file format","kind":"llm","weight":1.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"The paper does not name the file format of the released tractometry results.","anchors":["FsF-R1.3-02D — F-UJI: 'Data is available in a file format recommended by the target research co","RDA-R1.3-02D — data is expressed in a machine-understandable community standard","RDA-I1-01D — data uses a knowledge representation expressed in a standardised format"],"scored":true,"signal":null},{"key":"i_community_standard_vocabulary","label":"Community standard / vocabulary","kind":"llm","weight":1.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No community-standard checklist, metadata schema, or ontology is named as applied to the data.","anchors":["RDA-R1.3-01M — 'Metadata complies with a community standard' (priority Essential)","RDA-R1.3-01D — 'Data complies with a community standard'","RDA-I2-01M — '(Meta)data use vocabularies that follow FAIR principles'"],"scored":false,"signal":null},{"key":"i_qualified_references","label":"Identifiers for the resources the data depend on","kind":"llm","weight":0.5,"fraction":0.0,"verdict":"no","evidence":"The UCSF-PDGM dataset analyzed in this study is publicly available through The Cancer Imaging Archive (TCIA) at (https://doi.org/10.7937/TCIA.2020.C1GQ4842)","grounded":false,"rationale":"The paper provides a DOI for the source dataset (UCSF-PDGM) that the study's data depend on. [downgraded to 'no' — no verifiable quote from the paper]","anchors":["RDA-I3-01M — '(meta)data include references to other (meta)data'","RDA-I3-03M — 'metadata includes qualified references to other metadata'","FsF-I3-01M — F-UJI: 'Metadata includes links between the data and its related entities'"],"scored":false,"signal":null}]},"R":{"name":"Reusable","score":33.33,"criteria":[{"key":"r_reuse_license","label":"Reuse licence","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No license for the data is stated in the text; the CC-BY license applies to the article only.","anchors":["RDA-R1.1-01M — 'Metadata includes information about the licence under which the data can be reu","RDA-R1.1-02M — 'Metadata refers to a standard reuse licence'","RDA-R1.1-03M — 'Metadata refers to a machine-understandable reuse licence'"],"scored":true,"signal":null},{"key":"r_provenance_methods","label":"Provenance of the data","kind":"llm","weight":1.0,"fraction":1.0,"verdict":"yes","evidence":"Tractography reconstruction, tractometry, and FWE analysis were performed as previously described, using the open-source pyAFQ software version 2.1, which relies on techniques implemented in the DIPY software","grounded":true,"rationale":"The paper names specific software and version (pyAFQ 2.1, DIPY) used to produce the data.","anchors":["RDA-R1.2-01M — 'Metadata includes provenance information according to community- specific standa","FsF-R1.2-01M — F-UJI: 'Metadata includes provenance information about data creation or generati","W3C PROV-O (W3C Recommendation, 2013) — the entity/activity/agent model of provenance"],"scored":false,"signal":null},{"key":"r_documentation_codebook","label":"Documentation / codebook","kind":"llm","weight":1.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No README, data dictionary, or codebook is mentioned as accompanying the deposited data.","anchors":["RDA-R1-01M — '(Meta)data are richly described with a plurality of accurate and relevant attribu","FsF-R1-01MD — F-UJI: 'Metadata specifies the content of the data'","NIH DMS Policy Element 3 (NOT-OD-21-014) — Standards (documentation and metadata to accompany t"],"scored":false,"signal":null},{"key":"r_versioning","label":"Snapshot identified","kind":"llm","weight":0.5,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No version token or date is given for the tractometry dataset; only software versions are mentioned.","anchors":["DataCite Metadata Schema 4.6 — the 'Version' property","RDA-R1.2-01M — provenance information (which version was used is provenance)","NSTC Desirable Characteristics of Data Repositories (2022) — 'Provenance', 'Retention Policy'"],"scored":true,"signal":null},{"key":"x_code_availability","label":"Analysis code available","kind":"llm","weight":1.0,"fraction":0.5,"verdict":"partial","evidence":"Code to reproduce the statistical analysis and visualizations in the paper is available at [https://github.com/nrdg/fwe_tractometry_glioma]","grounded":false,"rationale":"The paper provides a GitHub repository URL for the code, which is a machine-resolvable locator. 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For neuroimaging data, deposit in OpenNeuro or NeuroVault.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"Results of tractometry analysis are available at: [https://figshare.com/articles/dataset/Free-water_elimination_tractometry_from_the_The_University_of_California_San_Francisco_Preoperative_Diffuse_Glioma_MRI_dataset/30402736]","why":"The data are deposited in figshare, a repository listed in re3data. [downgraded to 'partial' — no verifiable quote from the paper]","gain":8.33,"priority":"essential","scored":true},{"key":"a_data_openly_accessible","dimension":"A","label":"Access route free of preconditions","action":"Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. 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Archive the analysis code in a versioned repository (GitHub + a Zenodo release DOI).","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"Code to reproduce the statistical analysis and visualizations in the paper is available at [https://github.com/nrdg/fwe_tractometry_glioma]","why":"The paper provides a GitHub repository URL for the code, which is a machine-resolvable locator. [downgraded to 'partial' — no verifiable quote from the paper]","gain":4.17,"priority":"important","scored":true},{"key":"r_versioning","dimension":"R","label":"Snapshot identified","action":"Version the deposit and cite the exact version analysed (a version-specific DOI, or an accession with its version suffix). 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An author-gated dataset dies with the author's email address, and 'on reasonable request' has been shown repeatedly not to yield data.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"The study's own data derived from a public dataset are not sensitive, and no gatekeeper is named for their access.","gain":0.0,"priority":"useful","scored":false},{"key":"i_qualified_references","dimension":"I","label":"Identifiers for the resources the data depend on","action":"Cite by identifier every resource the data depend on — the source datasets' accessions, the reference build (GRCh38 / GCA_000001405.28), the cohort application number, the code DOI — and register those relations on the dataset record (IsDerivedFrom, IsSupplementTo). A name is not a link: it cannot be resolved, versioned, or followed by a machine.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"The UCSF-PDGM dataset analyzed in this study is publicly available through The Cancer Imaging Archive (TCIA) at (https://doi.org/10.7937/TCIA.2020.C1GQ4842)","why":"The paper provides a DOI for the source dataset (UCSF-PDGM) that the study's data depend on. [downgraded to 'no' — no verifiable quote from the paper]","gain":0.0,"priority":"useful","scored":false},{"key":"a_timeline_retention","dimension":"A","label":"Availability timing & retention","action":"State when the data become available AND how long they will be retained — cite the repository's preservation policy. NIH DMS Element 4 asks for both; most papers give neither.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"The paper states only that the data are 'available at' a URL, with no mention of retention period or persistence commitment.","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T14:03:48.708178Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}