{
  "abstract": "Glioblastoma (GBM) tumors with a high migration-to-proliferation ratio (D/k) are associated with treatment resistance, motivating the estimation of D/k from routine pre-treatment magnetic resonance imaging (MRI) data and the investigation of mechanisms underlying its heterogeneity across patients. Unlike previous approaches based on nested spherical approximations of enhancing and non-enhancing tumor volumes, we incorporate the spatial arrangement of the enhancing and non-enhancing tumor regions by deriving a 1D tumor density distribution from their segmentations and fitting a 1D reaction-diffusion model to estimate D/k. We achieved an error of under 5% when comparing our method to synthetic density distributions and found excellent correlation with synthetic tumor segmentations (Pearson correlation coefficient (PCC) = 0.996). We then evaluated associations between patient-specific D/k values and their clinical, transcriptomic, and radiomic features. When applied to 102 pre-treatment MRI scans from the Brain Tumor Segmentation (BraTS) 2017 dataset, elevated D/k significantly correlated with reduced overall survival (log-rank p = 0.01), older age (p = 0.04), and a trend toward lower Karnofsky Performance Status (p = 0.059). In a subset of 91 patients with available microarray data, mesenchymal-like phenotypes consistently exhibited elevated D/k across three established molecular subtype classifications (p = 0.013, p = 0.055, and p = 0.127). Elastic Net regularization followed by overrepresentation analysis revealed an enrichment of glycosylation-related pathways among genes associated with D/k variability (p = 0.0123 and p = 0.0368). Furthermore, Multi-Omic Factor Analysis identified a radiomic factor correlated with D/k (r = 0.57, p = 4x10-9), with the highest-weighted features corresponding to smoothness of the non-enhancing tumor region on T2 and FLAIR.",
  "abstract_shown": true,
  "affiliations": [
    "Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA",
    "UT Austin Cancer Research Center, The University of Texas at Austin, Austin, TX, 78712",
    "Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712",
    "Texas Advanced Computing Center, Austin, TX, 78758",
    "Institute for Systems Biology, Seattle, WA, 98109",
    "Diagnostic Medicine, The University of Texas at Austin, Austin, TX, 78712",
    "Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77230"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Rafael Bravo",
      "orcid": "0000-0002-0065-4341"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "David A. Hormuth"
    },
    {
      "affs": [
        0,
        2,
        3
      ],
      "name": "Ernesto A.B.F. Lima"
    },
    {
      "affs": [
        4
      ],
      "name": "Bahar Tercan"
    },
    {
      "affs": [
        4
      ],
      "name": "Boris Aguilar"
    },
    {
      "affs": [
        0,
        1,
        2,
        5,
        6
      ],
      "name": "Thomas E. Yankeelov",
      "orcid": "0000-0001-6201-3913"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0kdhyzd0dr5se.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.26.761772",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761772v1.full",
  "id": "0kdhyzd0dr5se",
  "issns": [
    "2692-8205"
  ],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761772v1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "systems biology"
    ],
    [
      "heading",
      "Article"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761772v1.full.pdf",
  "pmcid": "PMC13733866",
  "pmid": "42990308",
  "published": "2026-10-26",
  "publisher": "Cold Spring Harbor Laboratory Preprints",
  "ris": "https://research.draconitic.com/paper/0kdhyzd0dr5se.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T12:20:58Z",
      "native_id": "10.64898/2026.10.26.761772",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761772v1"
    },
    {
      "first_seen": "2026-11-08T12:23:33Z",
      "native_id": "PMC13733866",
      "source": "pmc",
      "url": "https://doi.org/10.64898/2026.10.26.761772"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:systems biology",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Estimating glioblastoma migration-to-proliferation ratio from single time-point MRI data via radial density distribution",
  "title_text": "Estimating glioblastoma migration-to-proliferation ratio from single time-point MRI data via radial density distribution",
  "type": "preprint",
  "updated_at": "2026-11-08T12:23:33Z",
  "url": "https://research.draconitic.com/paper/0kdhyzd0dr5se",
  "venue": "bioRxiv",
  "versions": []
}