{
  "abstract": "Objectives: To investigate whether whole-tumor histogram features derived from five non-Gaussian diffusion models, combined with clinicopathological factors, are associated with lymph node metastasis (LNM) in endometrial cancer (EC).\n\nMethods: This prospective study enrolled 73 patients with histologically confirmed EC and known nodal status. All underwent preoperative 3.0T MRI including diffusion-weighted imaging with 16 b-values. Thirteen parametric maps were derived from five non-Gaussian models: intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), fractional-order calculus (FROC), and continuous time random walk (CTRW). Clinicopathological data were collected, including demographics, comorbidities, serum markers, pathological type, myometrial invasion, cervical involvement, and MRI-based local stage. Clinical, radiomics, and combined logistic regression models were built. Performance was evaluated using AUC, calibration curves, decision curve analysis, and bootstrap analysis of intercept variability.\n\nResults: LNM was confirmed in 21 of 73 patients. The clinical model achieved an AUC of 0.837, the radiomics model achieved an AUC of 0.856, and the combined model achieved an AUC of 0.924, with 90.5% sensitivity and 82.7% specificity. DeLong tests revealed no significant AUC differences among models (P = 0.090–0.140). The combined model had the lowest Brier score (0.092) and provided higher net benefit across threshold probabilities of 0.1–0.3.\n\nConclusion: Non-Gaussian diffusion histogram parameters, particularly DKI_K_energy, demonstrate potential for predicting LNM in EC. A combined model incorporating DKI_K_energy with age and pathological type shows promise as a potential biomarker for risk stratification.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Magnetic Resonance, The First Affiliated Hospital of Henan Medical University, Xinxiang, Henan, China",
    "First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China",
    "MR Research China, GE Healthcare, Beijing, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yaqi Zang"
    },
    {
      "affs": [
        0
      ],
      "name": "Hongxia Wang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Wenpeng Ge"
    },
    {
      "affs": [
        0
      ],
      "name": "Huijia Yin"
    },
    {
      "affs": [
        2
      ],
      "name": "Lizhi Xie"
    },
    {
      "affs": [
        0
      ],
      "name": "Ruijing Qin"
    },
    {
      "affs": [
        0
      ],
      "name": "Wangyi Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Dongming Han"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6fx98qgz7vdnd.bib",
  "cited_by": 0,
  "doi": "10.3389/fmed.2026.1959334",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13736822/",
  "id": "6fx98qgz7vdnd",
  "issns": [
    "2296-858X"
  ],
  "landing_url": "https://doi.org/10.3389/fmed.2026.1959334",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2741|MRI in cancer diagnosis"
    ]
  ],
  "openalex": "W7223170399",
  "pdf_url": "https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1959334/pdf",
  "pmcid": "PMC13736822",
  "pmid": "42991826",
  "published": "2026-10-22",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/6fx98qgz7vdnd.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:22:11Z",
      "native_id": "PMC13736822",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fmed.2026.1959334"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|MRI in cancer diagnosis",
      "subfield": "imaging"
    }
  ],
  "title": "Whole-tumor histogram analysis of non-Gaussian diffusion MRI models combined with clinicopathological indicators for assessing lymph node metastasis in endometrial cancer",
  "title_text": "Whole-tumor histogram analysis of non-Gaussian diffusion MRI models combined with clinicopathological indicators for assessing lymph node metastasis in endometrial cancer",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:11Z",
  "url": "https://research.draconitic.com/paper/6fx98qgz7vdnd",
  "venue": "Frontiers in Medicine",
  "versions": []
}