{
  "abstract": "Gliomas with O6-methylguanine-DNA methyltransferase promoter (MGMTp)-methylation exhibit superior therapeutic responses, as well as longer progression-free survival and overall survival. Diffusion kurtosis imaging (DKI) is an extension of diffusion tensor imaging (DTI) that quantifies the degree of non-Gaussian diffusion of water molecules. This provides superior sensitivity to the complex microstructural heterogeneity of brain tumors. This study aimed to explore the diagnostic performance of DKI combined with magnetic resonance imaging (MRI) morphological features in predicting the 6-methylguanine-DNA methyltransferase promoter (MGMTp) status in gliomas. Sixty-one patients with glioma who underwent biopsy or surgery were included, comprising 39 cases in the MGMTp-methylated group and 22 cases in the MGMTp-unmethylated group. All patients underwent conventional MRI and DKI sequences preoperatively. This study quantified DKI metrics in the tumor region and in contralateral normal-appearing white matter (NAWM). Subsequently, relative DKI parameters were calculated for both the tumor parenchyma and the peritumoral edema region. These included relative mean kurtosis (rMK), relative radial kurtosis (rKr), relative axial kurtosis (rKa), relative mean diffusivity (rMD), and relative fractional anisotropy (rFA). Differences in clinical data, morphological features, and DKI-derived parameters were compared between the two groups. Receiver operating characteristic (ROC) curves were utilized to determine the diagnostic performance, including the area under the curve (AUC), sensitivity, and specificity. Among 61 patients, 39 (63.9%) had MGMTp methylation. MGMTp methylation was significantly associated with isocitrate dehydrogenase (IDH) mutation status (P=0.001). Blurred tumor margins were more frequent in methylated gliomas (71.8% vs. 18.2%, P<0.001). In the tumor parenchyma, the unmethylated group showed significantly higher rMK and rKr, and lower rMD (all P<0.001). Multivariable analysis confirmed that rMK [odds ratio (OR) =0.12, P=0.003], rMD (OR =4.85, P=0.02), and blurred margins (OR =6.42, P=0.009) remained independent predictors after adjusting for World Health Organization (WHO) grade and IDH status. The fusion model combining rMK and tumor margin achieved the highest AUC of 0.924 (sensitivity 87.8%, specificity 81.8%). Normalization significantly and critically enhances the predictive performance for MGMTp methylation status. DKI parameters, particularly rMK and rMD, combined with MRI morphological features (blurred tumor margin), provide excellent non-invasive prediction of MGMTp methylation status in gliomas, independent of tumor grade and IDH mutation status. This integrative approach may facilitate preoperative treatment planning and individualized chemotherapy strategies.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, China.",
    "Department of Radiology, The Affiliated Hospital, Southwest Medical University, Luzhou, China.",
    "Precision Imaging and Intelligent Analysis Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yin Gao"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhenyu Song"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Ping Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Huan Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fksaa43rqtstd.bib",
  "cited_by": 0,
  "doi": "10.21037/tcr-2026-1652",
  "fulltext_url": "https://europepmc.org/articles/PMC13743259",
  "id": "fksaa43rqtstd",
  "issns": [
    "2218-676X",
    "2219-6803"
  ],
  "landing_url": "https://europepmc.org/article/MED/42997756",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Advanced Neuroimaging Techniques and Applications"
    ]
  ],
  "openalex": "W7223259268",
  "pdf_url": "https://europepmc.org/articles/PMC13743259?pdf=render",
  "pmcid": "PMC13743259",
  "pmid": "42997756",
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fksaa43rqtstd.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:41Z",
      "native_id": "MED:42997756",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42997756"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Advanced Neuroimaging Techniques and Applications",
      "subfield": "imaging"
    }
  ],
  "title": "Predictive performance of diffusion kurtosis imaging combined with magnetic resonance imaging morphological features for MGMT promoter status in glioma.",
  "title_text": "Predictive performance of diffusion kurtosis imaging combined with magnetic resonance imaging morphological features for MGMT promoter status in glioma.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:41Z",
  "url": "https://research.draconitic.com/paper/fksaa43rqtstd",
  "venue": "Translational cancer research",
  "versions": []
}