{
  "abstract": "Background: Magnetic Resonance Spectroscopy (MRS) is a non-invasive imaging technique that detects various metabolites in brain tissues, making it essential for the grading of brain gliomas. MRS measures key metabolites such as N-acetyl-aspartate (NAA), choline (Cho), and creatine (Cr), whose ratio changes provide valuable information for glioma grading, guiding diagnosis, treatment, and prognosis. Current studies are largely single-center, with grading criteria differing across hospitals. This work aims to identify reliable and consistent metabolite ratios for glioma grading by jointly analyzing multicenter 3T single-voxel MRS of brain gliomas. The grading accuracy of these metabolite ratios is evaluated to establish a more standardized diagnostic basis.\n\nMaterials and methods: We retrospectively included the MRS data from 60 glioma patients from 3 hospitals. MRS results included 4 metabolite ratios: NAA/Cr, Cho/NAA, Cho/Cr, and NAA/(Cho + Cr). The Shapiro-Wilk test was employed to ascertain the normality of the data, and the differences were calculated using either an independent t-test or a Mann-Whitney U-test. The classification performance was evaluated using ROC curve and accuracy analysis.\n\nResults: Across multicenter data, statistically significant differences were found in Cho/NAA and Cho/Cr ratios between low- and high-grade gliomas (P < 0.05). ROC analysis showed AUCs of 0.73 for Cho/NAA and 0.74 for Cho/Cr, indicating good classification performance. Validation results showed 75% accuracy, supporting their potential for clinical glioma grading. The optimal cut-off value trained from any two hospitals is validated at a third hospital, and the accuracies of Cho/Cr are 77.78%, 100%, and 65%, respectively.\n\nConclusion: Multicenter data analysis confirmed the good classification accuracy of Cho/NAA and Cho/Cr in glioma grading, providing a reliable basis for clinical treatment and prognosis planning. This aligns with findings from other studies.\n\nClinical trial number: Not applicable.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12880-026-02640-5.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer and Information Engineering, Fujian Engineering Research Center for Medical Data Mining and Application, Xiamen University of Technology, Xiamen, 361024 China",
    "Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, Xiamen University, Xiamen, 361005 China",
    "Department of Applied Marine Physics and Engineering, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005 China",
    "Department of Radiology, The First Affiliated Hospital of Xiamen University, 55 Zhenhai Road, Siming District, Xiamen, 361000 China",
    "Department of Nuclear Magnetic Resonance, The Second Hospital & Clinical Medical School, Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou University, Lanzhou, 730030 China",
    "Department of Radiology, Zhongshan Hospital (Xiamen), Xiamen Municipal Clinical Research Center for Medical Imaging, Fujian Province Key Clinical Specialty for Medical Imaging, Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence, Fudan University, Xiamen, 361015 China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Di Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "Kaiwei Xie"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiacheng Lin"
    },
    {
      "affs": [
        1
      ],
      "name": "Jianshu Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Jialue Zhang"
    },
    {
      "affs": [
        2
      ],
      "name": "Meijin Lin"
    },
    {
      "affs": [
        1,
        3
      ],
      "name": "Xiaobo Qu"
    },
    {
      "affs": [
        4
      ],
      "name": "Wanjun Hu"
    },
    {
      "affs": [
        3
      ],
      "name": "Jianhua Wang"
    },
    {
      "affs": [
        5
      ],
      "name": "Dongyun Liang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0p039a2k1rerc.bib",
  "cited_by": 0,
  "doi": "10.1186/s12880-026-02640-5",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13715249/",
  "id": "0p039a2k1rerc",
  "issns": [
    "1471-2342"
  ],
  "landing_url": "https://doi.org/10.1186/s12880-026-02640-5",
  "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",
      "Research"
    ],
    [
      "openalex",
      "2741|Advanced MRI Techniques and Applications"
    ]
  ],
  "openalex": "W7171922633",
  "pdf_url": null,
  "pmcid": "PMC13715249",
  "pmid": "42947054",
  "published": "2026-07-31",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/0p039a2k1rerc.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T12:22:36Z",
      "native_id": "PMC13715249",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12880-026-02640-5"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Advanced MRI Techniques and Applications",
      "subfield": "imaging"
    }
  ],
  "title": "Gliomas grading prediction using multicenter 3T Magnetic Resonance Spectroscopy",
  "title_text": "Gliomas grading prediction using multicenter 3T Magnetic Resonance Spectroscopy",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:22:36Z",
  "url": "https://research.draconitic.com/paper/0p039a2k1rerc",
  "venue": "BMC Medical Imaging",
  "versions": []
}