{
  "abstract": "Purpose\n\nThis study aimed to develop and validate a classification model for preoperative prediction of WHO grading of meningioma based on spatial habitat radiomics that can be explained by Ki-67 and p53 biomarkers.Methods\n\nA retrospective cohort of 400 patients (261 WHO grade I; 139 grade II/III) from a single institution was analyzed. Contrast-enhancing tumors were partitioned into three biologically distinct subregions (hypo-, intermediate-, and hyper-enhancement) using a 3D Simple Linear Iterative Clustering algorithm. Radiomics and clinical-radiological features were selected based on dual associations with Ki-67 ≥ 5% and p53 ≥ 5%. Subsequently, three classification models for preoperative prediction of WHO grading were constructed: clinical-radiological model (C-model), whole-tumor radiomics model (CRW-model), and subregion-based radiomics model (CRS-model). Performance was evaluated using AUC in training (n = 280) and test (n = 120) cohorts.Results\n\nSpatial habitat radiomics demonstrated biomarker-specific predictive power: hyper-enhancement subregions achieved the highest Ki-67 prediction AUC (training: 0.81; test: 0.70), while hypo-enhancement subregions excelled in p53 prediction (training: 0.93; test: 0.57). The CRS-model, achieved the highest AUC in both the training cohort (0.89 vs. 0.80 for CRW-model and 0.76 for C-model) and the test cohort (0.76 vs. 0.68 and 0.67, respectively). In the training cohort, the CRS-model significantly outperformed both the CRW-model and C-model after Bonferroni correction (adjusted p = 0.000450 and 7.06 × 10⁻⁷, respectively). In the test cohort, the CRS-model showed numerically higher AUCs, but the differences were not statistically significant after correction (adjusted p = 0.0891 and 0.0918, respectively).Conclusions\n\nSpatial habitat radiomics may help characterize intratumoral microenvironmental heterogeneity associated with Ki-67 and p53 expression, while providing a biologically interpretable framework for preoperative WHO grading of meningiomas.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.",
    "Department of Radiology, Taizhou Municipal Hospital, Taizhou, China.",
    "Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China.",
    "Department of Radiology, The Central Hospital Affiliated to Shaoxing University, Shaoxing Central Hospital), Shaoxing, China.",
    "Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. xiaojunguan1102@zju.edu.cn.",
    "Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. xxjmailbox@zju.edu.cn."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Huanhuan Xie"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiting Peng"
    },
    {
      "affs": [
        2
      ],
      "name": "Xingzhi Chen"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Yanting Yu"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhang Yi"
    },
    {
      "affs": [
        0
      ],
      "name": "Wei Meng"
    },
    {
      "affs": [
        2
      ],
      "name": "Chencui Huang"
    },
    {
      "affs": [
        2
      ],
      "name": "Yi You"
    },
    {
      "affs": [
        0
      ],
      "name": "Fei Dong"
    },
    {
      "affs": [
        0
      ],
      "name": "Minming Zhang"
    },
    {
      "affs": [
        4
      ],
      "name": "Xiaojun Guan"
    },
    {
      "affs": [
        5
      ],
      "name": "Xiaojun Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d5n1ffe5c9mwy.bib",
  "cited_by": 0,
  "doi": "10.1186/s12880-026-02632-5",
  "fulltext_url": "https://europepmc.org/articles/PMC13718091",
  "id": "d5n1ffe5c9mwy",
  "issns": [
    "1471-2342"
  ],
  "landing_url": "https://europepmc.org/article/MED/42958086",
  "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": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7172221449",
  "pdf_url": "https://europepmc.org/articles/PMC13718091?pdf=render",
  "pmcid": "PMC13718091",
  "pmid": "42958086",
  "published": "2026-08-03",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/d5n1ffe5c9mwy.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T00:20:47Z",
      "native_id": "MED:42958086",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42958086"
    },
    {
      "first_seen": "2026-11-02T12:22:14Z",
      "native_id": "PMC13718091",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12880-026-02632-5"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Ki-67/p53-associated angiogenic heterogeneity for preoperative WHO grading of meningiomas: a spatial habitat radiomics study.",
  "title_text": "Ki-67/p53-associated angiogenic heterogeneity for preoperative WHO grading of meningiomas: a spatial habitat radiomics study.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:22:14Z",
  "url": "https://research.draconitic.com/paper/d5n1ffe5c9mwy",
  "venue": "BMC medical imaging",
  "versions": []
}