{
  "abstract": "Objectives: Preoperative identification of FIGO 2023 stage I endometrial cancer (EC) is important for treatment planning. We aimed to develop and validate a deep-learning radiomics nomogram (DLRN) based on multiparametric MRI to distinguish stage I EC from stage II–IV disease.\n\nMaterials and methods: This study retrospectively enrolled patients from three centers. Patients from Centers 1 and 2 were randomly split into a training set ( n = 616), and an internal test set ( n = 158), and Center 3 served as an external test set ( n = 232). The DLRN integrated deep-learning features, intratumoral and peritumoral radiomics features, and clinical characteristics. The DLRN was compared with clinical, radiomics, and deep-learning models. Discrimination was evaluated using the area under the receiver operating characteristic curve (AUC), with AUCs compared using DeLong’s test.\n\nResults: A total of 1006 patients (mean age, 56.5 ± 9.7 years) were analyzed. In the internal test set, the DLRN achieved an AUC of 0.852, higher than the clinical (0.673; p = 0.001), original radiomics (0.765; p = 0.030), and combined radiomics (0.793; p = 0.050) models. In the external test set, the DLRN remained robust (AUC = 0.829) and outperformed the clinical, original radiomics, and combined radiomics models (AUC = 0.680, 0.681, and 0.743; all p < 0.005). With DLRN assistance, radiologists showed improved diagnostic performance.\n\nConclusion: The MRI-based DLRN showed robust discrimination between FIGO 2023 stage I and stage II–IV EC across test sets, supporting its potential for preoperative risk stratification and decision support.\n\nKey Points: Question The introduction of the FIGO 2023 staging system has made the differentiation between stages more challenging, highlighting the need for better tools.\n\nFindings A new MRI-based deep-learning radiomics nomogram (DLRN) accurately identifies high-risk patients, significantly outperforming traditional and individual clinical prediction methods.\n\nCritical relevance statement This tool helps doctors make better preoperative treatment plans, potentially improving patient outcomes in endometrial cancer.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "Xiao Chu"
    },
    {
      "name": "Liting Gao"
    },
    {
      "name": "Xinyi Wang"
    },
    {
      "name": "Tao Quan"
    },
    {
      "name": "Yuxin Ding"
    },
    {
      "name": "Ying Chen"
    },
    {
      "name": "Genji Bai"
    },
    {
      "name": "Mingxiang Wei"
    },
    {
      "name": "Shuangqing Chen",
      "orcid": "0000-0002-1504-4773"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f3jykznap29ge.bib",
  "cited_by": 0,
  "doi": "10.1186/s13244-026-02428-9",
  "fulltext_url": null,
  "id": "f3jykznap29ge",
  "issns": [
    "1869-4101"
  ],
  "landing_url": "https://link.springer.com/article/10.1186/s13244-026-02428-9",
  "language": null,
  "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": [],
  "openalex": null,
  "pdf_url": "https://link.springer.com/content/pdf/10.1186/s13244-026-02428-9.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/f3jykznap29ge.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:21:25Z",
      "native_id": "10.1186/s13244-026-02428-9",
      "source": "crossref",
      "url": "https://link.springer.com/article/10.1186/s13244-026-02428-9"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:1869-4101",
      "subfield": "imaging"
    }
  ],
  "title": "MRI-based deep-learning radiomics nomogram for differentiating FIGO 2023 stage I from stage II–IV endometrial cancer: a multicenter study",
  "title_text": "MRI-based deep-learning radiomics nomogram for differentiating FIGO 2023 stage I from stage II–IV endometrial cancer: a multicenter study",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T00:23:10Z",
  "url": "https://research.draconitic.com/paper/f3jykznap29ge",
  "venue": "Insights into Imaging",
  "versions": []
}