{
  "abstract": "Objectives: To develop and externally validate MRI-based radiomics and clinicoradiomics models for predicting pathological lymph node metastasis (LNM) in neuroendocrine carcinoma of the cervix (NECC). Methods: This retrospective two-center study included 133 patients who underwent pretreatment pelvic MRI and pathological nodal assessment: 93 for development and 40 for external validation. Intratumoral and peritumoral features were extracted from T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted images. Stratified nested fivefold cross-validation evaluated the complete revised selection process. Locked models were assessed for discrimination, calibration, and exploratory net benefit, with conventional MRI nodal assessment as a comparator. Results: Pathological LNM was present in 54/133 patients (40.6%). The selected radiomics-only k-nearest neighbors (KNN) model had an external AUC of 0.783 (95% CI, 0.630 to 0.935), accuracy of 0.800, sensitivity of 0.813, and specificity of 0.792. The complete selection procedure yielded an out-of-fold AUC of 0.731. External AUCs were 0.727 for the clinical/MRI model and 0.767 for the clinicoradiomics model; neither fusion model differed significantly from the clinical/MRI comparator. Conventional MRI sensitivity was 0.563, with no significant paired difference from the selected model (p = 0.125). External calibration was imperfect (Brier score, 0.186; slope, 0.672). Conclusions: MRI radiomics showed moderate external discrimination of pathological LNM in NECC. Adding clinical variables did not demonstrate further benefit. Multicenter validation, recalibration, and clinical-impact assessment are needed before clinical application.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China.",
    "Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China.",
    "Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou 350014, China.",
    "Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China.",
    "Department of Obstetrics and Gynecology, Fujian Provincial Hospital, Fuzhou 350001, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jian Chen",
      "orcid": "0000-0003-4234-2028"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Fengle Jiang"
    },
    {
      "affs": [
        3
      ],
      "name": "Xingfa Chen"
    },
    {
      "affs": [
        4
      ],
      "name": "Rong Lin"
    },
    {
      "affs": [
        3
      ],
      "name": "Ning Ma"
    },
    {
      "affs": [
        0
      ],
      "name": "Haijuan Yu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yang Sun",
      "orcid": "0000-0002-1587-9797"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/66ag4xyay93yw.bib",
  "cited_by": 0,
  "doi": "10.3390/curroncol33100625",
  "fulltext_url": "https://europepmc.org/articles/PMC13703500",
  "id": "66ag4xyay93yw",
  "issns": [
    "1198-0052",
    "1718-7729"
  ],
  "landing_url": "https://europepmc.org/article/MED/42930192",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7222505488",
  "pdf_url": "https://europepmc.org/articles/PMC13703500?pdf=render",
  "pmcid": "PMC13703500",
  "pmid": "42930192",
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/66ag4xyay93yw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "MED:42930192",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42930192"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "MRI-Based Radiomics Model for Pretreatment Prediction of Pathological Lymph Node Metastasis in Neuroendocrine Carcinoma of the Cervix: Dual-Center Development and External Validation.",
  "title_text": "MRI-Based Radiomics Model for Pretreatment Prediction of Pathological Lymph Node Metastasis in Neuroendocrine Carcinoma of the Cervix: Dual-Center Development and External Validation.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/66ag4xyay93yw",
  "venue": "Current oncology (Toronto, Ont.)",
  "versions": []
}