{
    "abstract": "To investigate the predictability of late cervical lymph node metastasis using radiomics analysis of ultrasonographic images of tongue cancer.\nWe selected 128 patients with tongue cancer who underwent intraoral ultrasonography at the pre-treatment, 35 of whom had late cervical lymph node metastasis. Radiomics analysis was used to extract and quantify the image features. Segmentations were performed on three regions: the hypoechoic region with a 3-mm margin (0\u2009+\u20093-mm margin), the hypoechoic region alone (0-mm margin), and the 3-mm region surrounding the hypoechoic area (3-mm margin). Support vector machine (SVM) and neural network (NNT) were used as the machine learning models, and sensitivity, specificity, and area under the curve (AUC) from the receiver operating characteristic curves were determined for diagnostic performances.\nThe AUC values in the test group were 0.893, 0.929, and 0.679 for the SVM models with 0\u2009+\u20093-, 0-, and 3-mm margins, respectively. The AUC values in the test group were 0.905, 0.952, and 0.821 for the NNT models with 0\u2009+\u20093-, 0-, and 3-mm margins, respectively.\nRadiomics analysis and machine learning models using ultrasonographic images of pretreated tongue cancer with a hypoechoic area (0-mm margin) could be the best models to predict late cervical lymph node metastasis.\nThis study makes a significant contribution to the tongue cancer treatment because radiomics analysis and machine learning models using ultrasonographic images of before the primary treatment for the tongue cancer could predict late cervical lymph node metastasis with high accuracy.",
    "authors": [
        {
            "affiliation": "Department of Oral and Maxillofacial Radiology, Hiroshima University Hospital, Hiroshima, Japan.",
            "firstname": "Masaru",
            "initials": "M",
            "lastname": "Konishi"
        },
        {
            "affiliation": "Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.",
            "firstname": "Kiichi",
            "initials": "K",
            "lastname": "Shimabukuro"
        },
        {
            "affiliation": "Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.",
            "firstname": "Naoya",
            "initials": "N",
            "lastname": "Kakimoto"
        }
    ],
    "conclusions": null,
    "copyrights": "\u00a9 The Author(s) 2025. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site\u2014for further information please contact journals.permissions@oup.com.",
    "doi": "10.1093/dmfr/twaf016",
    "journal": "Dento maxillo facial radiology",
    "keywords": [
        "cervical lymph node metastasis",
        "machine learning model",
        "radiomics",
        "tongue cancer",
        "ultrasonography"
    ],
    "methods": null,
    "publication_date": "2025-03-21",
    "pubmed_id": "40116427",
    "results": "The AUC values in the test group were 0.893, 0.929, and 0.679 for the SVM models with 0\u2009+\u20093-, 0-, and 3-mm margins, respectively. The AUC values in the test group were 0.905, 0.952, and 0.821 for the NNT models with 0\u2009+\u20093-, 0-, and 3-mm margins, respectively.",
    "title": "Radiomics analysis of intraoral ultrasonographic images for prediction of late cervical lymph node metastasis in patients with tongue cancer: influence of marginal region.",
    "xml": "<Element 'PubmedArticle' at 0x7885047f0f90>"
}