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Volume 47 Issue 3
Mar.  2024
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Article Navigation >  Journal of Molecular Imaging  > 2024  >  47(3): 294-303
ZHOU Yuhao, HUANG Lihua, WEN Ge. Value of ultrasonography combined with model in predicting ipsilateral central cervical lymph node metastasis in papillary thyroid carcinoma[J]. Journal of Molecular Imaging, 2024, 47(3): 294-303. doi: 10.12122/j.issn.1674-4500.2024.03.12
Citation: ZHOU Yuhao, HUANG Lihua, WEN Ge. Value of ultrasonography combined with model in predicting ipsilateral central cervical lymph node metastasis in papillary thyroid carcinoma[J]. Journal of Molecular Imaging, 2024, 47(3): 294-303. doi: 10.12122/j.issn.1674-4500.2024.03.12
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Value of ultrasonography combined with model in predicting ipsilateral central cervical lymph node metastasis in papillary thyroid carcinoma

doi: 10.12122/j.issn.1674-4500.2024.03.12
  • 1.

    Department of Medical Ultrasonics, Nanfang Hospital Zengcheng Campus, Southern Medical University, Guangzhou 511338, China

  • 2.

    Imaging Center of Southern Hospital, Southern Medical University, Guangzhou 510515, China

  • Received Date: 2023-11-18
    Available Online: 2024-04-17
  • Publish Date: 2024-03-20
    Abstract
  •   Objective   To investigate the value of ultrasound combined model in predicting ipsilateral central cervical lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC).   Methods   The clinical data and preoperative two-dimensional ultrasound images of 137 patients with pathologically confirmed PTC in Nanfang Hospital of Southern Medical University from January 2021 to July 2021 were retrospectively analyzed, and they were were divided into metastatic group (n=65) and non-metastatic group (n=72) by postoperative pathological results. All patients underwent prophylactic central lymph node dissection. The lesions were delineated manually in the ultrasound images, and the texture features were derived from the processed ultrasound images. Then ICC, statistical screening, correlation coefficient screening and LASSO method were used, and the non-0 features filtered by LASSO were used as input to build the image feature model. 137 patients' clinically effective information was used to construct the same clinical feature model as the image feature model. A combined model was constructed by combining imaging features with clinical features.   Results   Among the image feature models, the ExtraTrees model has the best performance, and the AUC of the training set and the test set are 0.895 and 0.836 respectively. The optimal model for clinical features is also the ExtraTrees model, with AUC of 0.843 and 0.701 in the training and test sets, respectively. The combined model has the best predictive ability, with AUC of 0.900 and 0.854 for the training set and test set, respectively.   Conclusion   The combined model combining imaging features and clinical features has a good ability to predict CLNM in the ipsilateral central region of PTC, and it can provide a non-invasive and effective method for clinical decision-making.

     

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