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A Comparison of Different Radiomics Methods Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

  • 0Department of Ultrasound in Medicine, Sir Run Run Shaw Hospital, Zhejiang University, School of Medicine, Hangzhou, Zhejiang, China.
Journal of Clinical Ultrasound : Jcu +

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October 13, 2025

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October 13, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

A combined radiomics model using ultrasound images shows improved prediction of cervical lymph node metastasis in papillary thyroid carcinoma. This approach aids in tailoring surgical treatment for patients.

Area Of Science

  • Radiology
  • Oncology
  • Medical Imaging

Background

  • Accurate preoperative identification of cervical lymph node metastasis in papillary thyroid carcinoma (PTC) is crucial for surgical planning.
  • Ultrasound-based radiomics offers potential for non-invasive prediction of metastasis.

Purpose Of The Study

  • To develop and compare the performance of handcrafted radiomics, deep learning radiomics, and a combined model for predicting cervical lymph node metastasis in PTC.
  • To evaluate the efficacy of these models in guiding surgical treatment decisions.

Main Methods

  • Retrospective analysis of 441 PTC patients.
  • Extraction of handcrafted radiomics features (physician-selected) and deep learning radiomics features (automated DenseNet121).
  • Development of a combined model integrating both feature types; performance assessed using ROC analysis and DeLong's tests.

Main Results

  • The combined model demonstrated superior AUC in the training set (0.790) compared to handcrafted (0.743) and deep learning (0.730) models.
  • While the combined model showed higher AUC in the testing set (0.761), statistical significance was not reached.
  • Handcrafted radiomics achieved high accuracy (0.714 training, 0.707 testing), outperforming deep learning radiomics (0.698 training, 0.662 testing).

Conclusions

  • A combined radiomics model utilizing conventional ultrasound images improves the prediction of cervical lymph node metastasis in PTC.
  • This integrated approach offers enhanced predictive performance over individual radiomics models for clinical application.

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