AI-based multimodal prediction of lymph node metastasis and capsular invasion in cT1N0M0 papillary thyroid carcinoma.

Journal: Frontiers in endocrinology
Published Date: May 27, 2025

Abstract

BACKGROUND: Accurate preoperative evaluation of cT1N0M0 papillary thyroid carcinoma (PTC) is essential for guiding appropriate treatment strategies. Although ultrasound is widely used for clinical staging, it has limitations in detecting lymph node metastasis (LNM) and capsular invasion (CI), which may lead to misclassification of high-risk patients. Such undetected risks pose safety concerns for those undergoing radiofrequency ablation. This study aimed to develop an artificial intelligence (AI)-assisted predictive model that integrates ultrasound radiomics and deep learning features to improve the identification of LNM and CI, thereby enhancing risk stratification and optimizing treatment strategies for cT1N0M0 PTC patients.

Authors

  • Xiaowei Peng
    Department of Thyroid Surgery, Hunan Cancer Hospital & The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Peng Wu
    Department of Orthopedics, General Hospital of Ningxia Medical University, Yinchuan, China.
  • Wu Li
    Department of Thyroid Surgery, Hunan Cancer Hospital & The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Tao Ou-Yang
    Department of Medical Ultrasound, Hunan Cancer Hospital & The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Shi Chu Tang
    Department of Medical Ultrasound, Hunan Cancer Hospital & The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Shiwei Zhou
    Department of Thyroid Surgery, Hunan Cancer Hospital & The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Hui Li
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Xiaohua Song
    Department of Thyroid Surgery, Hunan Cancer Hospital & The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Yulong Tang
    Department of Thyroid Surgery, Hunan Cancer Hospital & The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.

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