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<p>Preoperatively distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA) remains a significant clinical challenge. Current ultrasound risk stratification systems show limited efficacy for follicular neoplasms, and existing artificial intelligence (AI) approaches lack sufficient validation. We developed and validated a deep learning model using ultrasound images to differentiate FTC from FTA and classify FTC into invasion subtypes. This multicenter retrospective study incorporated data from 31 hospitals, using 1531 patients for model development and 900 across three external test sets for validation. The model demonstrated high diagnostic performance, with AUCs of 0.816–0.847 for FTC vs FTA discrimination across external test sets and robust performance across subtypes (AUC range 0.754–0.910), and generalized well to varied clinical settings. Triple-classification macro-AUCs were 0.818–0.861. It consistently outperformed radiologists and improved diagnostic accuracy as an assistive tool. Our AI model provides a reliable, non-invasive tool for preoperative diagnosis and risk stratification of follicular thyroid neoplasms.</p>

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Artificial intelligence-enabled ultrasound diagnosis and stratification of follicular thyroid neoplasms: a multi-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月5日

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  • Jianming Li,
  • Haoyan Zhang,
  • Huan Zheng,
  • Yuancheng Cang,
  • Lin xue Qian,
  • Ligang Cui,
  • Xinping Wu,
  • Baoding Chen,
  • Man Lu,
  • Yong Xu,
  • Runqin Miao,
  • Desheng Sun,
  • Liping Liu,
  • Ping Li,
  • Changsong Xu,
  • Li Ma,
  • Guoyong Hua,
  • Shengnan Huo,
  • Yanjun Liu,
  • Weide Dai,
  • Kexin Lou,
  • Xiang Xie,
  • Liping Yang,
  • Fang Mei,
  • Bo Ping,
  • Xin Yang,
  • Jie Yu,
  • Kun Wang,
  • Ping Liang

摘要

Preoperatively distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA) remains a significant clinical challenge. Current ultrasound risk stratification systems show limited efficacy for follicular neoplasms, and existing artificial intelligence (AI) approaches lack sufficient validation. We developed and validated a deep learning model using ultrasound images to differentiate FTC from FTA and classify FTC into invasion subtypes. This multicenter retrospective study incorporated data from 31 hospitals, using 1531 patients for model development and 900 across three external test sets for validation. The model demonstrated high diagnostic performance, with AUCs of 0.816–0.847 for FTC vs FTA discrimination across external test sets and robust performance across subtypes (AUC range 0.754–0.910), and generalized well to varied clinical settings. Triple-classification macro-AUCs were 0.818–0.861. It consistently outperformed radiologists and improved diagnostic accuracy as an assistive tool. Our AI model provides a reliable, non-invasive tool for preoperative diagnosis and risk stratification of follicular thyroid neoplasms.