Evidence map›Paper›PMID 41781694›Full record

ArticleNPJ digital medicine2026

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

Jianming Li, Haoyan Zhang, Huan Zheng, Yuancheng Cang, Lin Xue Qian, Ligang Cui, Xinping Wu, Baoding Chen, Man Lu, Yong Xu and 19 more

Abstract read
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In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

29 authors.

Jianming Li *Department of Interventional Ultrasound, Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China.
Haoyan Zhang *CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Huan ZhengDepartment of Ultrasound, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, China.
Yuancheng CangDepartment of Interventional Ultrasound, Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China.
Lin Xue QianDepartment of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Ligang CuiDepartment of Ultrasound, Peking University Third Hospital, Beijing, China.
Xinping WuDepartment of Ultrasound, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
Baoding ChenDepartment of Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, China.
Man LuDepartment of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Yong XuDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Runqin MiaoDepartment of Ultrasound, Shanxi Cancer Hospital, Taiyuan, China.
Desheng SunDepartment of Medical Ultrasound, Peking University Shenzhen Hospital, Shenzhen, China.
Liping LiuDepartment of Interventional Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Ping LiDepartment of Ultrasound, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Changsong XuDepartment of ultrasound medicine, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huaian, China.
Li MaDepartment of Ultrasound, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangdong, Guangzhou, China.
Guoyong HuaDepartment of Interventional Ultrasound, Qinghai Provincial People's Hospital, Xining, China.
Shengnan HuoDepartment of Thyroid, Handan Hangang Hospital, Handan City, Hebei, China.
Yanjun LiuDepartment of Ultrasound, The First Affiliated Hospital of China Medical University, Shenyang, China.
Weide DaiDepartment of Ultrasound Medicine, Beijing Hospital, Beijing, China.
Kexin LouDepartment of Medical Ultrasound, Xuzhou Central Hospital, Xuzhou, China.
Xiang XieDepartment of Interventional Ultrasound, The Second Hospital of Anhui Medical University, Hefei, China.
Liping YangDepartment of Interventional Ultrasound, Puyang Traditional Chinese Medicine Hospital, Puyang, Henan, China.
Fang MeiDepartment of Pathology, Peking University Third Hospital, Beijing, China.
Bo PingDepartment of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
Xin YangCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Jie YuDepartment of Interventional Ultrasound, Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China. jiemi301@163.com.
Kun WangCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. kun.wang@ia.ac.cn.
Ping LiangDepartment of Interventional Ultrasound, Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China. liangping301@126.com.

Funding

Beijing Science Fund for Distinguished Young Scholars JQ22013National Natural Science Foundation of China 82441010National Natural Science Foundation of China 82471995
6 · The paper itself

Abstract

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.

Identifiers

PMID41781694
PMCPMC13076890

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.