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基于深度学习的人工智能模型来辅助 甲状腺结节的诊断和治疗:多中心 诊断研究.docx
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基于深度学习的人工智能模型来辅助 甲状腺结节的诊断和治疗:多中心 诊断研究.docx
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www.thelancet.com/digital-health
Vol 3
April 2021
e250
Articles
Deep learning-based artificial intelligence model to assist
thyroid nodule diagnosis and management: a multicentre
diagnostic study
Sui Peng*, Yihao Liu*, Weiming Lv*, Longzhong Liu*, Qian Zhou*, Hong Yang, Jie Ren, Guangjian Liu, Xiaodong Wang, Xuehua Zhang, Qiang Du,
Fangxing Nie, Gao Huang, Yuchen Guo, Jie Li, Jinyu Liang, Hangtong Hu, Han Xiao, Zelong Liu, Fenghua Lai, Qiuyi Zheng, Haibo Wang, Yanbing Li,
Erik K Alexander, Wei Wang, Haipeng Xiao
Summary
Background Strategies for integrating artificial intelligence (AI) into thyroid nodule management require additional
development and testing. We developed a deep-learning AI model (ThyNet) to differentiate between malignant
tumours and benign thyroid nodules and aimed to investigate how ThyNet could help radiologists improve diagnostic
performance and avoid unnecessary fine needle aspiration.
Methods ThyNet was developed and trained on 18 049 images of 8339 patients (training set) from two hospitals (the
First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, and Sun Yat-sen University Cancer Center,
Guangzhou, China) and tested on 4305 images of 2775 patients (total test set) from seven hospitals (the First Affiliated
Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China; the Sixth Affiliated Hospital of Sun Yat-
sen University, Guangzhou, China; the Guangzhou Army General Hospital, Guangzhou, China; the Third Affiliated
Hospital of Sun Yat-sen University, Guangzhou, China; the First Affiliated Hospital of Sun Yat-sen University; Sun
Yat-sen University Cancer Center; and the First Affiliated Hospital of Guangxi Medical University, Nanning, China) in
three stages. All nodules in the training and total test set were pathologically confirmed. The diagnostic performance
of ThyNet was first compared with 12 radiologists (test set A); a ThyNet-assisted strategy, in which ThyNet assisted
diagnoses made by radiologists, was developed to improve diagnostic performance of radiologists using images (test
set B); the ThyNet assisted strategy was then tested in a real-world clinical setting (using images and videos; test
set C). In a simulated scenario, the number of unnecessary fine needle aspirations avoided by ThyNet-assisted strategy
was calculated.
Findings The area under the receiver operating characteristic curve (AUROC) for accurate diagnosis of ThyNet (0·922
[95% CI 0·910–0·934]) was significantly higher than that of the radiologists (0·839 [0·834–0·844]; p<0·0001).
Furthermore, ThyNet-assisted strategy improved the pooled AUROC of the radiologists from 0·837 (0·832–0·842)
when diagnosing without ThyNet to 0·875 (0·871–0·880; p<0·0001) with ThyNet for reviewing images, and
from 0·862 (0·851–0·872) to 0·873 (0·863–0·883; p<0·0001) in the clinical test, which used images and videos. In
the simulated scenario, the number of fine needle aspirations decreased from 61·9% to 35·2% using the ThyNet-
assisted strategy, while missed malignancy decreased from 18·9% to 17·0%.
Interpretation The ThyNet-assisted strategy can significantly improve the diagnostic performance of radiologists and
help reduce unnecessary fine needle aspirations for thyroid nodules.
Funding National
Natural
Science
Foundation
of
China
and
Guangzhou
Science
and
Technology
Project.
Copyright
©
2021
The
Author(s).
Published
by
Elsevier
Ltd.
This
is
an
Open
Access
article
under
the
CC
BY-NC-ND
4.0 license.
Lancet Digit Health 2021;
3: e250–59
*Contributed equally to this
work
Clinical Trials
Unit
(Prof
S
Peng
PhD, Y
Liu
MD,
Q Zhou MS, Prof H Wang MPH),
Department of Endocrinology
(Y Liu, F Lai MM, Q Zheng MD,
Prof Y Li PhD, Prof H Xiao PhD),
Department of Medical
Ultrasonics, Institute of
Diagnostic and Interventional
Ultrasound (Y Liu, J Liang PhD,
H Hu MD, Han Xiao MD,
Z Liu MD, Prof W Wang), and
Department of Breast and
Thyroid Surgery
(Prof W
Lv
PhD,
J
Li
PhD),
The First Affiliated Hospital of
Sun Yat-sen University,
Guangzhou, China;
Department of Ultrasound,
Sun Yat-sen University Cancer
Center, State Key Laboratory
of Oncology in South China,
Guangzhou, China (L Liu PhD);
Department of Medical
Ultrasound, the First Affiliated
Hospital of Guangxi Medical
University, Nanning, China
(Prof H Yang PhD); Department
of Medical Ultrasonics,
the Third Affiliated Hospital of
Sun Yat-sen University,
Guangzhou,
China
(Prof J Ren PhD); Department
of Medical Ultrasonics, the
Sixth Affiliated Hospital of
Sun Yat-sen University,
Guangzhou, China (G Liu PhD);
Department of Medical
Ultrasonics, the First Affiliated
Introduction
Thyroid nodules are found in up to 68% of asymp
tomatic adults in the general population.
1
Approximately
7–15% of thyroid nodules are thyroid cancer, which is
the most rapidly increasing malignancy in all populations.
2
The large number of thyroid nodules, with only a fraction
being cancerous, calls for a reliable method to accurately
differentiate malignant from benign nodules.
Routine decision making for patients with thyroid
nodules depends on ultrasound or invasive fine needle
aspiration.
2
However,
the
assessment
of ultrasound
features is time consuming, subjective, and often
dependent on a radiologist’s experience and the available
ultrasound devices.
3
Ultrasound conclusions are often
inconsistent and even with fine needle aspirations
15–
30% of the samples still yield indeterminate cytological
findings.
4
Additional robust methods are needed to
improve diagnosis and fine needle aspiration strategies to
adapt to the exponential growth of patient needs and
burden on medical services.
Artificial intelligence (AI) has been reported to meet or
exceed human experts in medical imaging.
5–8
A few
Hospital of Guangzhou
University of Chinese
Medicine, Guangzhou, China
(X Wang MD); Department of
Ultrasound, the Guangzhou
Army General Hospital,
Guangzhou,
China
(X Zhang MD); Xiaobaishiji,
Beijing, China (Q Du ME,
F Nie ME, G Huang DE);
Institute for Brain and
Cognitive Sciences, Tsinghua
University, Beijing, China
(Y
Guo
ME);
Thyroid
Section,
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