A Thyroid Ultrasound Image-based Artificial Intelligence Model for Diagnosis of Central Compartment Lymph Node Metastasis in Papillary Thyroid Carcinoma.
10.3881/j.issn.1000-503X.13823
- Author:
Ying-Ying LI
1
;
Wen-Xuan SUN
2
;
Xian-Dong LIAO
2
;
Ming-Bo ZHANG
1
;
Fang XIE
1
;
Dong-Hao CHEN
2
;
Yan ZHANG
1
;
Yu-Kun LUO
1
Author Information
1. Department of Ultrasound,the First Medical Center of PLA General Hospital,Beijing 100853,China.
2. School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing 100876,China.
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
central compartment lymph node metastasis;
papillary thyroid carcinoma;
ultrasound
- MeSH:
Artificial Intelligence;
Humans;
Lymph Nodes/diagnostic imaging*;
Lymphatic Metastasis;
Retrospective Studies;
Risk Factors;
Thyroid Cancer, Papillary/diagnostic imaging*;
Thyroid Neoplasms/diagnostic imaging*
- From:
Acta Academiae Medicinae Sinicae
2021;43(6):911-916
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish an artificial intelligence model based on B-mode thyroid ultrasound images to predict central compartment lymph node metastasis(CLNM)in patients with papillary thyroid carcinoma(PTC). Methods We retrieved the clinical manifestations and ultrasound images of the tumors in 309 patients with surgical histologically confirmed PTC and treated in the First Medical Center of PLA General Hospital from January to December in 2018.The datasets were split into the training set and the test set.We established a deep learning-based computer-aided model for the diagnosis of CLNM in patients with PTC and then evaluated the diagnosis performance of this model with the test set. Result The accuracy,sensitivity,specificity,and area under receiver operating characteristic curve of our model for predicting CLNM were 80%,76%,83%,and 0.794,respectively. Conclusion Deep learning-based radiomics can be applied in predicting CLNM in patients with PTC and provide a basis for therapeutic regimen selection in clinical practice.
