Efficacy of transfer learning artificial intelligence model based on ultrasound in evaluating the probability of malignancy of partially cystic thyroid nodule
10.3969/j.issn.1006-5725.2025.06.018
- VernacularTitle:基于超声的迁移学习人工智能模型对甲状腺囊实性结节恶性概率的评估效能
- Author:
Ying ZOU
1
;
Jihua LIU
;
Jingyi LI
;
Hai BI
;
Yan SHI
;
Xiudi LU
;
Qibo ZHANG
Author Information
1. 天津中医药大学第一附属医院国家中医针灸临床医学研究中心医学影像科(天津 300381)
- Publication Type:Journal Article
- Keywords:
ultrasound;
partially cystic thyroid nodule;
transfer learning;
artificial intelligence model;
ultrasound-guided fine needle aspiration biopsy
- From:
The Journal of Practical Medicine
2025;41(6):889-895
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the feasibility and accuracy of an ultrasound-based transfer learning artificial intelligence model in predicting the malignancy probability of partially cystic thyroid nodules(PCTN).Methods A retrospective analysis was conducted on 246 patients with PCTN who had definitive pathological results and were admitted to Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University from January 2021 to December 2023.Patients were randomly divided into training and test cohorts at a ratio of 7:3.Ultrasonic image features of PCTN were evaluated,and independent risk factors were identified using multivariate logistic regression analysis,with the area under the curve(AUC)subsequently calculated.Additionally,five different pre-trained models-Inception_v3,EfficientNet,VGG19,ResNet50,and DenseNet121-were selected for transfer learning after data preprocessing using the PyTorch framework in Python.The AUC values of these models were calculated and compared.Results Solid portion greater than 50%,eccentric acute angle,ill-defined margin,spiculated or microlobulated margin,rim calcification,and microcalcification exhibited statistically significant differences(P<0.05)in distinguishing between benign and malignant PCTN.The AUC value derived from these independent risk factors was 0.843.Furthermore,among the five transfer learning models evaluated,the ResNet50 model demonstrated the highest diagnostic efficiency,achieving an AUC value of 0.903 2.Conclusion The ultrasound-based transfer learning artificial intelligence model demonstrated superior performance compared to traditional ultrasound image evaluation methods,enabling accurate prediction of the nature of PCTN and thereby reducing unnecessary ultrasound-guided fine needle biopsies.
