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Combining radiomics with ultrasound-based risk stratification systems for thyroid nodules: an approach for improving performance

Authors
 Vivian Y Park  ;  Eunjung Lee  ;  Hye Sun Lee  ;  Hye Jung Kim  ;  Jiyoung Yoon  ;  Jinwoo Son  ;  Kijun Song  ;  Hee Jung Moon  ;  Jung Hyun Yoon  ;  Ga Ram Kim  ;  Jin Young Kwak 
Citation
 EUROPEAN RADIOLOGY, Vol.31(4) : 2405-2413, 2021-04 
Journal Title
 EUROPEAN RADIOLOGY 
ISSN
 0938-7994 
Issue Date
2021-04
MeSH
Humans ; Middle Aged ; Retrospective Studies ; Risk Assessment ; Thyroid Neoplasms* / diagnostic imaging ; Thyroid Nodule* / diagnostic imaging ; Ultrasonography ; United States
Keywords
Risk assessment ; Thyroid neoplasms ; Thyroid nodule ; Ultrasonography
Abstract
Objectives: To develop a radiomics score using ultrasound images to predict thyroid malignancy and to investigate its potential as a complementary tool to improve the performance of risk stratification systems. Methods: We retrospectively included consecutive patients who underwent fine-needle aspiration (FNA) for thyroid nodules that were cytopathologically diagnosed as benign or malignant. Nodules were randomly assigned to a training and test set (8:2 ratio). A radiomics score was developed from the training set, and cutoff values based on the maximum Youden index (Rad_maxY) and for 5%, 10%, and 20% predicted malignancy risk (Rad_5%, Rad_10%, Rad_20%, respectively) were applied to the test set. The performances of the American College of Radiology (ACR) and the American Thyroid Association (ATA) guidelines were compared with the combined performances of the guidelines and radiomics score with interpretations from expert and nonexpert readers. Results: A total of 1624 thyroid nodules from 1609 patients (mean age, 50.1 years [range, 18-90 years]) were included. The radiomics score yielded an AUC of 0.85 (95% CI: 0.83, 0.87) in the training set and 0.75 (95% CI: 0.69, 0.81) in the test set (Rad_maxY). When the radiomics score was combined with the ACR or ATA guidelines (Rad_5%), all readers showed increased specificity, accuracy, and PPV and decreased unnecessary FNA rates (all p < .05), with no difference in sensitivity (p > .05). Conclusion: Radiomics help predict thyroid malignancy and improve specificity, accuracy, PPV, and unnecessary FNA rate while maintaining the sensitivity of the ACR and ATA guidelines for both expert and nonexpert readers. Key points: • The radiomics score yielded an AUC of 0.85 and 0.75 in the training and test set, respectively. • For all readers, combining a 5% predicted malignancy risk cutoff for the radiomics score with the ACR and ATA guidelines significantly increased specificity, accuracy, and PPV and decreased unnecessary FNA rates, with no decrease in sensitivity. • Radiomics can help predict malignancy in thyroid nodules in combination with risk stratification systems, by improving specificity, accuracy, and PPV and unnecessary FNA rates while maintaining sensitivity for both expert and nonexpert readers.
Full Text
https://link.springer.com/article/10.1007%2Fs00330-020-07365-9
DOI
10.1007/s00330-020-07365-9
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Kwak, Jin Young(곽진영) ORCID logo https://orcid.org/0000-0002-6212-1495
Kim, Ga Ram(김가람) ORCID logo https://orcid.org/0000-0002-4481-5792
Park, Vivian Youngjean(박영진) ORCID logo https://orcid.org/0000-0002-5135-4058
Yoon, Jung Hyun(윤정현) ORCID logo https://orcid.org/0000-0002-2100-3513
Yoon, Jiyoung(윤지영)
Lee, Hye Sun(이혜선) ORCID logo https://orcid.org/0000-0001-6328-6948
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/182367
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