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Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer

 Jiyoung Yoon  ;  Eunjung Lee  ;  Ja Seung Koo  ;  Jung Hyun Yoon  ;  Kee-Hyun Nam  ;  Jandee Lee  ;  Young Suk Jo  ;  Hee Jung Moon  ;  Vivian Youngjean Park  ;  Jin Young Kwak 
 PLOS ONE, Vol.15(11) : e0242806, 2020-11 
Journal Title
Issue Date
Adult ; Aged ; Artificial Intelligence* ; Carcinoma, Papillary / diagnosis ; Carcinoma, Papillary / epidemiology ; Carcinoma, Papillary / genetics* ; Carcinoma, Papillary / pathology ; Diagnosis, Computer-Assisted ; Female ; Humans ; Male ; Middle Aged ; Mutation / genetics ; Proto-Oncogene Proteins B-raf / genetics* ; Thyroid Gland / diagnostic imaging ; Thyroid Gland / pathology ; Thyroid Neoplasms / diagnostic imaging ; Thyroid Neoplasms / epidemiology ; Thyroid Neoplasms / genetics* ; Thyroid Neoplasms / pathology ; Thyroid Nodule ; Tomography, X-Ray Computed
Purpose: To investigate whether a computer-aided diagnosis (CAD) program developed using the deep learning convolutional neural network (CNN) on neck US images can predict the BRAFV600E mutation in thyroid cancer. Methods: 469 thyroid cancers in 469 patients were included in this retrospective study. A CAD program recently developed using the deep CNN provided risks of malignancy (0-100%) as well as binary results (cancer or not). Using the CAD program, we calculated the risk of malignancy based on a US image of each thyroid nodule (CAD value). Univariate and multivariate logistic regression analyses were performed including patient demographics, the American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TIRADS) categories and risks of malignancy calculated through CAD to identify independent predictive factors for the BRAFV600E mutation in thyroid cancer. The predictive power of the CAD value and final multivariable model for the BRAFV600E mutation in thyroid cancer were measured using the area under the receiver operating characteristic (ROC) curves. Results: In this study, 380 (81%) patients were positive and 89 (19%) patients were negative for the BRAFV600E mutation. On multivariate analysis, older age (OR = 1.025, p = 0.018), smaller size (OR = 0.963, p = 0.006), and higher CAD value (OR = 1.016, p = 0.004) were significantly associated with the BRAFV600E mutation. The CAD value yielded an AUC of 0.646 (95% CI: 0.576, 0.716) for predicting the BRAFV600E mutation, while the multivariable model yielded an AUC of 0.706 (95% CI: 0.576, 0.716). The multivariable model showed significantly better performance than the CAD value alone (p = 0.004). Conclusion: Deep learning-based CAD for thyroid US can help us predict the BRAFV600E mutation in thyroid cancer. More multi-center studies with more cases are needed to further validate our study results.
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1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kwak, Jin Young(곽진영) ORCID logo https://orcid.org/0000-0002-6212-1495
Koo, Ja Seung(구자승) ORCID logo https://orcid.org/0000-0003-4546-4709
Nam, Kee Hyun(남기현) ORCID logo https://orcid.org/0000-0002-6852-1190
Moon, Hee Jung(문희정) ORCID logo https://orcid.org/0000-0002-5643-5885
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, Jan Dee(이잔디) ORCID logo https://orcid.org/0000-0003-4090-0049
Jo, Young Suk(조영석) ORCID logo https://orcid.org/0000-0001-9926-8389
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