Open AccessArtificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer
Author(s) -
Jungmin Yoon,
Eunjung Lee,
Ja Seung Koo,
Jung Hyun Yoon,
KeeHyun Nam,
Jandee Lee,
Young Suk Jo,
Hee Jung Moon,
Vivian Youngjean Park,
Jin Young Kwak
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0242806
Subject(s) - medicine , thyroid cancer , malignancy , receiver operating characteristic , oncology , cancer , v600e , logistic regression , retrospective cohort study , mutation , biology , genetics , gene
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 BRAF V600E 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 BRAF V600E mutation in thyroid cancer. The predictive power of the CAD value and final multivariable model for the BRAF V600E 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 BRAF V600E 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 BRAF V600E mutation. The CAD value yielded an AUC of 0.646 (95% CI: 0.576, 0.716) for predicting the BRAF V600E 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 BRAF V600E mutation in thyroid cancer. More multi-center studies with more cases are needed to further validate our study results.