https://www.ncbi.nlm.nih.gov/pubmed/31287391
Radiology. 2019 Jul 9:181343. doi: 10.1148/radiol.2019181343. [Epub ahead of print]
Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists.
Buda M1, Wildman-Tobriner B1, Hoang JK1, Thayer D1, Tessler FN1, Middleton WD1, Mazurowski MA1.
Author information
1From the Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC 27705 (M.B., B.W.T., J.K.H., M.A.M.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (D.T., W.D.M.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (F.N.T.); and Department of Electrical and Computer Engineering, Duke University, Durham, NC (M.A.M.).
Abstract
Background Management of thyroid nodules may be inconsistent between different observers and time consuming for radiologists. An artificial intelligence system that uses deep learning may improve radiology workflow for management of thyroid nodules. Purpose To develop a deep learning algorithm that uses thyroid US images to decide whether a thyroid nodule should undergo a biopsy and to compare the performance of the algorithm with the performance of radiologists who adhere to American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS). Materials and Methods In this retrospective analysis, studies in patients referred for US with subsequent fine-needle aspiration or with surgical histologic analysis used as the standard were evaluated. The study period was from August 2006 to May 2010. A multitask deep convolutional neural network was trained to provide biopsy recommendations for thyroid nodules on the basis of two orthogonal US images as the input. In the training phase, the deep learning algorithm was first evaluated by using 10-fold cross-validation. Internal validation was then performed on an independent set of 99 consecutive nodules. The sensitivity and specificity of the algorithm were compared with a consensus of three ACR TI-RADS committee experts and nine other radiologists, all of whom interpreted thyroid US images in clinical practice. Results Included were 1377 thyroid nodules in 1230 patients with complete imaging data and conclusive cytologic or histologic diagnoses. For the 99 test nodules, the proposed deep learning algorithm achieved 13 of 15 (87%: 95% confidence interval [CI]: 67%, 100%) sensitivity, the same as expert consensus (P > .99) and higher than five of nine radiologists. The specificity of the deep learning algorithm was 44 of 84 (52%; 95% CI: 42%, 62%), which was similar to expert consensus (43 of 84; 51%; 95% CI: 41%, 62%; P = .91) and higher than seven of nine other radiologists. The mean sensitivity and specificity for the nine radiologists was 83% (95% CI: 64%, 98%) and 48% (95% CI: 37%, 59%), respectively. Conclusion Sensitivity and specificity of a deep learning algorithm for thyroid nodule biopsy recommendations was similar to that of expert radiologists who used American College of Radiology Thyroid Imaging and Reporting Data System guidelines.
© RSNA, 2019.
PMID: 31287391 DOI: 10.1148/radiol.2019181343
초음파, 갑상선, 결절, 딥러닝, 영상의학, 영상의학과, 의사, 수행률, 향상, CNN, 미국,
