Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network
Authors Xu YM, Zhang T, Xu H, Qi L, Zhang W, Zhang YD, Gao DS, Yuan M, Yu TF
Received 25 November 2019
Accepted for publication 5 April 2020
Published 29 April 2020 Volume 2020:12 Pages 2979—2992
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Antonella D'Anneo
Yi-Ming Xu,1 Teng Zhang,1 Hai Xu,1 Liang Qi,1 Wei Zhang,1 Yu-Dong Zhang,1 Da-Shan Gao,2 Mei Yuan,1 Tong-Fu Yu1
1Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 2 12sigma Technologies, San Diego, California, USA
Correspondence: Mei Yuan; Tong-Fu Yu
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, People’s Republic of China
, 210009 Tel +86-13405835354
Email email@example.com; firstname.lastname@example.org
Purpose: The purpose of this study is to compare the detection performance of the 3-dimensional convolutional neural network (3D CNN)-based computer-aided detection (CAD) models with radiologists of different levels of experience in detecting pulmonary nodules on thin-section computed tomography (CT).
Patients and Methods: We retrospectively reviewed 1109 consecutive patients who underwent follow-up thin-section CT at our institution. The 3D CNN model for nodule detection was re-trained and complemented by expert augmentation. The annotations of a consensus panel consisting of two expert radiologists determined the ground truth. The detection performance of the re-trained CAD model and three other radiologists at different levels of experience were tested using a free-response receiver operating characteristic (FROC) analysis in the test group.
Results: The detection performance of the re-trained CAD model was significantly better than that of the pre-trained network (sensitivity: 93.09% vs 38.44%). The re-trained CAD model had a significantly better detection performance than radiologists (average sensitivity: 93.09% vs 50.22%), without significantly increasing the number of false positives per scan (1.64 vs 0.68). In the training set, 922 nodules less than 3 mm in size in 211 patients at high risk were recommended for follow-up CT according to the Fleischner Society Guidelines. Fifteen of 101 solid nodules were confirmed to be lung cancer.
Conclusion: The re-trained 3D CNN-based CAD model, complemented by expert augmentation, was an accurate and efficient tool in identifying incidental pulmonary nodules for subsequent management.
Keywords: computer-aided detection, computed tomography, pulmonary nodules, convolutional neural network
This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.Download Article [PDF] View Full Text [HTML][Machine readable]