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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

DOI https://doi.org/10.2147/CMAR.S239927

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
; +86-13813810516
Fax +86-02568136861
Email yuanmeijiangsu@163.com; njmu_ytf@163.com

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

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