Can a Novel Deep Neural Network Improve the Computer-Aided Detection of Solid Pulmonary Nodules and the Rate of False-Positive Findings in Comparison to an Established Machine Learning Computer-Aided Detection?
Journal
Investigative radiology
ISSN: 1536-0210
Titre abrégé: Invest Radiol
Pays: United States
ID NLM: 0045377
Informations de publication
Date de publication:
01 02 2021
01 02 2021
Historique:
pubmed:
17
8
2020
medline:
16
10
2021
entrez:
16
8
2020
Statut:
ppublish
Résumé
The aim of this study was to compare the performance of 2 approved computer-aided detection (CAD) systems for detection of pulmonary solid nodules (PSNs) in an oncologic cohort. The first CAD system is based on a conventional machine learning approach (VD10F), and the other is based on a deep 3D convolutional neural network (CNN) CAD software (VD20A). Nine hundred sixty-seven patients with a total of 2451 PSNs were retrospectively evaluated using the 2 different CAD systems. All patients had thin-slice chest computed tomography (0.6 mm) using 100 kV and 100 mAs and a high-resolution kernel (I50f). The CAD images generated by VD10F were transferred to the PACS for evaluation. The images generated by VD20A were evaluated using a Web browser-based viewer. Finally, a senior radiologist who was blinded for the CAD results examined the thin-slice images of every patient (ground truth). A total of 2451 PSNs were detected by the senior radiologist. CAD-VD10F detected 1401 true-positive, 143 false-negative, 565 false-positive (FP), and 342 true-negative PSNs, resulting in sensitivity of 90.7%, specificity of 37.7%, positive predictive value of 0.71, and negative predictive value of 0.70. CAD-VD20A detected 1381 true-positive, 163 false-negative, 337 FP, and 570 true-negative PSNs, resulting in sensitivity of 89.4%, specificity of 62.8%, positive predictive value of 0.80, and negative predictive value 0.77, respectively. The rate of FP per scan was 0.6 for CAD-VD10F and 0.3 for CAD-VD20A. The new deep learning-based CAD software (VD20A) shows similar sensitivity with the conventional CAD software (VD10F), but a significantly higher specificity.
Identifiants
pubmed: 32796198
pii: 00004424-202102000-00005
doi: 10.1097/RLI.0000000000000713
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
103-108Informations de copyright
Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of interest and sources of funding: M.S.H. has received institutional research support from Siemens Healthineers Germany and GE USA. He is a scientific advisor of Siemens Healthineers Germany and has received speaker’s honorarium from Siemens Healthineers Germany and GE USA. R.G. is an employee of Siemens Healthcare AG, Germany. For the remaining authors, none were declared.
Références
Fraioli F, Serra G, Passariello R. CAD (computed-aided detection) and CADx (computer aided diagnosis) systems in identifying and characterising lung nodules on chest CT: overview of research, developments and new prospects. Radiol Med . 2010;115:385–3402.
Fraioli F, Bertoletti L, Napoli A, et al. Computer-aided detection (CAD) in lung cancer screening at chest MDCT: ROC analysis of CAD versus radiologist performance. J Thorac Imaging . 2007;22:241–246.
Hirose T, Nitta N, Shiraishi J, et al. Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy. Acad Radiol . 2008;15:1505–1512.
Nadealian Z, Nazari B, Sadri S, et al. Detection of pulmonary nodules in low-dose computed tomography using localized active contours and shape features. J Med Signals Sens . 2017;7:203–212.
Marten K, Engelke C. Computer-aided detection and automated CT volumetry of pulmonary nodules. Eur Radiol . 2007;17:888–901.
Christe A, Peters AA, Drakopoulos D, et al. Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images. Invest Radiol . 2019;54:627–632.
Lell MM, Kachelrieß M. Recent and upcoming technological developments in computed tomography: high speed, low dose, deep learning, multienergy. Invest Radiol . 2020;55:8–19.
Halder A, Dey D, Sadhu AK. Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review. J Digit Imaging . 2020;33:655–677.
Lin JS, Ligomenides P, Lo SC, et al. An application of convolution neural networks: reducing false-positives in lung nodule detection. In: Proceedings of 1994 I.E. Nuclear Science Symposium - NSS'94 . Norfolk, VA, IEEE; 1994:1842–1846.
Paul R, Hawkins SH, Schabath MB, et al. Predicting malignant nodules by fusing deep features with classical radiomics features. J Med Imaging (Bellingham) . 2018;5:11021.
Godoy MC, Kim TJ, White CS, et al. Benefit of computer-aided detection analysis for the detection of subsolid and solid lung nodules on thin- and thick-section CT. AJR Am J Roentgenol . 2013;200:74–83.
Zhao Y, de Bock GH, Vliegenthart R, et al. Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume. Eur Radiol . 2012;22:2076–2084.
Zhao Y, Zhao L, Yan Z, et al. A deep-learning based automatic pulmonary nodule detection system. Medical Imaging 2018: Computer-Aided Diagnosis. Vol. 10575. International Society for Optics and Photonics,2018.
University Medical Center Freiburg. Nora Medical Imaging Platform Project. 2020. Available at: http://www.nora-imaging.org/ . Accessed March 2020.
Rubin GD, Lyo JK, Paik DS, et al. Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. Radiology . 2005;234:274–283.
Awai K, Murao K, Ozawa A, et al. Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists’ detection performance. Radiology . 2004;230:347–352.
Kim JS, Kim JH, Cho G, et al. Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval—initial results. Radiology . 2005;236:295–299.
Brown MS, Goldin JG, Rogers S, et al. Computer-aided lung nodule detection in CT: results of large-scale observer test. Acad Radiol . 2005;12:681–686.
Goo JM, Lee JW, Lee HJ, et al. Automated lung nodule detection at low-dose CT: preliminary experience. Korean J Radiol . 2003;4:211–216.
Beyer F, Zierott L, Fallenberg EM, et al. Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader. Eur Radiol . 2007;17:2941–2947.
Beigelman-Aubry C, Raffy P, Yang W, et al. Computer-aided detection of solid lung nodules on follow-up MDCT screening: evaluation of detection, tracking, and reading time. Am J Roentgenol . 2007;189:948–955.
Li L, Liu Z, Huang H, et al. Evaluating the performance of a deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing lung nodules: comparison with the performance of double reading by radiologists. Thorac Cancer . 2019;10:183–192.
Kido S, Hirano Y, Mabu S. Deep learning for pulmonary image analysis: classification, detection, and segmentation. In: Lee G, Fujita H, eds. Deep Learning in Medical Image Analysis . Cham, Switzerland: Springer International Publishing; 2020:47–58.
Huang W, Xue Y, Wu Y. A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning. PloS One . 2019;14:e0219369.
Wang Q, Shen F, Shen L, et al. Lung nodule detection in CT images using a raw patch-based convolutional neural network. J Digit Imaging . 2019;32:971–979.
Ali I, Hart GR, Gunabushanam G, et al. Lung nodule detection via deep reinforcement learning. Front Oncol . 2018;8:108.
Xu YM, Zhang T, Xu H, et al. Deep learning in CT images: automated pulmonary nodule detection for subsequent management using convolutional neural network. Cancer Manag Res . 2020;12:2979–2992.
Masood A, Yang P, Sheng B, et al. Cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT. IEEE J Transl Eng Health Med . 2020;8:4300113.
Pezeshk A, Hamidian S, Petrick N, et al. 3-D convolutional neural networks for automatic detection of pulmonary nodules in chest CT. IEEE J Biomed Health Inform . 2019;23:2080–2090.
Jia T, Zhang H, Meng H. A novel lung nodules detection scheme based on vessel segmentation on CT images. Biomed Mater Eng . 2014;24:3179–3186.
Gurcan MN, Sahiner B, Petrick N, et al. Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med Phys . 2002;29:2552–2558.
Zheng S, Guo J, Cui X, et al. Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection. IEEE Trans Med Imaging . 2020;39:797–805.
Mehre SA, Mukhopadhyay S, Dutta A, et al. An automated lung nodule detection system for CT images using synthetic minority oversampling. In: Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis . San Diego, CA: SPIE Medical Imaging; 2016:97850H.
Javaid M, Javid M, Rehman MZU, et al. A novel approach to CAD system for the detection of lung nodules in CT images. Comput Methods Programs Biomed . 2016;135:125–139.
Choi WJ, Choi TS. Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput Methods Programs Biomed . 2014;113:37–54.
Zhang W, Wang X, Li X, et al. 3D skeletonization feature based computer-aided detection system for pulmonary nodules in CT datasets. Comput Biol Med . 2018;92:64–72.
Gong J, Liu JY, Wang LJ, et al. Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis. Phys Med . 2018;46:124–133.
Han H, Li L, Han F, et al. Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme. IEEE J Biomed Health Inform . 2015;19:648–659.
Messay T, Hardie RC, Rogers SK. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal . 2010;14:390–406.
Nithila EE, Kumar S. Automatic detection of solitary pulmonary nodules using swarm intelligence optimized neural networks on CT images. Eng Sci Tech Int J . 2017;20:1192–1202.