Lung Nodule Detection using Deep Learning
Principal Investigator
Name
Vismantas Dilys
Degrees
MSc
Institution
Canon Medical Research
Position Title
Scientist
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-388
Initial CDAS Request Approval
Jan 25, 2018
Title
Lung Nodule Detection using Deep Learning
Summary
Lung cancer is the leading cause of cancer death worldwide [1]. The National Lung Screening Trial [2] showed a reduction of 20% in lung cancer mortality in high-risk subjects scanned with low-dose Computed Tomography (CT), compared to the control group that received chest radiography. As a consequence of this result, lung cancer screening programs with low-dose CT imaging are being implemented in the US. The implementation of screening would mean a significant increase of reading effort for radiologists.
We propose training of a deep neural networks based CAD system to make lung cancer screening more cost-effective [3-5].
[1] Cancer Facts and Figures 2014 Am. Cancer Soc., 2014 [Online]. Available: http://www.cancer.org/acs/groups/cid/documents/webcontent/003115-pdf.pdf
[2] D. R. Aberle et al., “Reduced lung-cancer mortality with low-dose computed tomographic screening,” N. Eng. J. Med., vol. 365, pp. 395–409, 2011.
[3] White, C.S., Pugatch, R., Koonce, T., Rust, S.W. and Dharaiya, E., 2008. Lung nodule CAD software as a second reader: a multicenter study. Academic radiology, 15(3), pp.326-333.
[4] Sahiner, B., Chan, H.P., Hadjiiski, L.M., Cascade, P.N., Kazerooni, E.A., Chughtai, A.R., Poopat, C., Song, T., Frank, L., Stojanovska, J. and Attili, A., 2009. Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size. Academic radiology, 16(12), pp.1518-1530.
[5] Yuan, R., Vos, P.M. and Cooperberg, P.L., 2006. Computer-aided detection in screening CT for pulmonary nodules. American Journal of Roentgenology, 186(5), pp.1280-1287.
We propose training of a deep neural networks based CAD system to make lung cancer screening more cost-effective [3-5].
[1] Cancer Facts and Figures 2014 Am. Cancer Soc., 2014 [Online]. Available: http://www.cancer.org/acs/groups/cid/documents/webcontent/003115-pdf.pdf
[2] D. R. Aberle et al., “Reduced lung-cancer mortality with low-dose computed tomographic screening,” N. Eng. J. Med., vol. 365, pp. 395–409, 2011.
[3] White, C.S., Pugatch, R., Koonce, T., Rust, S.W. and Dharaiya, E., 2008. Lung nodule CAD software as a second reader: a multicenter study. Academic radiology, 15(3), pp.326-333.
[4] Sahiner, B., Chan, H.P., Hadjiiski, L.M., Cascade, P.N., Kazerooni, E.A., Chughtai, A.R., Poopat, C., Song, T., Frank, L., Stojanovska, J. and Attili, A., 2009. Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size. Academic radiology, 16(12), pp.1518-1530.
[5] Yuan, R., Vos, P.M. and Cooperberg, P.L., 2006. Computer-aided detection in screening CT for pulmonary nodules. American Journal of Roentgenology, 186(5), pp.1280-1287.
Aims
* Develop a deep neural networks based CAD system to decet lung nodules.
* Asses system performance in detecting nodules and classifying them as benign or malignant.
* Asses system robustness by using similar data bases for testing such as LIDC-IDRI
Collaborators
Vismantas Dilys, Scientist, Canon Medical Research Europe Ltd.
Erin Beveridge, Clinical Analyst, Canon Medical Research Europe Ltd.
Keith Goatman, Principal Scientist, Canon Medical Research Europe Ltd.