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Open AccessArticle

Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers

1
Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si 13620, Korea
2
AI Research Group, Monitor Corporation, Seoul 06628, Korea
3
Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul 07061, Korea
4
Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si 13620, Korea
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(12), 3908; https://doi.org/10.3390/jcm9123908
Received: 5 November 2020 / Revised: 25 November 2020 / Accepted: 29 November 2020 / Published: 2 December 2020
(This article belongs to the Special Issue Lung Cancer: Symptoms, Treatment, and Early Diagnosis)
We aimed to analyse the CT examinations of the previous screening round (CTprev) in NLST participants with incidence lung cancer and evaluate the value of DL-CAD in detection of missed lung cancers. Thoracic radiologists reviewed CTprev in participants with incidence lung cancer, and a DL-CAD analysed CTprev according to NLST criteria and the lung CT screening reporting & data system (Lung-RADS) classification. We calculated patient-wise and lesion-wise sensitivities of the DL-CAD in detection of missed lung cancers. As per the NLST criteria, 88% (100/113) of CTprev were positive and 74 of them had missed lung cancers. The DL-CAD reported 98% (98/100) of the positive screens as positive and detected 95% (70/74) of the missed lung cancers. As per the Lung-RADS classification, 82% (93/113) of CTprev were positive and 60 of them had missed lung cancers. The DL-CAD reported 97% (90/93) of the positive screens as positive and detected 98% (59/60) of the missed lung cancers. The DL-CAD made false positive calls in 10.3% (27/263) of controls, with 0.16 false positive nodules per scan (41/263). In conclusion, the majority of CTprev in participants with incidence lung cancers had missed lung cancers, and the DL-CAD detected them with high sensitivity and a limited false positive rate. View Full-Text
Keywords: lung neoplasms; deep learning; computer-aided diagnosis; multidetector computed tomography; early detection of cancer lung neoplasms; deep learning; computer-aided diagnosis; multidetector computed tomography; early detection of cancer
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MDPI and ACS Style

Cho, J.; Kim, J.; Lee, K.J.; Nam, C.M.; Yoon, S.H.; Song, H.; Kim, J.; Choi, Y.R.; Lee, K.H.; Lee, K.W. Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers. J. Clin. Med. 2020, 9, 3908. https://doi.org/10.3390/jcm9123908

AMA Style

Cho J, Kim J, Lee KJ, Nam CM, Yoon SH, Song H, Kim J, Choi YR, Lee KH, Lee KW. Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers. Journal of Clinical Medicine. 2020; 9(12):3908. https://doi.org/10.3390/jcm9123908

Chicago/Turabian Style

Cho, Jungheum; Kim, Jihang; Lee, Kyong J.; Nam, Chang M.; Yoon, Sung H.; Song, Hwayoung; Kim, Junghoon; Choi, Ye R.; Lee, Kyung H.; Lee, Kyung W. 2020. "Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers" J. Clin. Med. 9, no. 12: 3908. https://doi.org/10.3390/jcm9123908

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