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Performance of Deep Learning Model in Detecting Operable Lung Cancer With Chest Radiographs

Cha, Min Jae, MD; Chung, Myung Jin, MD, PhD; Lee, Jeong Hyun, MD; Lee, Kyung Soo, MD, PhD

doi: 10.1097/RTI.0000000000000388
Pulmonary/Thoracic
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Purpose: The aim of this study was to evaluate the diagnostic performance of a trained deep convolutional neural network (DCNN) model for detecting operable lung cancer with chest radiographs (CXRs).

Materials and Methods: The institutional review board approved this study. A deep learning model (DLM) based on DCNN was trained with 17,211 CXRs (5700 CT-confirmed lung nodules in 3500 CXRs and 13,711 normal CXRs), finally augmented to 600,000 images. For validation, a trained DLM was tested with 1483 CXRs with surgically resected lung cancer, marked and scored by 2 radiologists. Furthermore, diagnostic performances of DLM and 6 human observers were compared with 500 cases (200 visible T1 lung cancer on CXR and 300 normal CXRs) and analyzed using free-response receiver-operating characteristics curve (FROC) analysis.

Results: The overall detection rate of DLM for resected lung cancers (27.2±14.6 mm) was a sensitivity of 76.8% (1139/1483) with a false positive per image (FPPI) of 0.3 and area under the FROC curve (AUC) of 0.732. In the comparison with human readers, DLM demonstrated a sensitivity of 86.5% at 0.1 FPPI and a sensitivity of 92% at 0.3 FPPI with AUC of 0.899 at an FPPI range of 0.03 to 0.44 for detecting visible T1 lung cancers, which were superior to the average of 6 human readers [mean sensitivity; 78% (range, 71.6% to 82.6%) at an FPPI of 0.1% and 85% (range, 80.2% to 89.2%) at an FPPI of 0.3, AUC of 0.819 (range, 0.754 to 0.862) at an FPPI of 0.03 to 0.44).

Conclusions: A DLM has high diagnostic performance in detecting operable lung cancer with CXR, demonstrating a potential of playing a pivotal role for lung cancer screening.

Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

Present address: Min Jae Cha, MD, Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Korea.

Myung Jin Chung has received funding from Samsung Electronics. The remaining authors declare no conflicts of interest.

Correspondence to: Myung Jin Chung, MD, PhD, Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-Dong, Kangnam-Ku, Seoul 06351, Korea (e-mail: mj1.chung@samsung.com).

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