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Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs

Eui JEui Jin Hwang et al. — Radiology



Abstract

A deep learning–based computer-aided detection system improved the diagnostic yield for newly visible metastasis on chest radiographs in patients with cancer with a similar false-referral rate.




Background

A computer-aided detection (CAD) system may help surveillance for pulmonary metastasis at chest radiography in situations where there is limited access to CT.




Purpose

To evaluate whether a deep learning (DL)–based CAD system can improve diagnostic yield for newly visible lung metastasis on chest radiographs in patients with cancer.




Materials and Methods

A regulatory-approved CAD system for lung nodules was implemented to interpret chest radiographs from patients referred by the medical oncology department in clinical practice. In this retrospective diagnostic cohort study, chest radiographs interpreted with assistance from a CAD system after the implementation (January to April 2019, CAD-assisted interpretation group) and those interpreted before the implementation (September to December 2018, conventional interpretation group) of the CAD system were consecutively included. The diagnostic yield (frequency of true-positive detections) and false-referral rate (frequency of false-positive detections) of formal reports of chest radiographs for newly visible lung metastasis were compared between the two groups using generalized estimating equations. Propensity score matching was performed between the two groups for age, sex, and primary cancer.




Results

A total of 2916 chest radiographs from 1521 patients (1546 men, 1370 women; mean age, 62 years) and 5681 chest radiographs from 3456 patients (2941 men, 2740 women; mean age, 62 years) were analyzed in the CAD-assisted interpretation and conventional interpretation groups, respectively. The diagnostic yield for newly visible metastasis was higher in the CAD-assisted interpretation group (0.86%, 25 of 2916 [95% CI: 0.58, 1.3] vs 0.32%, 18 of 568 [95% CI: 0.20, 0.50%]; P = .004). The false-referral rate in the CAD-assisted interpretation group (0.34%, 10 of 2916 [95% CI: 0.19, 0.64]) was not inferior to that in the conventional interpretation group (0.25%, 14 of 5681 [95% CI: 0.15, 0.42]) at the noninferiority margin of 0.5% (95% CI of difference: –0.15, 0.35).




Conclusion

A deep learning–based computer-aided detection system improved the diagnostic yield for newly visible metastasis on chest radiographs in patients with cancer with a similar false-referral rate.

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AUTHORS

Eui JEui Jin Hwang, Jeong Su Lee, Jong Hyuk Lee, Woo Hyeon Lim, Jae Hyun Kim, Kyu Sung Choi, Tae Won Choi, Tae-Hyung Kim, Jin Mo Goo, Chang Min Parke Hyun Kim, Kyu Sung Choi, Tae Won Choi, Tae-Hyung Kim, Jin Mo Goo, Chang Min Park

From the Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., J.S.L., J.H.L., W.H.L., J.H.K., K.S.C., T.W.C., T.H.K., J.M.G., C.M.P.); Department of Radiology, Namwon Medical Center, Namwon, Korea (W.H.L.); Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (K.S.C.); and Department of Radiology, Naval Pohang Hospital, Pohang, Korea (T.H.K.).

PUBLISHED
Radiology

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