Validation of a deep learning-based computer-aided system for lung nodule detection in a Chinese lung cancer screening program

Xiaonan Cui, Sunyi Zheng, Marjolein A. Heuvelmans, Yihui Du, Grigory Sidorenkov, Monique D. Dorrius, Raymond N.J. Veldhuis, Matthijs Oudkerk, Geertruida H. de Bock, Peter M.A. van Ooijen, Rozemarijn Vliegenthart, Zhaoxiang Ye

Research output: Contribution to journalArticleAcademicpeer-review


Background: Previously, we developed a deep learning-based computer-aided detection (DL-CAD) system based on the CT scans from the U.S. The purpose of this study was to validate its effectiveness for automatic pulmonary nodule detection in a Chinese lung cancer screening program.

Methods: The proprietary DL-CAD system (MIPNOD 1.0) was pre-trained on the public LIDC-IDRI dataset with 888 CT scans. There were 360 low-dose CT scans that were retrospectively collected from a Chinese lung cancer screening project and evaluated independently by radiologists in a double reading fashion and the DL-CAD system. An extra senior radiologist checked all the results and made the consensus as to the reference standard. Free-response Receiver operating characteristic analysis was applied to assess the detection performance of the DL-CAD system.

Results: After making the consensus, there were 262 nodules in 196 participants and 164 scans without nodules. The DL-CAD system achieved a sensitivity of 89.3% with one false positive per scan, while radiologists had a sensitivity of 76.0% for detection during double reading. Among all undetected nodules, only two were missed by both radiologists and the DL-CAD system. The comparison of detection performance between the DL-CAD system and radiologists in nodule types was as follows. (1) solid nodules (89.7% vs 76.8%; P = 0.003). (2) part-solid nodules (90.9% vs 77.3%; P = 0.375). (3) non-solid nodules (87.3% vs 72.7%; P = 0.134).

Conclusion: The DL-CAD system showed good performance of pulmonary nodule detection in a Chinese population and could potentially provide assistance for radiologists in lung cancer screening programs.
Original languageEnglish
Article number4168
JournalEuropean respiratory journal
Issue number64
Publication statusPublished - 28 Oct 2020
EventERS International Congress 2020 - Virtual
Duration: 7 Sept 20209 Sept 2020


  • deep learning
  • Lung cancer
  • screening program
  • lung nodule detection


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