Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program

Xiaonan Cui, Sunyi Zheng, Marjolein A Heuvelmans, Yihui Du, Grigory Sidorenkov, Shuxuan Fan, Yanju Li, Yongsheng Xie, Zhongyuan Zhu, Monique D Dorrius, Yingru Zhao, Raymond N J Veldhuis, Geertruida H de Bock, Matthijs Oudkerk, Peter M A van Ooijen, Rozemarijn Vliegenthart, Zhaoxiang Ye

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Objective: To evaluate the performance of a deep learning-based computer-aided detection (DL-CAD) system in a Chinese low-dose CT (LDCT) lung cancer screening program.

Materials and methods: One-hundred-and-eighty individuals with a lung nodule on their baseline LDCT lung cancer screening scan were randomly mixed with screenees without nodules in a 1:1 ratio (total: 360 individuals). All scans were assessed by double reading and subsequently processed by an academic DL-CAD system. The findings of double reading and the DL-CAD system were then evaluated by two senior radiologists to derive the reference standard. The detection performance was evaluated by the Free Response Operating Characteristic curve, sensitivity and false-positive (FP) rate. The senior radiologists categorized nodules according to nodule diameter, type (solid, part-solid, non-solid) and Lung-RADS.

Results: The reference standard consisted of 262 nodules >= 4 mm in 196 individuals; 359 findings were considered false positives. The DL-CAD system achieved a sensitivity of 90.1% with 1.0 FP/scan for detection of lung nodules regardless of size or type, whereas double reading had a sensitivity of 76.0% with 0.04 FP/scan (P = 0.001). The sensitivity for detection of nodules >= 4 -

Conclusions: The DL-CAD system can accurately detect pulmonary nodules on LDCT, with an acceptable false-positive rate of 1 nodule per scan and has higher detection performance than double reading. This DL-CAD system may assist radiologists in nodule detection in LDCT lung cancer screening.

Original languageEnglish
Article number110068
Number of pages7
JournalEuropean Journal of Radiology
Early online date24-Nov-2021
Publication statusPublished - Jan-2022


  • Computer-assisted diagnosis
  • Early detection of cancer
  • Pulmonary nodules
  • Artificial intelligence
  • Computed tomography
  • CT
  • CAD

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