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*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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 - ≤ 6 mm was significantly higher with DL-CAD than with double reading (86.3% vs. 58.9% respectively; P = 0.001). Sixty-three nodules were only identified by the DL-CAD system, and 27 nodules only found by double reading. The DL-CAD system reached similar performance compared to double reading in Lung-RADS 3 (94.3% vs. 90.0%, P = 0.549) and Lung-RADS 4 nodules (100.0% vs. 97.0%, P = 1.000), but showed a higher sensitivity in Lung-RADS 2 (86.2% vs. 65.4%, P < 0.001). 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
Volume146
Early online date24 Nov 2021
DOIs
Publication statusPublished - Jan 2022

Keywords

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

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