Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program
| Autor: | Zhaoxiang Ye, Geertruida H. de Bock, Xiaonan Cui, Monique D. Dorrius, Zhongyuan Zhu, Yihui Du, Raymond N.J. Veldhuis, Sunyi Zheng, Shuxuan Fan, Yongsheng Xie, Rozemarijn Vliegenthart, Grigory Sidorenkov, Peter M. A. van Ooijen, Yingru Zhao, Yanju Li, Matthijs Oudkerk, Marjolein A Heuvelmans |
|---|---|
| Přispěvatelé: | Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE), Life Course Epidemiology (LCE), Damage and Repair in Cancer Development and Cancer Treatment (DARE), Cardiovascular Centre (CVC), Datamanagement & Biometrics |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Oncology
medicine.medical_specialty Nodule detection China Artificial intelligence Lung Neoplasms COMPUTER-AIDED DETECTION Computer-assisted diagnosis IMAGES UT-Hybrid-D Sensitivity and Specificity VALIDATION Deep Learning Internal medicine medicine Humans Radiology Nuclear Medicine and imaging CAD PULMONARY NODULES Lung Computed tomography POPULATION business.industry Deep learning Reproducibility of Results Solitary Pulmonary Nodule General Medicine FALSE-POSITIVE REDUCTION medicine.anatomical_structure Early detection of cancer 2023 OA procedure AUTOMATIC DETECTION Radiographic Image Interpretation Computer-Assisted business Tomography X-Ray Computed Lung cancer screening CT |
| Zdroj: | European Journal of Radiology, 146:110068. ELSEVIER IRELAND LTD European journal of radiology, 146:110068. Elsevier |
| ISSN: | 0720-048X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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