| Autor: |
Jun Shao, Gang Wang, Le Yi, Chengdi Wang, Tianzhong Lan, Xiuyuan Xu, Jixiang Guo, Taibing Deng, Dan Liu, Bojiang Chen, Zhang Yi, Weimin Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Frontiers in Bioscience-Landmark, Vol 27, Iss 7, p 212 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2768-6701 |
| DOI: |
10.31083/j.fbl2707212 |
| Popis: |
Background: Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability, especially in resource-limited areas. The combination of mobile CT and deep learning technique has inspired innovations in the routine clinical practice. Methods: This study recruited participants prospectively in two rural sites of western China. A deep learning system was developed to assist clinicians to identify the nodules and evaluate the malignancy with state-of-the-art performance assessed by recall, free-response receiver operating characteristic curve (FROC), accuracy (ACC), area under the receiver operating characteristic curve (AUC). Results: This study enrolled 12,360 participants scanned by mobile CT vehicle, and detected 9511 (76.95%) patients with pulmonary nodules. Majority of participants were female (8169, 66.09%), and never-smokers (9784, 79.16%). After 1-year follow-up, 86 patients were diagnosed with lung cancer, with 80 (93.03%) of adenocarcinoma, and 73 (84.88%) at stage I. This deep learning system was developed to detect nodules (recall of 0.9507; FROC of 0.6470) and stratify the risk (ACC of 0.8696; macro-AUC of 0.8516) automatically. Conclusions: A novel model for lung cancer screening, the integration mobile CT with deep learning, was proposed. It enabled specialists to increase the accuracy and consistency of workflow and has potential to assist clinicians in detecting early-stage lung cancer effectively. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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