医学
结核(地质)
尤登J统计
放射科
核医学
计算机断层摄影术
肺孤立结节
接收机工作特性
内科学
生物
古生物学
作者
Wenhui Lv,Wang Yang,Changsheng Zhou,Mei Yuan,Minxia Pang,Xiangming Fang,Qirui Zhang,Chuxi Huang,Xinyu Li,Zhen Zhou,Yao Yu,Yizhou Wang,Mengjie Lu,Qiang Xu,Xiuli Li,Haoliang Lin,Xiaofan Lu,Jing Sun,Yuxia Tang,Fangrong Yan,Bing Zhang,Zhen Cheng,Longjiang Zhang,Guangming Lu
出处
期刊:Lung Cancer
[Elsevier]
日期:2021-05-01
卷期号:155: 78-86
被引量:16
标识
DOI:10.1016/j.lungcan.2021.03.008
摘要
Abstract
Purpose
To propose a practical strategy for the clinical application of deep learning algorithm, i.e., Hierarchical-Ordered Network-ORiented Strategy (HONORS), and a new approach to pulmonary nodule classification in various clinical scenarios, i.e., Filter-Guided Pyramid NETwork (FGP-NET). Materials and methods
We developed and validated FGP-NET on a collection of 2106 pulmonary nodules on computed tomography images which combined screened and clinically detected nodules, and performed external test (n = 341). The area under the curves (AUCs) of FGP-NET were assessed. A comparison study with a group of 126 skilled radiologists was conducted. On top of FGP-NET, we built up our HONORS which was composed of two solutions. In the Human Free Solution, we used the high sensitivity operating point for screened nodules, but the high specificity operating point for clinically detected nodules. In the Human-Machine Coupling Solution, we used the Youden point. Results
FGP-NET achieved AUCs of 0.969 and 0.847 for internal and external test. The AUCs of the subsets of the external test set ranged from 0.890 to 0.942. The average sensitivity and specificity of the 126 radiologists were 72.2 ± 15.1 % and 71.7 ± 15.5 %, respectively, while a higher sensitivity (93.3 %) but a relatively inferior specificity (64.0 %) were achieved by FGP-NET. HONORS-guided FGP-NET identified benign nodules with high sensitivity (sensitivity,95.5 %; specificity, 72.5 %) in the screened nodules, and identified malignant nodules with high specificity (sensitivity, 31.0 %; specificity, 97.5 %) in the clinically detected nodules. These nodules could be reliably diagnosed without any intervention from radiologists, via the Human Free Solution. The remaining ambiguous nodules were diagnosed with high performance, which however required manual confirmation by radiologists, via the Human-Machine Coupling Solution. Conclusions
FGP-NET performed comparably to skilled radiologists in terms of diagnosing pulmonary nodules. HONORS, due to its high performance, might reliably contribute a second opinion, aiding in optimizing the clinical workflow.