Recognition of Peripheral Lung Cancer and Focal Pneumonia on Chest Computed Tomography Images Based on Convolutional Neural Network

doi:10.1177/15330338221085375

	Recognition of Peripheral Lung Cancer and Focal Pneumonia on Chest Computed Tomography Images Based on Convolutional Neural Network
	Cheng, Xiaoyue 1; He Wen 1; Hao You 2,3; Li Hua 2; Wu Xiaohua 1; Cao Qiuting 1; Liu Jiabao 1
	2022-03-01
发表期刊	TECHNOLOGY IN CANCER RESEARCH & TREATMENT
ISSN	1533-0346
卷号	21 页码:12
摘要	Introduction: Chest computed tomography (CT) is important for the early screening of lung diseases and clinical diagnosis, particularly during the COVID-19 pandemic. We propose a method for classifying peripheral lung cancer and focal pneumonia on chest CT images and undertake 5 window settings to study the effect on the artificial intelligence processing results. Methods: A retrospective collection of CT images from 357 patients with peripheral lung cancer having solitary solid nodule or focal pneumonia with a solitary consolidation was applied. We segmented and aligned the lung parenchyma based on some morphological methods and cropped this region of the lung parenchyma with the minimum 3D bounding box. Using these 3D cropped volumes of all cases, we designed a 3D neural network to classify them into 2 categories. We also compared the classification results of the 3 physicians with different experience levels on the same dataset. Results: We conducted experiments using 5 window settings. After cropping and alignment based on an automatic preprocessing procedure, our neural network achieved an average classification accuracy of 91.596% under a 5-fold cross-validation in the full window, in which the area under the curve (AUC) was 0.946. The classification accuracy and AUC value were 90.48% and 0.957 for the junior physician, 94.96% and 0.989 for the intermediate physician, and 96.92% and 0.980 for the senior physician, respectively. After removing the error prediction, the accuracy improved significantly, reaching 98.79% in the self-defined window2. Conclusion: Using the proposed neural network, in separating peripheral lung cancer and focal pneumonia in chest CT data, we achieved an accuracy competitive to that of a junior physician. Through a data ablation study, the proposed 3D CNN can achieve a slightly higher accuracy compared with senior physicians in the same subset. The self-defined window2 was the best for data training and evaluation.
关键词	chest CT peripheral lung cancer focal pneumonia 3D CNN window settings
DOI	10.1177/15330338221085375
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2017YFB1002703] ; National Key Basic Research Program of China[2015CB554507] ; National Natural Science Foundation of China[61379082]
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000772300800001
出版者	SAGE PUBLICATIONS INC
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18937
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	He Wen
作者单位	1.Capital Med Univ, Beijing Friendship Hosp, Dept Radiol, Beijing 100050, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Cheng, Xiaoyue,He Wen,Hao You,et al. Recognition of Peripheral Lung Cancer and Focal Pneumonia on Chest Computed Tomography Images Based on Convolutional Neural Network[J]. TECHNOLOGY IN CANCER RESEARCH & TREATMENT,2022,21:12.
APA	Cheng, Xiaoyue.,He Wen.,Hao You.,Li Hua.,Wu Xiaohua.,...&Liu Jiabao.(2022).Recognition of Peripheral Lung Cancer and Focal Pneumonia on Chest Computed Tomography Images Based on Convolutional Neural Network.TECHNOLOGY IN CANCER RESEARCH & TREATMENT,21,12.
MLA	Cheng, Xiaoyue,et al."Recognition of Peripheral Lung Cancer and Focal Pneumonia on Chest Computed Tomography Images Based on Convolutional Neural Network".TECHNOLOGY IN CANCER RESEARCH & TREATMENT 21(2022):12.