Open AccessToward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network
Author(s) -
Zhang Chao,
Sun Xing,
Dang Kang,
Li Ke,
Guo Xiaowei,
Chang Jia,
Yu Zongqiao,
Huang Feiyue,
Wu Yunsheng,
Liang Zhu,
Liu Zaiyi,
Zhang Xuegong,
Gao Xinglin,
Huang Shaohong,
Qin Jie,
Feng Weineng,
Zhou Tao,
Zhang Yanbin,
Fang Weijun,
Zhao Mingfang,
Yang Xuening,
Zhou Qing,
Wu Yilong,
Zhong Wenzhao
Publication year - 2019
Publication title -
the oncologist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.176
H-Index - 164
eISSN - 1549-490X
pISSN - 1083-7159
DOI - 10.1634/theoncologist.2018-0908
Subject(s) - convolutional neural network , medicine , deep learning , artificial intelligence , lung cancer , nodule (geology) , artificial neural network , radiology , solitary pulmonary nodule , pattern recognition (psychology) , clinical practice , sensitivity (control systems) , computed tomography , machine learning , computer science , pathology , paleontology , family medicine , electronic engineering , engineering , biology
Background Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well‐trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images. Materials and Methods Open‐source data sets and multicenter data sets have been used in this study. A three‐dimensional convolutional neural network (CNN) was designed to detect pulmonary nodules and classify them into malignant or benign diseases based on pathologically and laboratory proven results. Results The sensitivity and specificity of this well‐trained model were found to be 84.4% (95% confidence interval [CI], 80.5%–88.3%) and 83.0% (95% CI, 79.5%–86.5%), respectively. Subgroup analysis of smaller nodules (<10 mm) have demonstrated remarkable sensitivity and specificity, similar to that of larger nodules (10–30 mm). Additional model validation was implemented by comparing manual assessments done by different ranks of doctors with those performed by three‐dimensional CNN. The results show that the performance of the CNN model was superior to manual assessment. Conclusion Under the companion diagnostics, the three‐dimensional CNN with a deep learning algorithm may assist radiologists in the future by providing accurate and timely information for diagnosing pulmonary nodules in regular clinical practices. Implications for Practice The three‐dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment. Although it still warrants further improvement and validation in larger screening cohorts, its clinical application could definitely facilitate and assist doctors in clinical practice.