Artificial Intelligence Imaging Research
The effectiveness of deep learning techniques in differentiating benign and malignant lung nodules on target CT scans
Tao Guangyu, Ye Jianding, Ye Xiaodan, Mao Li, Yu Lingming, Zhou Zhen, Li Xiuli
Published 2019-11-10
Cite as Chin J Radiol, 2019,53(11): 952-956. DOI: 10.3760/cma.j.issn.1005-1201.2019.11.005
Abstract
ObjectiveTo evaluate the effectiveness of deep learning model trained on routine CT scans when identity the malignant and benign lung nodule on target CT scans dataset.
MethodsThis retrospective study enrolled 923 patients with lung nodules found by chest CT scan in Shanghai Chest Hospital from January 2016 to December 2018. A total of 969 nodules with pathological report were analyzed. The deep learning based pulmonary malignant prediction method in a fine-grained classification manner was used to make the prediction, and the AUC (the area under the curve), accuracy, sensitivity and specificity of routine CT scans and target CT scans were compared, and Delong test and IDI (Integrated Discrimination Improvement) were employed to provide statistical results. Furthermore, statistical methods were used to investigate the differences between the benign and malignant classification of nodules on routine CT and on target CT.
ResultsIn the benign and malignant discrimination task, AUC, accuracy, sensitivity and specificity on the routine scans were 0.81, 82.0%, 86.0% and 56.6% respectively, while the AUC, accuracy, sensitivity and specificity on the target scans were 0.84, 85.0%, 88.8% and 60.5% respectively. The IDI was 0.056 (Z test, P<0.05), and there was statistically significant difference in ROC (Delong test, P=0.01).
ConclusionsThe deep learning model trained on the data set of routine CT scans can achieve better diagnostic efficiency in target CT scans data.
Key words:
Tomography, X-ray computed; Deep learning; Lung nodule; Target scanning
Contributor Information
Tao Guangyu
Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
Ye Jianding
Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
Ye Xiaodan
Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
Mao Li
Deepwise Artificial Intelligence Lab, Beijing 100080, China
Yu Lingming
Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
Zhou Zhen
Deepwise Artificial Intelligence Lab, Beijing 100080, China
Li Xiuli
Deepwise Artificial Intelligence Lab, Beijing 100080, China
