Performance of Deep-learning-based Artificial Intelligence on Detection of Pulmonary Nodules in Chest CT.
10.3779/j.issn.1009-3419.2019.06.02
- Author:
Xinling LI
1
;
Fangfang GUO
2
;
Zhen ZHOU
3
;
Fandong ZHANG
3
;
Qin WANG
1
;
Zhijun PENG
1
;
Datong SU
1
;
Yaguang FAN
4
;
Ying WANG
1
Author Information
1. Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China.
2. Department of Radiology, the First Affiliated Hospital of XinXiang Medical College, Xinxiang 453100, China.
3. Deepwise Healthcare, Beijing 100080, China.
4. Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin 300052, China.
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Computed tomography;
Deep learning;
Detection;
Lung nodules
- MeSH:
Artificial Intelligence;
Deep Learning;
Humans;
Lung Neoplasms;
diagnosis;
diagnostic imaging;
Multiple Pulmonary Nodules;
diagnosis;
diagnostic imaging;
Tomography, X-Ray Computed
- From:
Chinese Journal of Lung Cancer
2019;22(6):336-340
- CountryChina
- Language:Chinese
-
Abstract:
BACKGROUND:The detection of pulmonary nodules is a key step to achieving the early diagnosis and therapy of lung cancer. Deep learning based Artificial intelligence (AI) presents as the state of the art in the area of nodule detection, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the performance of AI in the detection of malignant and non-calcified nodules in chest CT.
METHODS:Two hundred chest computed tomography (CT) data were randomly selected from a self-built nodule database from Tianjin Medical University General Hospital. Both the pathology confirmed lung cancers and the nodules in the process of follow-up were included. All CTs were processed by AI and the results were compared with that of radiologists retrieved from the original medical reports. The ground truths were further determined by two experienced radiologists. The size and characteristics of the nodules were evaluated as well. The sensitivity and false positive rate were used to evaluate the effectiveness of AI and radiologists in detecting nodules. The McNemar test was used to determine whether there was a significant difference.
RESULTS:A total of 889 non-calcified nodules were determined by experts on chest CT, including 133 lung cancers. Of them, 442 nodules were less than 5 mm. The cancer detection rates of AI and radiologists are 100%. The sensitivity of AI on nodule detection was significantly higher than that of radiologists (99.1% vs 43%, P<0.001). The false-positive rate of AI was 4.9 per CT and decreased to 1.5 when nodules less than 5 mm were excluded.
CONCLUSIONS:AI achieves the detection of all malignancies and improve the sensitivity of pulmonary nodules detection beyond radiologists, with a low false positive rate after excluding small nodules.
