| Citation: |
ZHANG Zhenghua, CAI Yaqian, HAN Dan, ZHOU Xiaojun, HUANG Yilong, LI Junli. Pulmonary Nodule Screening and Qualitative Diagnosis Based on Deep Learning[J]. Cancer Research on Prevention and Treatment, 2020, 47(4): 283-287. DOI: 10.3971/j.issn.1000-8578.2020.19.1107
|
To explore the clinical application value of deep learning-based artificial intelligence (AI) in the detection and related quantitative measurement of pulmonary nodules.
We collected 250 cases of chest CT scan and compared the misdiagnosis rate, missed diagnosis rate, sensitivity, positive predictive value and average diagnosis time of pulmonary nodules among group A (hospitalized), group B (AI) and group C (hospitalized+AI). Meanwhile, AI quantization parameters of solid nodules and ground glass nodules (GGN) were compared, and ROC curve analysis was performed for the parameters with statistical difference.
A total of 2230 nodules were identified. The misdiagnosis rate of group B was significantly higher than those of group A and C, and the positive predictive value was significantly lower than those of group A and C (P < 0.05). The rate of missed diagnosis in group A was significantly higher than those in group B and C, and the sensitivity was significantly lower than those in group B and C (P < 0.05). There were statistically significant differences in the long diameter, maximum area, volume, minimum CT value and malignant probability between solid benign and malignant nodules (P < 0.05). The indexes of area under the ROC curve (AUC) greater than 0.7 were: long diameter, maximum area, volume and malignant probability. There were statistically significant differences in the length, maximum area, volume, average CT value, maximum CT value and malignant probability between GGN benign and malignant nodules (P < 0.05). ROC curve analysis of all parameters showed that AUC was greater than 0.7.
AI-assisted film reading could significantly improve work efficiency and sensitivity of pulmonary nodules detection and reduce the rates of misdiagnosis and missed diagnosis. Meanwhile, it has certain reference value for the prediction of benign and malignant pulmonary nodules.
| [1] |
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis[J]. Med Image Anal, 2017, 42(9): 60-88.
|
| [2] |
Qin C, Yao D, Shi Y, et al. Computer-aided detection in chest radiography based on artificial intelligence: a survey[J]. Biomed Eng Online, 2018, 17(1): 113.
|
| [3] |
Gong L, Jiang S, Yang Z, et al. Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks[J]. Int J Comput Assist Radiol Surg, 2019, 14(11): 1969-1979.
|
| [4] |
Valente IR, Cortez PC, Neto EC, et al. Automatic 3D pulmonary nodule detection in CT images: A survey[J]. Comput Methods Programs Biomed, 2016, 124: 91-107.
|
| [5] |
中国食品药品检定研究院, 中华医学会放射学分会心胸学组.胸部CT肺结节数据标注与质量控制专家共识(2018)[J].中华放射学杂志, 2019, 53(1): 9-15.
China Institute of Food and Drug Control, Chinese Medical Association. Expert consensus on the rule and quality control of pulmonary nodule annotation based on thoracic CT (2018)[J]. Zhonghua Fang She Xue Za Zhi, 2019, 53(1): 9-15.
|
| [6] |
Shaffie A, Soliman A, Fraiwan L, et al. A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules[J]. Technol Cancer Res Treat, 2018, 17: 1533033818798800.
|
| [7] |
Zhang G, Yang Z, Gong L, et al. An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images[J]. J Med Syst, 2019, 43(7): 181.
|
| [8] |
胡琼洁, 陈冲, 王玉锦, 等.实习医师以共同阅片和第二阅片者模式使用计算机辅助检测系统在低剂量CT中的应用研究[J].放射学实践, 2018, 33(10): 1022-1028.
Hu QJ, Chen C, Wang YJ, et al. A computer-aided detection (CAD) system on low-dose chest CT images in concurent-reader and second-reader modes:influence on interns[J]. Fang She Xue Shi Jian, 2018, 33(10): 1022-1028.
|
| [9] |
邵亚军, 张荣宝, 郭佑民, 等.计算机辅助工具对肺结节检测效能的研究[J].实用放射学杂志, 2018, 34(9): 1430-1433.
Shao YJ, Zhang BR, Guo YM, et al. Effectiveness of computer-aided detection for pulmonary nodules[J]. Shi Yong Fang She Xue Za Zhi, 2018, 34(9): 1430-1433.
|
| [10] |
顾亚峰, 李琼, 范丽, 等.不同窗宽窗位下肺亚实性结节及其实性成分大小对病理等级的预测价值[J].中华放射学杂志, 2017, 51(7): 484-488.
Gu YF, Li Q, Fan L, et al. Predictive value of whole nodule size and solid component size of pulmonary subsolid nodule with different window setting for the pathologic grade[J]. Zhonghua Fang She Xue Za Zhi, 2017, 51(7): 484-488.
|
| [11] |
Du Y, Zhao Y, Sidorenkov G, et al. Methods of computed tomography screening and management of lung cancer in Tianjin: design of a population-based cohort study[J]. Cancer Biol Med, 2019, 16(1): 181-188.
|
| [12] |
Shi Z, Deng J, She Y, et al. Quantitative features can predict further growth of persistent pure ground-glass nodule[J]. Quant Imaging Med Surg, 2019, 9(2): 283-291.
|
| [13] |
Kitami A, Sano F, Hayashi S, et al. Correlation between histological invasiveness and the computed tomography value in pure ground-glass nodules[J]. Surg Today, 2016, 46(5): 593-598.
|
| [14] |
熊廷伟, 李川, 龚明福, 等. MSCT在肺孤立性磨玻璃结节鉴别诊断中的价值[J].中华肺部疾病杂志(电子版), 2018, 11(4): 401-404.
Xiong TW, Li C, Gong MF, et al. Value of multi-slice spiral CT in differential diagnosis of solitary ground-glass opacity in lungs[J]. Zhonghua Fei Bu Ji Bing Za Zhi(Dian Zi Ban), 2018, 11(4): 401-404.
|
| [15] |
Yang Y, Wang WW, Ren Y, et al. Computerized texture analysis predicts histological invasiveness within lung adenocarcinoma manifesting as pure ground-glass nodules[J]. Acta Radiol, 2019, 60(10): 1258-1264.
|
| [16] |
矫娜, 吴明祥, 龚静山, 等.计算机辅助诊断定量分析表现为磨玻璃样结节的肺原位腺癌与非典型腺瘤样增生[J].中国CT和MRI杂志, 2015, 13(6): 29-31.
Jiao N, Wu MX, Gong JS, et al. Computer-aided Differential Diagnosis of Adenocarcinoma Insitu and Atypical Adenomatous Hyperplasis Which Appear as Ground Glass Opacity[J]. Zhongguo CT He MRI Za Zhi, 2015, 13(6): 29-31.
|
| [17] |
Xiang W, Xing Y, Jiang S, et al. Morphological factors differentiating between early lung adenocarcinomas appearing as pure ground-glass nodules measuring ≤10 mm on thin-section computed tomography[J]. Cancer Imaging, 2014, 14: 33.
|
| [18] |
曹恩涛, 于红, 范丽, 等.纯磨玻璃密度结节肺腺癌的CT三维定量分析[J].中华放射学杂志, 2016, 50(12): 940-945.
Cao ET, Yu H, Fan L, et al. Quantitative CT analysis of early-stage lung adenocarcinoma with pure ground-glass opacity[J]. Zhonghua Fang She Xue Za Zhi, 2016, 50(12): 940-945.
|
This work is licensed under a Creative Commons Attribution 3.0 License.
Copyright © Editorial Department of Cancer Prevention Research 鄂公网安备 42011102005013号 鄂ICP备2022015867号
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: