A comparative analysis of peripheral lung shadow diagnosed by artificial intelligence-assisted chest CT versus postoperative pathology in 810 patients_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

DU Lin ¹ , ZHANG Hong ¹ , LUO Xiangfeng ² , LV Jun ¹ ,  SUN Daqiang ¹

1. Department of Thoracic Surgery, Tianjin Chest Hospital Affiliated to Tianjin University, Tianjin, 300222, P. R. China;
2. LinkDoc Science and Technology Co. Ltd., Beijing, 100080, P. R. China;

Corresponding author：

SUN Daqiang, Email: sdqmdxkyy@163.com

Keywords：

Lung cancer; artificial intelligence; auxiliary diagnosis; peripheral lung shadow

DOI：

10.7507/1007-4848.202201018

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To investigate the clinical value of artificial intelligence (AI)-assisted chest computed tomography (CT) in the diagnosis of peripheral lung shadow. Methods The CT image data of 810 patients with peripheral pulmonary shadow treated by thoracic surgery in Tianjin Chest Hospital Affiliated to Tianjin University from January 2018 to July 2019 were retrospectively analyzed using AI-assisted chest CT imaging diagnosis system. There were 339 males and 471 females with a median age of 63 years. The malignant probability of preoperative AI-assisted diagnosis of peripheral pulmonary shadow was compared with the results of postoperative pathology. Results The pathological diagnosis of 810 patients with peripheral pulmonary shadow was lung cancer in 627 (77.4%) patients, precancerous lesion in 30 (3.7%) patients and benign lesion in 153 (18.9%) patients. The median probability of malignant AI diagnosis before operation was 86.0% (lung cancer), 90.0% (precancerous lesion) and 37.0% (benign lesion), respectively. According to the analysis of receiver operating characteristic (ROC) curve of AI malignant probability distribution in this group of patients, the area under the ROC curve was 0.882. The critical value of malignant probability for diagnosis of lung cancer was 75.0% with a sensitivity of 0.856 and specificity of 0.814. A total of 571 patients were diagnosed with AI malignancy probability≥75.0%, among whom 537 patients were pathologically diagnosed as lung cancer with a positive predictive value of 94.0% (537/571). Conclusion The AI-assisted chest CT diagnosis system has a high accuracy in the diagnosis of peripheral lung cancer with malignant probability≥75.0% as the diagnostic threshold.

Citation： DU Lin, ZHANG Hong, LUO Xiangfeng, LV Jun, SUN Daqiang. A comparative analysis of peripheral lung shadow diagnosed by artificial intelligence-assisted chest CT versus postoperative pathology in 810 patients. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(7): 854-858. doi: 10.7507/1007-4848.202201018 Copy

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13.	Halder A, Dey D, Sadhu AK. Lung nodule detection from feature engineering to deep learning in thoracic CT images: A comprehensive review. J Digit Imaging, 2020, 33(3): 655-677.
14.	Marcovici PA, Taylor GA. Journal club: Structured radiology reports are more complete and more effective than unstructured reports. AJR Am J Roentgenol, 2014, 203(6): 1265-1271.
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1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. Baker SR, Patel RH, Yang L, et al. Malpractice suits in chest radiology: An evaluation of the histories of 8265 radiologists. J Thorac Imaging, 2013, 28(6): 388-391.
3. Del Ciello A, Franchi P, Contegiacomo A, et al. Missed lung cancer: When, where, and why? Diagn Interv Radiol, 2017, 23(2): 118-126.
4. Prabhakar B, Shende P, Augustine S. Current trends and emerging diagnostic techniques for lung cancer. Biomed Pharmacother, 2018, 106: 1586-1599.
5. 陈俊任, 曾瑜, 张超, 等. 人工智能医学应用的文献传播的可视化研究. 中国循证医学杂志, 2021, 21(8): 973-979.
6. Baldwin DR, Gustafson J, Pickup L, et al. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax, 2020, 75(4): 306-312.
7. Liang M, Tang W, Xu DM, et al. Low-dose CT screening for lung cancer: Computer-aided detection of missed lung cancers. Radiology, 2016, 281(1): 279-288.
8. Vassallo L, Traverso A, Agnello M, et al. A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies. Eur Radiol, 2019, 29(1): 144-152.
9. Li X, Guo F, Zhou Z, et al. Performance of deep-learning-based artificial intelligence on detection of pulmonary nodules in chest CT. Zhongguo Fei Ai Za Zhi, 2019, 22(6): 336-340.
10. Cai J, Xu D, Liu S, et al. The added value of computer-aided detection of small pulmonary nodules and missed lung cancers. J Thorac Imaging, 2018, 33(6): 390-395.
11. Zhang G, Jiang S, Yang Z, et al. Automatic nodule detection for lung cancer in CT images: A review. Comput Biol Med, 2018, 103: 287-300.
12. Weikert T, Akinci D, Antonoli T, et al. Evaluation of an AI-powered lung nodule algorithm for detection and 3D segmentation of primary lung tumors. Contrast Media Mol Imaging, 2019, 2019: 1545747.
13. Halder A, Dey D, Sadhu AK. Lung nodule detection from feature engineering to deep learning in thoracic CT images: A comprehensive review. J Digit Imaging, 2020, 33(3): 655-677.
14. Marcovici PA, Taylor GA. Journal club: Structured radiology reports are more complete and more effective than unstructured reports. AJR Am J Roentgenol, 2014, 203(6): 1265-1271.
15. Sahni VA, Silveira PC, Sainani NI, et al. Impact of a structured report template on the quality of MRI reports for rectal cancer staging. AJR Am J Roentgenol, 2015, 205(3): 584-588.
16. Jha S, Topol EJ. Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA, 2016, 316(22): 2353-2354.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

A comparative analysis of peripheral lung shadow diagnosed by artificial intelligence-assisted chest CT versus postoperative pathology in 810 patients

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