Artificial intelligence-assisted diagnosis and treatment system in prediction of benign or malignant lung nodules and infiltration degree_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

CAO Mengkun ¹ , JIANG Jie ¹ , ZHU Xiaolei ¹ , LI Ning ¹ , WANG Jianweng ¹ , LIN Junfeng ¹ , LIU Hongming ¹ , DENG Chengqing ¹ , CAI Xiqian ² ,  GENG Guojun ¹

1. Department of Thoracic Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, 361000, Fujian, P.R.China;
2. School of Basic Medicine, Fujian Medical University, Fuzhou, 350005, P.R.China;

Corresponding author：

GENG Guojun, Email: ggj622@126.com

Keywords：

Artificial intelligence; lung nodules; volume doubling time; invasive adenocarcinoma; tumor

DOI：

10.7507/1007-4848.202101020

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To evaluate the effectiveness of the artificial intelligence-assisted diagnosis and treatment system in distinguishing benign and malignant lung nodules and the infiltration degree.Methods Clinical data of 87 patients with pulmonary nodules admitted to the First Affiliated Hospital of Xiamen University from January 2019 to August 2020 were retrospectively analyzed, including 33 males aged 55.1±10.4 years, and 54 females aged 54.5±14.1 years. A total of 90 nodules were included, which were divided into a malignant tumor group (n=80) and a benign lesion group (n=10), and the malignant tumor group was subdivided into an invasive adenocarcinoma group (n=60) and a non-invasive adenocarcinoma group (n=20). The malignant probability and doubling time of each group were compared and its ability to predict the benign and malignant nodules and the invasion degree was analyzed.Results Between the malignant tumor group and the benign lesion group, the malignant probability was significantly different, and the malignant probability could better distinguish malignant nodules and benign lesions (87.2%±9.1% vs. 28.8%±29.0%, P=0.000). The area under the curve (AUC) was 0.949. The maximum diameter of nodules in the benign lesion group was significantly longer than that in the malignant tumor group (1.270±0.481 cm vs. 0.990±0.361 cm, P=0.026); the doubling time of benign lesions was significantly longer than that of malignant nodules (1 083.600±258.180 d vs. 527.025±173.176 d, P=0.000), and the AUC was 0.975. The maximum diameter of the nodule in the invasive adenocarcinoma group was longer than that of the non-invasive adenocarcinoma group (1.350±0.355 cm vs. 0.863±0.271 cm, P=0.000), and there was no statistical difference in the probability of malignancy between the invasive adenocarcinoma group and the non-invasive adenocarcinoma group (89.7%±5.7% vs. 86.4%±9.9%, P=0.082). The AUC was 0.630. The doubling time of the invasive adenocarcinoma group was significantly shorter than that of the non-invasive adenocarcinoma group (392.200±138.050 d vs. 571.967±160.633 d, P=0.000), and the AUC was 0.829.Conclusion The malignant probability and doubling time of lung nodules calculated by the artificial intelligence-assisted diagnosis and treatment system can be used in the assessment of the preoperative benign and malignant lung nodules and the infiltration degree.

Citation： CAO Mengkun, JIANG Jie, ZHU Xiaolei, LI Ning, WANG Jianweng, LIN Junfeng, LIU Hongming, DENG Chengqing, CAI Xiqian, GENG Guojun. Artificial intelligence-assisted diagnosis and treatment system in prediction of benign or malignant lung nodules and infiltration degree. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2021, 28(3): 283-287. doi: 10.7507/1007-4848.202101020 Copy

1.	钱桂生. 肺癌不同病理类型发病率的变化情况及其原因. 中华肺部疾病杂志(电子版), 2011, 4(1): 1-6.
2.	萧毅, 刘士远. 肺结节影像人工智能技术现状与思考. 肿瘤影像学, 2018, 27(4): 249-252.
3.	张逊. 人工智能辅助肺癌诊疗一体化解决方案的临床实践与展望. 中国胸心血管外科临床杂志, 2019, 26(12): 1167-1170.
4.	Ciompi F, Chung K, van Riel SJ, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep, 2017, 7: 468-479.
5.	李欣菱, 王颖. 人工智能在肺结节检测与诊断中的应用及发展. 新发传染病电子杂志, 2019, 4(3): 185-189.
6.	Mohamed Hoesein FA, de Jong PA, Mets OM. Optimizing lung cancer screening: Nodule size, volume doubling time, morphology and evaluation of other diseases. Ann Transl Med, 2015, 3(2): 19.
7.	Usuda K, Saito Y, Sagawa M, et al. Tumor doubling time and prognostic assessment of patients with primary lung cancer. Cancer, 1994, 74(8): 2239-2244.
8.	宋伟, 金征宇, 严洪珍, 等. 初步评估 16 层螺旋 CT 的 Lung Care 软件在肺结节研究中的辅助价值. 中华放射学杂志, 2005, 39(1): 11-16.
9.	王华斌, 李苏建, 卢光明. 多层螺旋 CT 评估孤立性肺结节的临床研究进展. 放射学实践, 2010, 25(1): 105-108.
10.	Oda S, Awai K, Murao K, et al. Volume-doubling time of pulmonary nodules with ground glass opacity at multidetector CT: Assessment with computer-aided three-dimensional volumetry. Acad Radiol, 2011, 18(1): 63-69.
11.	Walter JE, Heuvelmans MA, Ten Haaf K, et al. Persisting new nodules in incidence rounds of the NELSON CT lung cancer screening study. Thorax, 2019, 74(3): 247-253.
12.	裘杨波, 毛锋, 张辉, 等. 早期肺癌进展趋势的影响因素和 CT 研判. 中国肺癌杂志, 2018, 21(10): 793-799.
13.	Zhang YP, Heuvelmans MA, Zhang H, et al. Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons. Clin Radiol, 2018, 73(5): 504.
14.	Son JY, Lee HY, Kim JH, et al. Quantitative CT analysis of pulmonary ground-glass opacity nodules for distinguishing invasive adenocarcinoma from non-invasive or minimally invasive adenocarcinoma: the added value of using iodine mapping. Eur Radiol, 2016, 26(1): 43-54.

1. 钱桂生. 肺癌不同病理类型发病率的变化情况及其原因. 中华肺部疾病杂志(电子版), 2011, 4(1): 1-6.
2. 萧毅, 刘士远. 肺结节影像人工智能技术现状与思考. 肿瘤影像学, 2018, 27(4): 249-252.
3. 张逊. 人工智能辅助肺癌诊疗一体化解决方案的临床实践与展望. 中国胸心血管外科临床杂志, 2019, 26(12): 1167-1170.
4. Ciompi F, Chung K, van Riel SJ, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep, 2017, 7: 468-479.
5. 李欣菱, 王颖. 人工智能在肺结节检测与诊断中的应用及发展. 新发传染病电子杂志, 2019, 4(3): 185-189.
6. Mohamed Hoesein FA, de Jong PA, Mets OM. Optimizing lung cancer screening: Nodule size, volume doubling time, morphology and evaluation of other diseases. Ann Transl Med, 2015, 3(2): 19.
7. Usuda K, Saito Y, Sagawa M, et al. Tumor doubling time and prognostic assessment of patients with primary lung cancer. Cancer, 1994, 74(8): 2239-2244.
8. 宋伟, 金征宇, 严洪珍, 等. 初步评估 16 层螺旋 CT 的 Lung Care 软件在肺结节研究中的辅助价值. 中华放射学杂志, 2005, 39(1): 11-16.
9. 王华斌, 李苏建, 卢光明. 多层螺旋 CT 评估孤立性肺结节的临床研究进展. 放射学实践, 2010, 25(1): 105-108.
10. Oda S, Awai K, Murao K, et al. Volume-doubling time of pulmonary nodules with ground glass opacity at multidetector CT: Assessment with computer-aided three-dimensional volumetry. Acad Radiol, 2011, 18(1): 63-69.
11. Walter JE, Heuvelmans MA, Ten Haaf K, et al. Persisting new nodules in incidence rounds of the NELSON CT lung cancer screening study. Thorax, 2019, 74(3): 247-253.
12. 裘杨波, 毛锋, 张辉, 等. 早期肺癌进展趋势的影响因素和 CT 研判. 中国肺癌杂志, 2018, 21(10): 793-799.
13. Zhang YP, Heuvelmans MA, Zhang H, et al. Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons. Clin Radiol, 2018, 73(5): 504.
14. Son JY, Lee HY, Kim JH, et al. Quantitative CT analysis of pulmonary ground-glass opacity nodules for distinguishing invasive adenocarcinoma from non-invasive or minimally invasive adenocarcinoma: the added value of using iodine mapping. Eur Radiol, 2016, 26(1): 43-54.

Previous Article
肝细胞癌免疫治疗疗效预测与评价
Next Article
晚期肝细胞癌系统治疗进展与挑战

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Artificial intelligence-assisted diagnosis and treatment system in prediction of benign or malignant lung nodules and infiltration degree

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content