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：

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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

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3.	張遜. 人工智能輔助肺癌診療一體化解決方案的臨床實踐與展望. 中國胸心血管外科臨床雜志, 2019, 26(12): 1167-1170.
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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.	宋偉, 金征宇, 嚴洪珍, 等. 初步評估 16 層螺旋 CT 的 Lung Care 軟件在肺結節研究中的輔助價值. 中華放射學雜志, 2005, 39(1): 11-16.
9.	王華斌, 李蘇建, 盧光明. 多層螺旋 CT 評估孤立性肺結節的臨床研究進展. 放射學實踐, 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.	裘楊波, 毛鋒, 張輝, 等. 早期肺癌進展趨勢的影響因素和 CT 研判. 中國肺癌雜志, 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. 錢桂生. 肺癌不同病理類型發病率的變化情況及其原因. 中華肺部疾病雜志(電子版), 2011, 4(1): 1-6.
2. 蕭毅, 劉士遠. 肺結節影像人工智能技術現狀與思考. 腫瘤影像學, 2018, 27(4): 249-252.
3. 張遜. 人工智能輔助肺癌診療一體化解決方案的臨床實踐與展望. 中國胸心血管外科臨床雜志, 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, 王穎. 人工智能在肺結節檢測與診斷中的應用及發展. 新發傳染病電子雜志, 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. 宋偉, 金征宇, 嚴洪珍, 等. 初步評估 16 層螺旋 CT 的 Lung Care 軟件在肺結節研究中的輔助價值. 中華放射學雜志, 2005, 39(1): 11-16.
9. 王華斌, 李蘇建, 盧光明. 多層螺旋 CT 評估孤立性肺結節的臨床研究進展. 放射學實踐, 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. 裘楊波, 毛鋒, 張輝, 等. 早期肺癌進展趨勢的影響因素和 CT 研判. 中國肺癌雜志, 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.

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

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