Establishment and verification of a mathematical prediction model for benignancy and malignancy in subsolid pulmonary nodules_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

WU Bin , MA Jun ,  SHI Hongcan

Department of Thoracic and Cardiac Surgery, Affiliated Subei People’s Hospital of Yangzhou University, Yangzhou, 225001, Jiangsu, P.R.China;

Corresponding author：

SHI Hongcan, Email: shihongcan@yzu.edu.cn

Keywords：

Subsolid nodules; lung cancer; benign and malignant diagnosis; prediction model

DOI：

10.7507/1007-4848.202006048

Video：

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Objective To explore the independent risk factors for benign and malignant subsolid pulmonary nodules and establish a malignant probability prediction model.Methods A retrospective analysis was performed in 443 patients with subsolid pulmonary nodules admitted to Subei People's Hospital of Jiangsu Province from 2014 to 2018 with definite pathological findings. The patients were randomly divided into a modeling group and a validation group. There were 296 patients in the modeling group, including 125 males and 171 females, with an average age of 55.9±11.1 years. There were 147 patients in the verification group, including 68 males and 79 females, with an average age of 56.9±11.6 years. Univariate and multivariate analysis was used to screen the independent risk factors for benign and malignant lesions of subsolid pulmonary nodules, and then a prediction model was established. Based on the validation data, the model of this study was compared and validated with Mayo, VA, Brock and PKUPH models.Results Univariate and multivariate analysis showed that gender, consolidation/tumor ratio (CTR), boundary, spiculation, lobulation and carcinoembryonic antigen (CEA) were independent risk factors for the diagnosis of benign and malignant subsolid pulmonary nodules. The prediction model formula for malignant probability was: P=e^x/(1+e^x). X=0.018+(1.436×gender)+(2.068×CTR)+(−1.976×boundary)+ (2.082×spiculation)+(1.277×lobulation)+(2.296×CEA). In this study, the area under the curve was 0.856, the sensitivity was 81.6%, the specificity was 75.6%, the positive predictive value was 95.4%, and the negative predictive value was 39.8%. Compared with the traditional model, the predictive value of this model was significantly better than that of Mayo, VA, Brock and PKUPH models.Conclusion Compared with Mayo, VA, Brock and PKUPH models, the predictive value of the model is more ideal and has greater clinical application value, which can be used for early screening of subsolid nodules.

Citation： WU Bin, MA Jun, SHI Hongcan. Establishment and verification of a mathematical prediction model for benignancy and malignancy in subsolid pulmonary nodules. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2021, 28(3): 311-318. doi: 10.7507/1007-4848.202006048 Copy

1.	Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2.	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.
3.	Chen ZF, Jiang SX, Li ZL, et al. Clinical value of 18 F-FDG PET/CT in prediction of visceral pleural invasion of subsolid nodule stage Ⅰ lung adenocarcinoma. Acad Radiol, 2020, 27(12): 1691-1699.
4.	Chen KN. The diagnosis and treatment of lung cancer presented as ground-glass nodule. Gen Thorac Cardiovasc Surg, 2020, 68(7): 697-702.
5.	Meyer M, Vliegenthart R, Henzler T, et al. Management of progressive pulmonary nodules found during and outside of CT lung cancer screening studies. J Thorac Oncol, 2017, 12(12): 1755-1765.
6.	Murphy A, Skalski M, Gaillard F. The utilisation of convolutional neural networks in detecting pulmonary nodules: a review. Br J Radiol, 2018, 91: 20180028.
7.	Karki A, Shah R, Fein A. Multiple pulmonary nodules in malignancy. Curr Opin Pulm Med, 2017, 23(4): 285-289.
8.	Swensen SJ, Silverstein MD, Ilstrup DM, et al. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med, 1997, 157(8): 849-855.
9.	Gould MK, Ananth L, Barnett PG, et al. A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. Chest, 2007, 131(2): 383-388.
10.	McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med, 2013, 369(10): 910-919.
11.	李运, 陈克终, 隋锡朝, 等. 孤立性肺结节良恶性判断数学预测模型的建立. 北京大学学报(医学版), 2011, 43(3): 450-454.
12.	李廷源, 杨丽. 孤立性肺结节的研究进展. 中华肺部疾病杂志(电子版), 2017, 10(5): 608-612.
13.	MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017. Radiology, 2017, 284(1): 228-243.
14.	Ng CSH, Chu CM, Lo CK, et al. Hybrid operating room dyna-computed tomography combined image-guided electromagnetic navigation bronchoscopy dye marking and hookwire localization video-assisted thoracic surgery metastasectomy. Interact Cardiovasc Thorac Surg, 2018, 26(2): 338-340.
15.	Elia S, Loprete S, De Stefano A, et al. Does aggressive management of solitary pulmonary nodules pay off? Breathe (Sheff), 2019, 15(1): 15-23.
16.	Hattori A, Matsunaga T, Takamochi K, et al. Neither maximum tumor size nor solid component size is prognostic in part-solid lung cancer: Impact of tumor size should be applied exclusively to solid lung cancer. Ann Thorac Surg, 2016, 102(2): 407-415.
17.	鲍彤, 肖飞, 郭永庆, 等. 对孤立性肺结节恶性概率预测模型的验证、比较和改良. 中国胸心血管外科临床杂志, 2018, 25(6): 471-476.
18.	Hu H, Wang Q, Tang H, et al. Multi-slice computed tomography characteristics of solitary pulmonary ground-glass nodules: Differences between malignant and benign. Thorac Cancer, 2016, 7(1): 80-87.
19.	Yang X, Dong X, Wang J, et al. Computed tomography-based radiomics signature: A potential indicator of epidermal growth factor receptor mutation in pulmonary adenocarcinoma appearing as a subsolid nodule. Oncologist, 2019, 24(11): e1156-e1164.
20.	李金龙. 恶性孤立性肺结节诊断与治疗. 临床与病理杂志, 2017, 37(6): 1276-1281.
21.	李笑莹, 刘芳, 车海杰, 等. 肿瘤标志物预测孤立性肺结节恶性概率模型的建立与初步评价. 山东大学学报(医学版), 2017, 55(4): 60-64.
22.	Zhang Y, Jheon S, Li H, et al. Results of low-dose computed tomography as a regular health examination among Chinese hospital employees. J Thorac Cardiovasc Surg, 2020, 160(3): 824-831.
23.	中华医学会呼吸病学分会肺癌学组中国肺癌防治联盟专家组. 肺结节诊治中国专家共识(2018年版). 中华结核和呼吸杂志, 2018, 41(10): 763-771.
24.	刘施乾, 吴海新. 孤立性肺结节良恶性的CT诊断. 航空航天医学杂志, 2019, 30(6): 648-651.
25.	Choi HK, Ghobrial M, Mazzone PJ. Models to estimate the probability of malignancy in patients with pulmonary nodules. Ann Am Thorac Soc, 2018, 15(10): 1117-1126.

1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2. 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.
3. Chen ZF, Jiang SX, Li ZL, et al. Clinical value of 18 F-FDG PET/CT in prediction of visceral pleural invasion of subsolid nodule stage Ⅰ lung adenocarcinoma. Acad Radiol, 2020, 27(12): 1691-1699.
4. Chen KN. The diagnosis and treatment of lung cancer presented as ground-glass nodule. Gen Thorac Cardiovasc Surg, 2020, 68(7): 697-702.
5. Meyer M, Vliegenthart R, Henzler T, et al. Management of progressive pulmonary nodules found during and outside of CT lung cancer screening studies. J Thorac Oncol, 2017, 12(12): 1755-1765.
6. Murphy A, Skalski M, Gaillard F. The utilisation of convolutional neural networks in detecting pulmonary nodules: a review. Br J Radiol, 2018, 91: 20180028.
7. Karki A, Shah R, Fein A. Multiple pulmonary nodules in malignancy. Curr Opin Pulm Med, 2017, 23(4): 285-289.
8. Swensen SJ, Silverstein MD, Ilstrup DM, et al. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med, 1997, 157(8): 849-855.
9. Gould MK, Ananth L, Barnett PG, et al. A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. Chest, 2007, 131(2): 383-388.
10. McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med, 2013, 369(10): 910-919.
11. 李运, 陈克终, 隋锡朝, 等. 孤立性肺结节良恶性判断数学预测模型的建立. 北京大学学报(医学版), 2011, 43(3): 450-454.
12. 李廷源, 杨丽. 孤立性肺结节的研究进展. 中华肺部疾病杂志(电子版), 2017, 10(5): 608-612.
13. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017. Radiology, 2017, 284(1): 228-243.
14. Ng CSH, Chu CM, Lo CK, et al. Hybrid operating room dyna-computed tomography combined image-guided electromagnetic navigation bronchoscopy dye marking and hookwire localization video-assisted thoracic surgery metastasectomy. Interact Cardiovasc Thorac Surg, 2018, 26(2): 338-340.
15. Elia S, Loprete S, De Stefano A, et al. Does aggressive management of solitary pulmonary nodules pay off? Breathe (Sheff), 2019, 15(1): 15-23.
16. Hattori A, Matsunaga T, Takamochi K, et al. Neither maximum tumor size nor solid component size is prognostic in part-solid lung cancer: Impact of tumor size should be applied exclusively to solid lung cancer. Ann Thorac Surg, 2016, 102(2): 407-415.
17. 鲍彤, 肖飞, 郭永庆, 等. 对孤立性肺结节恶性概率预测模型的验证、比较和改良. 中国胸心血管外科临床杂志, 2018, 25(6): 471-476.
18. Hu H, Wang Q, Tang H, et al. Multi-slice computed tomography characteristics of solitary pulmonary ground-glass nodules: Differences between malignant and benign. Thorac Cancer, 2016, 7(1): 80-87.
19. Yang X, Dong X, Wang J, et al. Computed tomography-based radiomics signature: A potential indicator of epidermal growth factor receptor mutation in pulmonary adenocarcinoma appearing as a subsolid nodule. Oncologist, 2019, 24(11): e1156-e1164.
20. 李金龙. 恶性孤立性肺结节诊断与治疗. 临床与病理杂志, 2017, 37(6): 1276-1281.
21. 李笑莹, 刘芳, 车海杰, 等. 肿瘤标志物预测孤立性肺结节恶性概率模型的建立与初步评价. 山东大学学报(医学版), 2017, 55(4): 60-64.
22. Zhang Y, Jheon S, Li H, et al. Results of low-dose computed tomography as a regular health examination among Chinese hospital employees. J Thorac Cardiovasc Surg, 2020, 160(3): 824-831.
23. 中华医学会呼吸病学分会肺癌学组中国肺癌防治联盟专家组. 肺结节诊治中国专家共识(2018年版). 中华结核和呼吸杂志, 2018, 41(10): 763-771.
24. 刘施乾, 吴海新. 孤立性肺结节良恶性的CT诊断. 航空航天医学杂志, 2019, 30(6): 648-651.
25. Choi HK, Ghobrial M, Mazzone PJ. Models to estimate the probability of malignancy in patients with pulmonary nodules. Ann Am Thorac Soc, 2018, 15(10): 1117-1126.

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Establishment and verification of a mathematical prediction model for benignancy and malignancy in subsolid pulmonary nodules

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