Invasiveness evaluation of pulmonary ground-glass nodules by CT features combined with tumor markers: A retrospective cohort study_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

HE Hua ¹ , HU Wenteng ² , LIN Ruijiang ² , WEI Ning ² , MA Minjie ² ,  HAN Biao ²

1. The First Clinical Medical College of Lanzhou University, Lanzhou, 730030, P. R. China;
2. Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, P. R. China;

Corresponding author：

HAN Biao, Email: hanbiao66@163.com

Keywords：

Ground-glass nodules; CT characteristic; tumor markers; tumor invasiveness; artificial intelligence

DOI：

10.7507/1007-4848.202108087

Video：

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Objective To explore the independent risk factors for tumor invasiveness of ground-glass nodules and establish a tumor invasiveness prediction model. Methods A retrospective analysis was performed in 389 patients with ground-glass nodules admitted to the Department of Thoracic Surgery in the First Hospital of Lanzhou University from June 2018 to May 2021 with definite pathological findings, including clinical data, imaging features and tumor markers. A total of 242 patients were included in the study according to inclusion criteria, including 107 males and 135 females, with an average age of 57.98±9.57 years. CT data of included patients were imported into the artificial intelligence system in DICOM format. The artificial intelligence system recognized, automatically calculated and output the characteristics of pulmonary nodules, such as standard diameter, solid component size, volume, average CT value, maximum CT value, minimum CT value, central CT value, and whether there were lobulation, burr sign, pleural depression and blood vessel passing. The patients were divided into two groups: a preinvasive lesions group (atypical adenomatoid hyperplasia/adenocarcinoma in situ) and an invasive lesions group (minimally invasive adenocarcinoma/invasive adenocarcinoma). Univariate and multivariate analyses were used to screen the independent risk factors for tumor invasiveness of ground-glass nodules and then a prediction model was established. The receiver operating characteristic (ROC) curve was drawn, and the critical value was calculated. The sensitivity and specificity were obtained according to the Yorden index. Results Univariate and multivariate analyses showed that central CT value, Cyfra21-1, solid component size, nodular nature and burr of the nodules were independent risk factors for the diagnosis of tumor invasiveness of ground-glass nodules. The optimum critical value of the above indicators between preinvasive lesions and invasive lesions were –309.00 Hu, 3.23 ng/mL, 8.65 mm, respectively. The prediction model formula for tumor invasiveness probability was logit (P)=0.982–(3.369×nodular nature)+(0.921×solid component size)+(0.002×central CT value)+(0.526×Cyfra21-1)–(0.0953×burr). The areas under the curve obtained by plotting the ROC curve using the regression probabilities of regression model was 0.908. The accuracy rate was 91.3%. Conclusion The logistic regression model established in this study can well predict the tumor invasiveness of ground-glass nodules by CT and tumor markers with high predictive value.

Citation： HE Hua, HU Wenteng, LIN Ruijiang, WEI Ning, MA Minjie, HAN Biao. Invasiveness evaluation of pulmonary ground-glass nodules by CT features combined with tumor markers: A retrospective cohort study. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(9): 1113-1119. doi: 10.7507/1007-4848.202108087 Copy

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3.	Kligerman S, Cai L, White CS. The effect of computer-aided detection on radiologist performance in the detection of lung cancers previously missed on a chest radiograph. J Thorac Imaging, 2013, 28(4): 244-252.
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5.	Li Q, Dai J, Zhang P, et al. Management of pulmonary ground glass nodules: Less is more. Ann Thorac Surg, 2021, 112(1): 1-2.
6.	Hansell DM, Bankier AA, MacMahon H, et al. Fleischner Society: Glossary of terms for thoracic imaging. Radiology, 2008, 246(3): 697-722.
7.	Wagenaar SS. New WHO-classification of lung and pleural tumors. Ned Tijdschr Geneeskd, 1999, 143(19): 984-990.
8.	张汝思, 张梅芳, 高树庚, 等. 第五版胸部肿瘤WHO分类中肺原位腺癌分类更改的解读. 中国胸心血管外科临床杂志, 2021, 28(9): 1012-1015.
9.	Henschke CI, Yankelevitz DF, Mirtcheva R, et al. CT screening for lung cancer: Frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol, 2002, 178(5): 1053-1057.
10.	Kuriyama K, Tateishi R, Doi O, et al. Prevalence of air bronchograms in small peripheral carcinomas of the lung on thin-section CT: Comparison with benign tumors. AJR Am J Roentgenol, 1991, 156(5): 921-924.
11.	Yang ZG, Sone S, Takashima S, et al. High-resolution CT analysis of small peripheral lung adenocarcinomas revealed on screening helical CT. AJR Am J Roentgenol, 2001, 176(6): 1399-1407.
12.	Imielinski M, Berger AH, Hammerman PS, et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell, 2012, 150(6): 1107-1120.
13.	Yang J, Wang H, Geng C, et al. Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules. Biomed Eng Online, 2018, 17(1): 20.
14.	Chang B, Hwang JH, Choi YH, et al. Natural history of pure ground-glass opacity lung nodules detected by low-dose CT scan. Chest, 2013, 143(1): 172-178.
15.	Behera M, Owonikoko TK, Gal AA, et al. Lung adenocarcinoma staging using the 2011 IASLC/ATS/ERS classification: A pooled analysis of adenocarcinoma in situ and minimally invasive adenocarcinoma. Clin Lung Cancer, 2016, 17(5): e57-e64.
16.	Murakami S, Ito H, Tsubokawa N, et al. Prognostic value of the new IASLC/ATS/ERS classification of clinical stage ⅠA lung adenocarcinoma. Lung Cancer, 2015, 90(2): 199-204.
17.	Kakinuma R, Ohmatsu H, Kaneko M, et al. Progression of focal pure ground-glass opacity detected by low-dose helical computed tomography screening for lung cancer. J Comput Assist Tomogr, 2004, 28(1): 17-23.
18.	Suzuki K, Asamura H, Kusumoto M, et al. "Early" peripheral lung cancer: Prognostic significance of ground glass opacity on thin-section computed tomographic scan. Ann Thorac Surg, 2002, 74(5): 1635-1639.
19.	Asamura H, Suzuki K, Watanabe S, et al. A clinicopathological study of resected subcentimeter lung cancers: A favorable prognosis for ground glass opacity lesions. Ann Thorac Surg, 2003, 76(4): 1016-1022.
20.	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.
21.	Naidich DP, Bankier AA, MacMahon H, et al. Recommendations for the management of subsolid pulmonary nodules detected at CT: A statement from the Fleischner Society. Radiology, 2013, 266(1): 304-317.
22.	Tomita M, Shimizu T, Ayabe T, et al. Prognostic significance of tumour marker index based on preoperative CEA and CYFRA 21-1 in non-small cell lung cancer. Anticancer Res, 2010, 30(7): 3099-3102.
23.	Schneider J, Velcovsky HG, Morr H, et al. Comparison of the tumor markers tumor M2-PK, CEA, CYFRA 21-1, NSE and SCC in the diagnosis of lung cancer. Anticancer Res, 2000, 20(6D): 5053-5058.
24.	Huang X, Li J, Wang F, et al. CT combined with tumor markers in the diagnosis and prognosis of hepatocellular carcinoma. J BUON, 2018, 23(4): 985-991.
25.	Sah SK, Zeng C, Li X, et al. CT features and analysis for misdiagnosis of parotid tuberculosis. Clin Imaging, 2016, 40(4): 810-815.
26.	Li X, Zhang W, Yu Y, et al. CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction. BMC Cancer, 2020, 20(1): 60.
27.	Wu H, Liu C, Xu M, et al. A Retrospective study of mean computed tomography value to predict the tumor invasiveness in AAH and clinical stage ⅠA lung cancer. Zhongguo Fei Ai Za Zhi, 2018, 21(3): 190-196.
28.	Liu Y, Sun H, Zhou F, et al. Imaging features of TSCT predict the classification of pulmonary preinvasive lesion, minimally and invasive adenocarcinoma presented as ground glass nodules. Lung Cancer, 2017, 108: 192-197.
29.	Tamura M, Matsumoto I, Saito D, et al. Mean computed tomography value to predict the tumor invasiveness in clinical stage ⅠA lung cancer. Ann Thorac Surg, 2017, 104(1): 261-266.
30.	Kitami A, Sano F, Hayashi S, et al. Correlation between histological invasiveness and the computed tomography value in pure ground-glass nodules. Surg Today, 2016, 46(5): 593-598.
31.	徐辉, 陆亚东, 顾涛, 等. 肺部单发微小磨玻璃结节(<10 mm) MSCT特征对肺腺癌病理亚型的诊断价值分析. 临床医药文献电子杂志, 2020, 7(52): 152,69.
32.	Ikeda K, Awai K, Mori T, et al. Differential diagnosis of ground-glass opacity nodules: CT number analysis by three-dimensional computerized quantification. Chest, 2007, 132(3): 984-990.
33.	Lee HY, Choi YL, Lee KS, et al. Pure ground-glass opacity neoplastic lung nodules: Histopathology, imaging, and management. AJR Am J Roentgenol, 2014, 202(3): W224-W233.
34.	Zhang Y, Qiang JW, Ye JD, et al. High resolution CT in differentiating minimally invasive component in early lung adenocarcinoma. Lung Cancer, 2014, 84(3): 236-241.
35.	Li W, Wang X, Zhang Y, et al. Radiomic analysis of pulmonary ground-glass opacity nodules for distinction of preinvasive lesions, invasive pulmonary adenocarcinoma and minimally invasive adenocarcinoma based on quantitative texture analysis of CT. Chin J Cancer Res, 2018, 30(4): 415-424.
36.	Peng M, Peng F, Zhang C, et al. Correction: Preoperative prediction of Ki-67 labeling index by three-dimensional CT image parameters for differential diagnosis of ground-glass opacity (GGO). PLoS One, 2019, 14(2): e0211950.
37.	Ichinose J, Kawaguchi Y, Nakao M, et al. Utility of maximum CT value in predicting the invasiveness of pure ground-glass nodules. Clin Lung Cancer, 2020, 21(3): 281-287.
38.	She Y, Zhao L, Dai C, et al. Preoperative nomogram for identifying invasive pulmonary adenocarcinoma in patients with pure ground-glass nodule: A multi-institutional study. Oncotarget, 2017, 8(10): 17229-17238.
39.	Si MJ, Tao XF, Du GY, et al. Thin-section computed tomography-histopathologic comparisons of pulmonary focal interstitial fibrosis, atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma with pure ground-glass opacity. Eur J Radiol, 2016, 85(10): 1708-1715.
40.	Lee HJ, Goo JM, Lee CH, et al. Predictive CT findings of malignancy in ground-glass nodules on thin-section chest CT: The effects on radiologist performance. Eur Radiol, 2009, 19(3): 552-560.
41.	Gao F, Li M, Ge X, et al. Multi-detector spiral CT study of the relationships between pulmonary ground-glass nodules and blood vessels. Eur Radiol, 2013, 23(12): 3271-3277.
42.	Fan L, Liu SY, Li QC, et al. Multidetector CT features of pulmonary focal ground-glass opacity: Differences between benign and malignant. Br J Radiol, 2012, 85(1015): 897-904.
43.	Zhai Y, Hui Z, Men Y, et al. Combined neat model for the prognosis of postoperative stage Ⅲ-N2 non-small cell lung cancer. Thorac Cancer, 2020, 11(9): 2610-2617.
44.	Yoshimura A, Uchino J, Hasegawa K, et al. Carcinoembryonic antigen and CYFRA 21-1 responses as prognostic factors in advanced non-small cell lung cancer. Transl Lung Cancer Res, 2019, 8(3): 227-234.
45.	Tomita M, Ayabe T, Maeda R, et al. Serum carcinoembryonic antigen level predicts cancer-specific outcomes of resected non-small cell lung cancer with interstitial pneumonia. World J Oncol, 2018, 9(5-6): 136-140.
46.	Baek AR, Seo HJ, Lee JH, et al. Prognostic value of baseline carcinoembryonic antigen and cytokeratin 19 fragment levels in advanced non-small cell lung cancer. Cancer Biomark, 2018, 22(1): 55-62.
47.	Doweck I, Barak M, Uri N, et al. The prognostic value of the tumour marker Cyfra 21-1 in carcinoma of head and neck and its role in early detection of recurrent disease. Br J Cancer, 2000, 83(12): 1696-1701.

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. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin, 2019, 69(1): 7-34.
3. Kligerman S, Cai L, White CS. The effect of computer-aided detection on radiologist performance in the detection of lung cancers previously missed on a chest radiograph. J Thorac Imaging, 2013, 28(4): 244-252.
4. Schreuder A, Scholten ET, van Ginneken B, et al. Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: Ready for practice? Transl Lung Cancer Res, 2021, 10(5): 2378-2388.
5. Li Q, Dai J, Zhang P, et al. Management of pulmonary ground glass nodules: Less is more. Ann Thorac Surg, 2021, 112(1): 1-2.
6. Hansell DM, Bankier AA, MacMahon H, et al. Fleischner Society: Glossary of terms for thoracic imaging. Radiology, 2008, 246(3): 697-722.
7. Wagenaar SS. New WHO-classification of lung and pleural tumors. Ned Tijdschr Geneeskd, 1999, 143(19): 984-990.
8. 张汝思, 张梅芳, 高树庚, 等. 第五版胸部肿瘤WHO分类中肺原位腺癌分类更改的解读. 中国胸心血管外科临床杂志, 2021, 28(9): 1012-1015.
9. Henschke CI, Yankelevitz DF, Mirtcheva R, et al. CT screening for lung cancer: Frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol, 2002, 178(5): 1053-1057.
10. Kuriyama K, Tateishi R, Doi O, et al. Prevalence of air bronchograms in small peripheral carcinomas of the lung on thin-section CT: Comparison with benign tumors. AJR Am J Roentgenol, 1991, 156(5): 921-924.
11. Yang ZG, Sone S, Takashima S, et al. High-resolution CT analysis of small peripheral lung adenocarcinomas revealed on screening helical CT. AJR Am J Roentgenol, 2001, 176(6): 1399-1407.
12. Imielinski M, Berger AH, Hammerman PS, et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell, 2012, 150(6): 1107-1120.
13. Yang J, Wang H, Geng C, et al. Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules. Biomed Eng Online, 2018, 17(1): 20.
14. Chang B, Hwang JH, Choi YH, et al. Natural history of pure ground-glass opacity lung nodules detected by low-dose CT scan. Chest, 2013, 143(1): 172-178.
15. Behera M, Owonikoko TK, Gal AA, et al. Lung adenocarcinoma staging using the 2011 IASLC/ATS/ERS classification: A pooled analysis of adenocarcinoma in situ and minimally invasive adenocarcinoma. Clin Lung Cancer, 2016, 17(5): e57-e64.
16. Murakami S, Ito H, Tsubokawa N, et al. Prognostic value of the new IASLC/ATS/ERS classification of clinical stage ⅠA lung adenocarcinoma. Lung Cancer, 2015, 90(2): 199-204.
17. Kakinuma R, Ohmatsu H, Kaneko M, et al. Progression of focal pure ground-glass opacity detected by low-dose helical computed tomography screening for lung cancer. J Comput Assist Tomogr, 2004, 28(1): 17-23.
18. Suzuki K, Asamura H, Kusumoto M, et al. "Early" peripheral lung cancer: Prognostic significance of ground glass opacity on thin-section computed tomographic scan. Ann Thorac Surg, 2002, 74(5): 1635-1639.
19. Asamura H, Suzuki K, Watanabe S, et al. A clinicopathological study of resected subcentimeter lung cancers: A favorable prognosis for ground glass opacity lesions. Ann Thorac Surg, 2003, 76(4): 1016-1022.
20. 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.
21. Naidich DP, Bankier AA, MacMahon H, et al. Recommendations for the management of subsolid pulmonary nodules detected at CT: A statement from the Fleischner Society. Radiology, 2013, 266(1): 304-317.
22. Tomita M, Shimizu T, Ayabe T, et al. Prognostic significance of tumour marker index based on preoperative CEA and CYFRA 21-1 in non-small cell lung cancer. Anticancer Res, 2010, 30(7): 3099-3102.
23. Schneider J, Velcovsky HG, Morr H, et al. Comparison of the tumor markers tumor M2-PK, CEA, CYFRA 21-1, NSE and SCC in the diagnosis of lung cancer. Anticancer Res, 2000, 20(6D): 5053-5058.
24. Huang X, Li J, Wang F, et al. CT combined with tumor markers in the diagnosis and prognosis of hepatocellular carcinoma. J BUON, 2018, 23(4): 985-991.
25. Sah SK, Zeng C, Li X, et al. CT features and analysis for misdiagnosis of parotid tuberculosis. Clin Imaging, 2016, 40(4): 810-815.
26. Li X, Zhang W, Yu Y, et al. CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction. BMC Cancer, 2020, 20(1): 60.
27. Wu H, Liu C, Xu M, et al. A Retrospective study of mean computed tomography value to predict the tumor invasiveness in AAH and clinical stage ⅠA lung cancer. Zhongguo Fei Ai Za Zhi, 2018, 21(3): 190-196.
28. Liu Y, Sun H, Zhou F, et al. Imaging features of TSCT predict the classification of pulmonary preinvasive lesion, minimally and invasive adenocarcinoma presented as ground glass nodules. Lung Cancer, 2017, 108: 192-197.
29. Tamura M, Matsumoto I, Saito D, et al. Mean computed tomography value to predict the tumor invasiveness in clinical stage ⅠA lung cancer. Ann Thorac Surg, 2017, 104(1): 261-266.
30. Kitami A, Sano F, Hayashi S, et al. Correlation between histological invasiveness and the computed tomography value in pure ground-glass nodules. Surg Today, 2016, 46(5): 593-598.
31. 徐辉, 陆亚东, 顾涛, 等. 肺部单发微小磨玻璃结节(<10 mm) MSCT特征对肺腺癌病理亚型的诊断价值分析. 临床医药文献电子杂志, 2020, 7(52): 152,69.
32. Ikeda K, Awai K, Mori T, et al. Differential diagnosis of ground-glass opacity nodules: CT number analysis by three-dimensional computerized quantification. Chest, 2007, 132(3): 984-990.
33. Lee HY, Choi YL, Lee KS, et al. Pure ground-glass opacity neoplastic lung nodules: Histopathology, imaging, and management. AJR Am J Roentgenol, 2014, 202(3): W224-W233.
34. Zhang Y, Qiang JW, Ye JD, et al. High resolution CT in differentiating minimally invasive component in early lung adenocarcinoma. Lung Cancer, 2014, 84(3): 236-241.
35. Li W, Wang X, Zhang Y, et al. Radiomic analysis of pulmonary ground-glass opacity nodules for distinction of preinvasive lesions, invasive pulmonary adenocarcinoma and minimally invasive adenocarcinoma based on quantitative texture analysis of CT. Chin J Cancer Res, 2018, 30(4): 415-424.
36. Peng M, Peng F, Zhang C, et al. Correction: Preoperative prediction of Ki-67 labeling index by three-dimensional CT image parameters for differential diagnosis of ground-glass opacity (GGO). PLoS One, 2019, 14(2): e0211950.
37. Ichinose J, Kawaguchi Y, Nakao M, et al. Utility of maximum CT value in predicting the invasiveness of pure ground-glass nodules. Clin Lung Cancer, 2020, 21(3): 281-287.
38. She Y, Zhao L, Dai C, et al. Preoperative nomogram for identifying invasive pulmonary adenocarcinoma in patients with pure ground-glass nodule: A multi-institutional study. Oncotarget, 2017, 8(10): 17229-17238.
39. Si MJ, Tao XF, Du GY, et al. Thin-section computed tomography-histopathologic comparisons of pulmonary focal interstitial fibrosis, atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma with pure ground-glass opacity. Eur J Radiol, 2016, 85(10): 1708-1715.
40. Lee HJ, Goo JM, Lee CH, et al. Predictive CT findings of malignancy in ground-glass nodules on thin-section chest CT: The effects on radiologist performance. Eur Radiol, 2009, 19(3): 552-560.
41. Gao F, Li M, Ge X, et al. Multi-detector spiral CT study of the relationships between pulmonary ground-glass nodules and blood vessels. Eur Radiol, 2013, 23(12): 3271-3277.
42. Fan L, Liu SY, Li QC, et al. Multidetector CT features of pulmonary focal ground-glass opacity: Differences between benign and malignant. Br J Radiol, 2012, 85(1015): 897-904.
43. Zhai Y, Hui Z, Men Y, et al. Combined neat model for the prognosis of postoperative stage Ⅲ-N2 non-small cell lung cancer. Thorac Cancer, 2020, 11(9): 2610-2617.
44. Yoshimura A, Uchino J, Hasegawa K, et al. Carcinoembryonic antigen and CYFRA 21-1 responses as prognostic factors in advanced non-small cell lung cancer. Transl Lung Cancer Res, 2019, 8(3): 227-234.
45. Tomita M, Ayabe T, Maeda R, et al. Serum carcinoembryonic antigen level predicts cancer-specific outcomes of resected non-small cell lung cancer with interstitial pneumonia. World J Oncol, 2018, 9(5-6): 136-140.
46. Baek AR, Seo HJ, Lee JH, et al. Prognostic value of baseline carcinoembryonic antigen and cytokeratin 19 fragment levels in advanced non-small cell lung cancer. Cancer Biomark, 2018, 22(1): 55-62.
47. Doweck I, Barak M, Uri N, et al. The prognostic value of the tumour marker Cyfra 21-1 in carcinoma of head and neck and its role in early detection of recurrent disease. Br J Cancer, 2000, 83(12): 1696-1701.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Invasiveness evaluation of pulmonary ground-glass nodules by CT features combined with tumor markers: A retrospective cohort study

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