The impact of lung nodule centerline and related parameters on the prognosis of non-small cell lung cancer patients with surgery based on the NLST database_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

GAO Xianglong ^1,2 , HU Junxi ^1,2 , WENG Xiaoyao ^1,2 , YAO Shaowen ^1,2 ,  LU Shichun ^1,2

1. School of Medicine, Yangzhou University, Yangzhou, 225001, Jiangsu, P. R. China;
2. Department of Thoracic Surgery, Northern Jiangsu People’s Hospital, Yangzhou, 225001, Jiangsu, P. R. China;

Corresponding author：

LU Shichun, Email: yzchest@163.com

Keywords：

Centerline; lung nodule; Mimics software; disease-free survival; overall survival

DOI：

10.7507/1007-4848.202201077

Video：

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Objective To evaluate the predictive performance of the geometric characteristics, centerline (CL) of pulmonary nodules for prognosis in patients with surgically treatment in the National Lung Screening Trial (NLST). Methods CT images of 178 patients who underwent surgical treatment and were diagnosed with non-small cell lung cancer (NSCLC) in the low-dose CT (LDCT) cohort from the NLST image database were selected, including 99 males and 79 females, with a median age of 64 (59, 68) years. CT images were processed using commercial software Mimics 21.0 to record the volume, surface area, CL and the area perpendicular to the centerline of pulmonary nodules. Receiver operating characteristic (ROC) curve was used to compare the predictive performance of LD, AD and CL on prognosis. Univariate Cox regression was used to explore the influencing factors for postoperative disease-free survival (DFS) and overall survival (OS), and meaningful independent variables were included in the multivariate Cox regression to construct the prediction model. Results The area under the curve (AUC) of CL for postoperative recurrence and death were 0.650 and 0.719, better than LD (0.596, 0.623) and AD (0.600, 0.631). Multivariate Cox proportional risk regression analysis showed that pulmonary nodule volume (P=0.010), the maximum area perpendicular to the centerline (MApc) (P=0.028) and lymph node metastasis (P<0.001) were independent risk factors for DFS. Meanwhile, age (P=0.010), CL (P=0.043), lymph node metastasis (P<0.001), MApc (P=0.022) and the average area perpendicular to the centerline (AApc) (P=0.016) were independently associated with OS. Conclusion For the postoperative outcomes of NSCLC patients in the LDCT cohort of the NLST, the CL of the pulmonary nodule prediction performance for prognosis is superior to the LD and AD, CL can effectively predict the risk stratification and prognosis of lung cancer, and spheroid tumors have a better prognosis.

Citation： GAO Xianglong, HU Junxi, WENG Xiaoyao, YAO Shaowen, LU Shichun. The impact of lung nodule centerline and related parameters on the prognosis of non-small cell lung cancer patients with surgery based on the NLST database. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(9): 1148-1155. doi: 10.7507/1007-4848.202201077 Copy

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2.	Park S, Lee SM, Choe J, et al. CT evaluation for clinical lung cancer staging: Do multiplanar measurements better reflect pathologic T-stage than Axial measurements? Korean J Radiol, 2019, 20(7): 1207-1215.
3.	Kamran SC, Coroller T, Milani N, et al. The impact of quantitative CT-based tumor volumetric features on the outcomes of patients with limited stage small cell lung cancer. Radiat Oncol, 2020, 15(1): 14.
4.	Walter JE, Heuvelmans MA, Bock GH, et al. Characteristics of new solid nodules detected in incidence screening rounds of low-dose CT lung cancer screening: The NELSON study. Thorax, 2018, 73(8): 741-747.
5.	Heuvelmans MA, Walter JE, Vliegenthart R, et al. Disagreement of diameter and volume measurements for pulmonary nodule size estimation in CT lung cancer screening. Thorax, 2018, 73(8): 779-781.
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12.	Li WJ, Lv FJ, Tan YW, et al. Benign and malignant pulmonary part-solid nodules: Differentiation via thin-section computed tomography. Quant Imaging Med Surg, 2022, 12(1): 699-710.
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14.	Morgant MC, Pagès PB, Orsini B, et al. Time trends in surgery for lung cancer in France from 2005 to 2012: A nationwide study. Eur Respir J, 2015, 46(4): 1131-1139.
15.	Pagès PB, Cottenet J, Mariet AS, et al. In-hospital mortality following lung cancer resection: Nationwide administrative database. Eur Respir J, 2016, 47(6): 1809-1817.
16.	Mery CM, Pappas AN, Burt BM, et al. Diameter of non-small cell lung cancer correlates with long-term survival: Implications for T stage. Chest, 2005, 128(5): 3255-3260.
17.	Davey A, van Herk M, Faivre-Finn C, et al. Is tumour sphericity an important prognostic factor in patients with lung cancer? Radiother Oncol, 2020, 143: 73-80.
18.	Li Q, Kim J, Balagurunathan Y, et al. CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy. Radiat Oncol, 2017, 12(1): 158.
19.	Tarsitano A, Ricotta F, Cercenelli L, et al. Pretreatment tumor volume and tumor sphericity as prognostic factors in patients with oral cavity squamous cell carcinoma. J Craniomaxillofac Surg, 2019, 47(3): 510-515.
20.	Tanner NT, Dai L, Bade BC, et al. Assessing the generalizability of the National Lung Screening Trial: Comparison of patients with stage 1 disease. Am J Respir Crit Care Med, 2017, 196(5): 602-608.
21.	US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for lung cancer: US preventive services task force recommendation statement. JAMA, 2021, 325(10): 962-970.
22.	Kim H, Goo JM, Kim YT, et al. CT-defined visceral pleural invasion in T1 lung adenocarcinoma: Lack of relationship to disease-free survival. Radiology, 2019, 292(3): 741-749.
23.	Sheikh M, Mukeriya A, Shangina O, et al. Postdiagnosis smoking cessation and reduced risk for lung cancer progression and mortality: A prospective cohort study. Ann Intern Med, 2021, 174(9): 1232-1239.
24.	Micke P, Mattsson JS, Djureinovic D, et al (eds). WHO Classification of Lung Tumours Pleura, Thymus and Heart, 4th Edition. Lyon: IARC Press, 2015.
25.	Yu M, Wang Z, Yang G, et al. A model of malignant risk prediction for solitary pulmonary nodules on 18 F-FDG PET/CT: Building and estimating. Thorac Cancer, 2020, 11(5): 1211-1215.
26.	Kamiya S, Iwano S, Umakoshi H, et al. Computer-aided volumetry of part-solid lung cancers by using CT: Solid component size predicts prognosis. Radiology, 2018, 287(3): 1030-1040.
27.	Cody DD, Kim HJ, Cagnon CH, et al. Normalized CT dose index of the CT scanners used in the national lung screening trial. AJR Am J Roentgenol, 2010, 194(6): 1539-1546.
28.	Yoshida Y, Yanagawa M, Hata A, et al. Quantitative volumetry of ground-glass nodules on high-spatial-resolution CT with 0. 25-mm section thickness and 1024 matrix: Phantom and clinical studies. Eur J Radiol Open, 2021, 8: 100362.

1. Bankier AA, MacMahon H, Goo JM, et al. Recommendations for measuring pulmonary nodules at CT: A statement from the Fleischner Society. Radiology, 2017, 285(2): 584-600.
2. Park S, Lee SM, Choe J, et al. CT evaluation for clinical lung cancer staging: Do multiplanar measurements better reflect pathologic T-stage than Axial measurements? Korean J Radiol, 2019, 20(7): 1207-1215.
3. Kamran SC, Coroller T, Milani N, et al. The impact of quantitative CT-based tumor volumetric features on the outcomes of patients with limited stage small cell lung cancer. Radiat Oncol, 2020, 15(1): 14.
4. Walter JE, Heuvelmans MA, Bock GH, et al. Characteristics of new solid nodules detected in incidence screening rounds of low-dose CT lung cancer screening: The NELSON study. Thorax, 2018, 73(8): 741-747.
5. Heuvelmans MA, Walter JE, Vliegenthart R, et al. Disagreement of diameter and volume measurements for pulmonary nodule size estimation in CT lung cancer screening. Thorax, 2018, 73(8): 779-781.
6. Data from lung_phantom. The cancer imaging archive. URL: https://doi.org/10.7937/K9/TCIA.2015.08A1IXOO.
7. Kirby J, Prior F, Petrick N, et al. Introduction to special issue on datasets hosted in The Cancer Imaging Archive (TCIA). Med Phys, 2020, 47(12): 6026-6028.
8. Nair VS, Sundaram V, Desai M, et al. Accuracy of models to identify lung nodule cancer risk in the National Lung Screening Trial. Am J Respir Crit Care Med, 2018, 197(9): 1220-1223.
9. NIH National Cancer Institute Cancer Data Access System. URL: https://biometry.nci.nih.gov/cdas/nlst/, 2021-03-02.
10. Ren H, Zhou L, Liu G, et al. An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing. Quant Imaging Med Surg, 2020, 10(1): 233-242.
11. Instruction for use for Mimics Medical 21.0. URL: http://www.materialise.com/electronic-instructions-for-use, 2021-03-02.
12. Li WJ, Lv FJ, Tan YW, et al. Benign and malignant pulmonary part-solid nodules: Differentiation via thin-section computed tomography. Quant Imaging Med Surg, 2022, 12(1): 699-710.
13. Kim H, Goo JM, Kim YT, et al. Clinical T category of non-small cell lung cancers: Prognostic performance of unidimensional versus bidimensional measurements at CT. Radiology, 2019, 290(3): 807-813.
14. Morgant MC, Pagès PB, Orsini B, et al. Time trends in surgery for lung cancer in France from 2005 to 2012: A nationwide study. Eur Respir J, 2015, 46(4): 1131-1139.
15. Pagès PB, Cottenet J, Mariet AS, et al. In-hospital mortality following lung cancer resection: Nationwide administrative database. Eur Respir J, 2016, 47(6): 1809-1817.
16. Mery CM, Pappas AN, Burt BM, et al. Diameter of non-small cell lung cancer correlates with long-term survival: Implications for T stage. Chest, 2005, 128(5): 3255-3260.
17. Davey A, van Herk M, Faivre-Finn C, et al. Is tumour sphericity an important prognostic factor in patients with lung cancer? Radiother Oncol, 2020, 143: 73-80.
18. Li Q, Kim J, Balagurunathan Y, et al. CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy. Radiat Oncol, 2017, 12(1): 158.
19. Tarsitano A, Ricotta F, Cercenelli L, et al. Pretreatment tumor volume and tumor sphericity as prognostic factors in patients with oral cavity squamous cell carcinoma. J Craniomaxillofac Surg, 2019, 47(3): 510-515.
20. Tanner NT, Dai L, Bade BC, et al. Assessing the generalizability of the National Lung Screening Trial: Comparison of patients with stage 1 disease. Am J Respir Crit Care Med, 2017, 196(5): 602-608.
21. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for lung cancer: US preventive services task force recommendation statement. JAMA, 2021, 325(10): 962-970.
22. Kim H, Goo JM, Kim YT, et al. CT-defined visceral pleural invasion in T1 lung adenocarcinoma: Lack of relationship to disease-free survival. Radiology, 2019, 292(3): 741-749.
23. Sheikh M, Mukeriya A, Shangina O, et al. Postdiagnosis smoking cessation and reduced risk for lung cancer progression and mortality: A prospective cohort study. Ann Intern Med, 2021, 174(9): 1232-1239.
24. Micke P, Mattsson JS, Djureinovic D, et al (eds). WHO Classification of Lung Tumours Pleura, Thymus and Heart, 4th Edition. Lyon: IARC Press, 2015.
25. Yu M, Wang Z, Yang G, et al. A model of malignant risk prediction for solitary pulmonary nodules on 18 F-FDG PET/CT: Building and estimating. Thorac Cancer, 2020, 11(5): 1211-1215.
26. Kamiya S, Iwano S, Umakoshi H, et al. Computer-aided volumetry of part-solid lung cancers by using CT: Solid component size predicts prognosis. Radiology, 2018, 287(3): 1030-1040.
27. Cody DD, Kim HJ, Cagnon CH, et al. Normalized CT dose index of the CT scanners used in the national lung screening trial. AJR Am J Roentgenol, 2010, 194(6): 1539-1546.
28. Yoshida Y, Yanagawa M, Hata A, et al. Quantitative volumetry of ground-glass nodules on high-spatial-resolution CT with 0. 25-mm section thickness and 1024 matrix: Phantom and clinical studies. Eur J Radiol Open, 2021, 8: 100362.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

The impact of lung nodule centerline and related parameters on the prognosis of non-small cell lung cancer patients with surgery based on the NLST database

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