Progress in diagnosis of pulmonary ground-glass opacity nodules by radiomic analysis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LI Hong ¹ , HAN Lu ¹ , LI Xiang ¹ , YU Liang ¹ , YUAN Mei ² , GU Wanjun ³ , CHEN Liang ² ,  WANG Jun ²

1. Nanjing Medical University, Nanjing, 210029, P.R.China;
2. The First Clinical Medical College of Nanjing Medical University, Nanjing, 210029, P.R.China;
3. Department of Biomedical Engineering, Southeast University, Nanjing, 210029, P.R.China;

Corresponding author：

WANG Jun, Email: drwangjun@njmu.edu.cn

Keywords：

Radiomics; ground-glass opacity nodules; CT

DOI：

10.7507/1007-4848.201812001

Video：

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Differential diagnosis of benign and malignant ground glass nodule (GGN) is of great significance to the early detection, diagnosis and treatment of lung cancer. Increasing attention has been paid to radiomics technology application in early diagnosis of benign and malignant GGN, which can analyze the characteristic appearances of GGN in non-invasive manner. This article reviews the latest research progress of radiomics in the diagnosis of GGN.

Citation： LI Hong, HAN Lu, LI Xiang, YU Liang, YUAN Mei, GU Wanjun, CHEN Liang, WANG Jun. Progress in diagnosis of pulmonary ground-glass opacity nodules by radiomic analysis. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2019, 26(8): 805-809. doi: 10.7507/1007-4848.201812001 Copy

1.	Paing M P, Choomchuay S. Ground glass opacity (GGO) nodules detection from lung CT scans. International Symposium on Electronics and Smart Devices, 2017: 230-235.
2.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
3.	Austin JH, Müller NL, Friedman PJ, et al. Glossary of terms for CT of the lungs: recommendations of the Nomenclature Committee of the Fleischner Society. Radiology, 1996, 200(2): 327-331.
4.	Lee HY, Lee KS. Ground-glass opacity nodules: histopathology, imaging evaluation, and clinical implications. Thorac Imaging, 2011, 26(2): 106-118.
5.	Ramiporta R, Bolejack V, Crowley J, et al. The IASLC Lung Cancer Staging Project: Proposals for the Revisions of the T Descriptors in the Forthcoming Eighth Edition of the TNM Classification for Lung Cancer. J Thorac Oncol, 2015, 10(7): 990-1003.
6.	Dmsci JH, Saghir Z, Wille MM, et al. Ground-glass opacity lung nodules in the era of lung cancer ct screening: radiology, pathology, and clinical management. Oncol, 2016, 30(3): 266-274.
7.	Gao JW, Rizzo S, Ma LH, et al. Pulmonary ground-glass opacity: computed tomography features, histopathology and molecular pathology. Transl Lung Cancer Res, 2017, 6(1): 68-75.
8.	Engeler CE, Tashjian JH, Trenkner SW, et al. Ground-glass opacity of the lung parenchyma: a guide to analysis with high-resolution CT. Am J Roentgenol, 1993, 160(2): 249-251.
9.	Lee SM, Park CM, Goo JM, et al. Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as ground-glass nodules: differentiation by using CT features. Radiol, 2013, 268(1): 265-273.
10.	Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006.
11.	Sun F, Xi J, Zhan C, et al. Ground glass opacities: Imaging, pathology and gene mutations. J Thorac Cardiovasc Surg, 2018, 156(2): 808-813.
12.	Robert J. Gillies, Paul E. Kinahan, Hedvig hricak, et al Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
13.	Yuan M, Zhang YD, Pu XH, et al. Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol, 2017, 27(11): 4857-4865.
14.	Zhang X, Xu X, Tian Q, et al. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging, 2017, 46(5): 1281-1288.
15.	Huang Y, Liu Z, He L, et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology, 2016, 281(3): 947-957.
16.	Coroller TP, Grossmann P, Hou Y, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol, 2015, 114(3): 345-350.
17.	Sun R, Limkin EJ, Vakalopoulou M, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol, 2018, 19(9): 1180-1191.
18.	Yang Y, Yang Y, Zhou X, et al. EGFR L858R mutation is associated with lung adenocarcinoma patients with dominant ground-glass opacity. Lung Cancer, 2015, 87(3): 272-277.
19.	Dai J, Zhang P, Yang Y, et al. F-043 the predictive value of radiologic features of ground-glass opacity on tumour invasiveness and epidermal growth factor receptor mutation. Inter Cardiovasc Thorac Surg, 2015, 21(suppl_1): S12-S13.
20.	Mackin D, Fave X, Zhang L, et al. Measuring computed tomography scanner variability of radiomics features. Invest Radiol, 2015, 50(11): 757-765.
21.	Suzuki K, Li F, Sone S, Doi K. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose ct by use of massive training artificial neural network. IEEE Transactions on Medical Imaging, 2005, 24(9): 1138-1150.
22.	Kumar V, Gu YH, Basu S, et al. QIN " Radiomics: The Process and the Challenges”. Magn Reson Imaging, 2012, 30(9): 1234-1248.
23.	Muller-Gartner HW, Links JM, Prince JL, et al. Measurement of radiotracer concentration in brain gray matter using positron emission tomography: mri-based correction for partial volume effects. J Cereb Blood Flow Metab, 1992, 12(4): 571-583.
24.	Zhang YJ. A survey on evaluation methods for image segmentation. Pattern Recognition, 1996, 28(8): 1335-1346.
25.	Vincent L, Soille P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1991, 13(6): 583-598.
26.	Sethian JA. Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. 2nd ed. Cambridge: Cambridge University Press, 1999.
27.	Pal NR, Pal SK. A review on image segmentation techniques. Pattern Recognition, 1993, 26(9): 1277-1294.
28.	Malladi R, Sethian JA, Vemuri BC. Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell, 1995, 17(2): 158-175.
29.	Yamamoto S, Korn RL, Oklu R, et al. Kuo ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization. Radiology, 2014, 272(2): 568-576.
30.	Wu T, Zhou F, Soodeen-Lalloo AK, et al. The association between imaging features of tsct and the expression of pd-l1 in patients with surgical resection of lung adenocarcinoma. Clin Lung Cancer, 2018 Nov 14. doi: 10.1016/j.cllc.2018.10.012.[Epubaheadofprint].
31.	Zhou M, Leung A, Echegaray S, et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology, 2017, 286(1): 307-315.
32.	Gruetzemacher, Richard, Gupta, et al. U. 2016, AMCIS2016, Intelligence and Intelligent Systems (SIsing deep learning for pulmonary nodule detection & diagnosis GODIS).
33.	Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 2018, 24(10): 1559-1567.
34.	Lambin P, Rth L, Deist T M, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology, 2017, 14(12): 749.
35.	Kalpathycramer J, Mamomov A, Zhao B, et al. Radiomics of lung nodules: a multi-institutional study of robustness and agreement of quantitative imaging features. Tomography, 2016, 2(4): 430-437.
36.	Lee G, Park H, Sohn I, et al. Comprehensive computed tomography radiomics analysis of lung adenocarcinoma for prognostication. Oncologist, 2018, 23(7): 806-813.
37.	Li J, Xia T, Yang X, et al. Malignant solitary pulmonary nodules: assessment of mass growth rate and doubling time at follow-up CT. J Thorac Dis, 2018, 10(S7): S797-S806.
38.	Qin L, Gavrielides MA, Sahiner B, et al. Statistical analysis of lung nodule volume measurements with CT in a large-scale phantom study. Medical Physics, 2015, 42(7): 3932.
39.	Han D, Heuvelmans M, Vliegenthart R, et al. Influence of lung nodule margin on volume- and diameter-based reader variability in CT lung cancer screening. Br J Radiol, 2018, 91(1090): 20170405.
40.	Bae KT, Fuangtharnthip P, Prasad SR, et al. Adrenal masses: CT characterization with histogram analysis method. Radiology, 2003, 228(3): 735-742.
41.	Ko JP, Suh J, Ibidapo O, et al. Lung Adenocarcinoma: correlation of quantitative ct findings with pathologic findings. Radiology, 2016, 280(3): 931-939.
42.	Son JY, Lee HY, Lee KS, et al. Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma. PLoS One, 2014, 9(8): e104066.
43.	Bak SH, Lee HY, Kim JH, et al. Quantitative CT scanning analysis of pure ground-glass opacity nodules predicts further ct scanning change. Chest, 2016, 149(1): 180-191.
44.	Fried DV, Tucker SL, Zhou S, et al. Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. Int J Radiat Oncol Biol Phys, 2014, 90(4): 834-842.
45.	Ganeshan B, Abaleke S, Young RC, et al. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging, 2010, 10(1): 137-143.
46.	O’Connor JP, Rose CJ, Waterton JC, et al. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res, 2015, 21(2): 249-257.
47.	Suzuki K, Li F, Sone S, et al. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans Med Imaging, 2005, 24(9): 1138-1150.
48.	Zhao W, Yang J, Sun Y, et al. 3D Deep Learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas. Cancer Res, 2018, 78(24): 6881-6889.
49.	Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Annals of Oncology, 2017, 28(6): 1191-1206.

1. Paing M P, Choomchuay S. Ground glass opacity (GGO) nodules detection from lung CT scans. International Symposium on Electronics and Smart Devices, 2017: 230-235.
2. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
3. Austin JH, Müller NL, Friedman PJ, et al. Glossary of terms for CT of the lungs: recommendations of the Nomenclature Committee of the Fleischner Society. Radiology, 1996, 200(2): 327-331.
4. Lee HY, Lee KS. Ground-glass opacity nodules: histopathology, imaging evaluation, and clinical implications. Thorac Imaging, 2011, 26(2): 106-118.
5. Ramiporta R, Bolejack V, Crowley J, et al. The IASLC Lung Cancer Staging Project: Proposals for the Revisions of the T Descriptors in the Forthcoming Eighth Edition of the TNM Classification for Lung Cancer. J Thorac Oncol, 2015, 10(7): 990-1003.
6. Dmsci JH, Saghir Z, Wille MM, et al. Ground-glass opacity lung nodules in the era of lung cancer ct screening: radiology, pathology, and clinical management. Oncol, 2016, 30(3): 266-274.
7. Gao JW, Rizzo S, Ma LH, et al. Pulmonary ground-glass opacity: computed tomography features, histopathology and molecular pathology. Transl Lung Cancer Res, 2017, 6(1): 68-75.
8. Engeler CE, Tashjian JH, Trenkner SW, et al. Ground-glass opacity of the lung parenchyma: a guide to analysis with high-resolution CT. Am J Roentgenol, 1993, 160(2): 249-251.
9. Lee SM, Park CM, Goo JM, et al. Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as ground-glass nodules: differentiation by using CT features. Radiol, 2013, 268(1): 265-273.
10. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006.
11. Sun F, Xi J, Zhan C, et al. Ground glass opacities: Imaging, pathology and gene mutations. J Thorac Cardiovasc Surg, 2018, 156(2): 808-813.
12. Robert J. Gillies, Paul E. Kinahan, Hedvig hricak, et al Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
13. Yuan M, Zhang YD, Pu XH, et al. Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol, 2017, 27(11): 4857-4865.
14. Zhang X, Xu X, Tian Q, et al. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging, 2017, 46(5): 1281-1288.
15. Huang Y, Liu Z, He L, et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology, 2016, 281(3): 947-957.
16. Coroller TP, Grossmann P, Hou Y, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol, 2015, 114(3): 345-350.
17. Sun R, Limkin EJ, Vakalopoulou M, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol, 2018, 19(9): 1180-1191.
18. Yang Y, Yang Y, Zhou X, et al. EGFR L858R mutation is associated with lung adenocarcinoma patients with dominant ground-glass opacity. Lung Cancer, 2015, 87(3): 272-277.
19. Dai J, Zhang P, Yang Y, et al. F-043 the predictive value of radiologic features of ground-glass opacity on tumour invasiveness and epidermal growth factor receptor mutation. Inter Cardiovasc Thorac Surg, 2015, 21(suppl_1): S12-S13.
20. Mackin D, Fave X, Zhang L, et al. Measuring computed tomography scanner variability of radiomics features. Invest Radiol, 2015, 50(11): 757-765.
21. Suzuki K, Li F, Sone S, Doi K. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose ct by use of massive training artificial neural network. IEEE Transactions on Medical Imaging, 2005, 24(9): 1138-1150.
22. Kumar V, Gu YH, Basu S, et al. QIN " Radiomics: The Process and the Challenges”. Magn Reson Imaging, 2012, 30(9): 1234-1248.
23. Muller-Gartner HW, Links JM, Prince JL, et al. Measurement of radiotracer concentration in brain gray matter using positron emission tomography: mri-based correction for partial volume effects. J Cereb Blood Flow Metab, 1992, 12(4): 571-583.
24. Zhang YJ. A survey on evaluation methods for image segmentation. Pattern Recognition, 1996, 28(8): 1335-1346.
25. Vincent L, Soille P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1991, 13(6): 583-598.
26. Sethian JA. Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. 2nd ed. Cambridge: Cambridge University Press, 1999.
27. Pal NR, Pal SK. A review on image segmentation techniques. Pattern Recognition, 1993, 26(9): 1277-1294.
28. Malladi R, Sethian JA, Vemuri BC. Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell, 1995, 17(2): 158-175.
29. Yamamoto S, Korn RL, Oklu R, et al. Kuo ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization. Radiology, 2014, 272(2): 568-576.
30. Wu T, Zhou F, Soodeen-Lalloo AK, et al. The association between imaging features of tsct and the expression of pd-l1 in patients with surgical resection of lung adenocarcinoma. Clin Lung Cancer, 2018 Nov 14. doi: 10.1016/j.cllc.2018.10.012.[Epubaheadofprint].
31. Zhou M, Leung A, Echegaray S, et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology, 2017, 286(1): 307-315.
32. Gruetzemacher, Richard, Gupta, et al. U. 2016, AMCIS2016, Intelligence and Intelligent Systems (SIsing deep learning for pulmonary nodule detection & diagnosis GODIS).
33. Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 2018, 24(10): 1559-1567.
34. Lambin P, Rth L, Deist T M, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology, 2017, 14(12): 749.
35. Kalpathycramer J, Mamomov A, Zhao B, et al. Radiomics of lung nodules: a multi-institutional study of robustness and agreement of quantitative imaging features. Tomography, 2016, 2(4): 430-437.
36. Lee G, Park H, Sohn I, et al. Comprehensive computed tomography radiomics analysis of lung adenocarcinoma for prognostication. Oncologist, 2018, 23(7): 806-813.
37. Li J, Xia T, Yang X, et al. Malignant solitary pulmonary nodules: assessment of mass growth rate and doubling time at follow-up CT. J Thorac Dis, 2018, 10(S7): S797-S806.
38. Qin L, Gavrielides MA, Sahiner B, et al. Statistical analysis of lung nodule volume measurements with CT in a large-scale phantom study. Medical Physics, 2015, 42(7): 3932.
39. Han D, Heuvelmans M, Vliegenthart R, et al. Influence of lung nodule margin on volume- and diameter-based reader variability in CT lung cancer screening. Br J Radiol, 2018, 91(1090): 20170405.
40. Bae KT, Fuangtharnthip P, Prasad SR, et al. Adrenal masses: CT characterization with histogram analysis method. Radiology, 2003, 228(3): 735-742.
41. Ko JP, Suh J, Ibidapo O, et al. Lung Adenocarcinoma: correlation of quantitative ct findings with pathologic findings. Radiology, 2016, 280(3): 931-939.
42. Son JY, Lee HY, Lee KS, et al. Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma. PLoS One, 2014, 9(8): e104066.
43. Bak SH, Lee HY, Kim JH, et al. Quantitative CT scanning analysis of pure ground-glass opacity nodules predicts further ct scanning change. Chest, 2016, 149(1): 180-191.
44. Fried DV, Tucker SL, Zhou S, et al. Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. Int J Radiat Oncol Biol Phys, 2014, 90(4): 834-842.
45. Ganeshan B, Abaleke S, Young RC, et al. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging, 2010, 10(1): 137-143.
46. O’Connor JP, Rose CJ, Waterton JC, et al. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res, 2015, 21(2): 249-257.
47. Suzuki K, Li F, Sone S, et al. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans Med Imaging, 2005, 24(9): 1138-1150.
48. Zhao W, Yang J, Sun Y, et al. 3D Deep Learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas. Cancer Res, 2018, 78(24): 6881-6889.
49. Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Annals of Oncology, 2017, 28(6): 1191-1206.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Progress in diagnosis of pulmonary ground-glass opacity nodules by radiomic analysis

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