[Home ] [Archive]    
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
IJRR Information::
For Authors::
For Reviewers::
News & Events::
Web Mail::
Search in website

Advanced Search
Receive site information
Enter your Email in the following box to receive the site news and information.
Hard Copy 2322-3243
Online 2345-4229
Online Submission
Now you can send your articles to IJRR office using the article submission system.



:: Volume 20, Issue 4 (10-2022) ::
Int J Radiat Res 2022, 20(4): 737-745 Back to browse issues page
The significance of peritumoral 5mm regions features for radiomics model in distinguishing the lung adenocarcinomas and granulomas
Y. Geng , L. Sun , M. Sun , Z. Zhang , J. Liu
Department of Radiology, The fourth Affiliated Hospital of China Medical University, Shengyang, China , 1403952319@qq.com
Abstract:   (955 Views)
Background: To investigate whether features of 5-mm peritumoral regions could significantly improve the predictive efficacy of a radiomics model based on solid pulmonary tumors at distinguishing lung adenocarcinoma(LAC) from granuloma(GR). Materials and Methods: We retrospectively evaluated 167 lung tumors pathologically proven to be LAC (96) or GR (71) and divided them into training group (116) and testing (51) group. We delineated each tumor with three different measures using the tumor and its 5-mm peritumoral region. Then, we extracted 465 features from each volume of interest(VOI) and chose the optimal features to build the diagnostic models. We built four different models using different methods. Finally, we compared the performance of the four models in the test set. Results: The area under the curve(AUC) of each model in the test group was 0.765 (95% confidence interval(CI): 0.620–0.909), 0.797 (95%CI: 0.670–0.924), and 0.784 (95%CI: 0.647–0.920), respectively. Results of the DeLong test showed that the differences between model 2, model 3, and model 1 were not significant. Results of net reclassification improvement(NRI) showed that model 2 and model 3 had better differential diagnostic efficacy than model 1, with accuracies(ACCs) of 0.784, 0.745, and 0.686, respectively, but the differences were not significant (P>0.05). Moreover, the nomogram had good diagnostic and predictive abilities, with an AUC of 0.848 (95%CI: 0.736–0.961) and an ACC of 0.804. Conclusions: Features of 5-mm peritumoral regions improved the predictive ability of the radiomics model based on the solid pulmonary tumor, but the difference was not significant.
Keywords: lung adenocarcinomas, granulomas, radiomics, nomogram, machine learning.
Full-Text [PDF 2189 kb]   (533 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
1. Bosetti C, Traini E, Alam T, Allen CA, Carreras G, Compton K, et al. (2020) National burden of cancer in Italy, 1990-2017: a systematic analysis for the global burden of disease study 2017. Sci Rep, 10(1): 22099. [DOI:10.1038/s41598-020-79176-3] [PMID] []
2. Yang X, Man J, Chen H, Zhang T, Yin X, He Q, et al. (2021) Temporal trends of the lung cancer mortality attributable to smoking from 1990 to 2017: A global, regional and national analysis. Lung Cancer, 152: 49-57. [DOI:10.1016/j.lungcan.2020.12.007] [PMID]
3. Oudkerk M, Liu S, Heuvelmans MA, Walter JE, Field JK (2021) Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives. Nat Rev Clin Oncol, 18(3): 135-51. [DOI:10.1038/s41571-020-00432-6] [PMID]
4. Chen CH, Chang CK, Tu CY, Liao WC, Wu BR, Chou KT, et al. (2018) Radiomic features analysis in computed tomography images of lung nodule classification. PLoS One, 13(2): e0192002. [DOI:10.1371/journal.pone.0192002] [PMID] []
5. Thorsteinsson H, Alexandersson A, Oskarsdottir GN, Skuladottir R, Isaksson HJ, Jonsson S, et al. (2012) Resection rate and outcome of pulmonary resections for non-small-cell lung cancer: a nationwide study from Iceland. J Thorac Oncol, 7(7): 1164-9. [DOI:10.1097/JTO.0b013e318252d022] [PMID]
6. Qvick A, Stenmark B, Carlsson J, Isaksson J, Karlsson C, Helenius G (2021) Liquid biopsy as an option for predictive testing and prognosis in patients with lung cancer. Molecular Medicine, 27(1): 68. https://doi.org/10.21203/rs.3.rs-174609/v1 [DOI:10.1186/s10020-021-00331-1]
7. Kim H, Park CM, Goo JM, Wildberger JE, Kauczor HU (2015) Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer. Invest Radiol, 50(9): 571-83. [DOI:10.1097/RLI.0000000000000152] [PMID]
8. Bach PB, Mirkin JN, Oliver TK, Azzoli CG, Berry DA, Brawley OW, et al. (2012) Benefits and harms of CT screening for lung cancer: a systematic review. JAMA, 307(22): 2418-29. [DOI:10.1001/jama.2012.5521] [PMID] []
9. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 48(4): 441-6. [DOI:10.1016/j.ejca.2011.11.036] [PMID] []
10. Gillies R, Kinahan P, Hricak H (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology, 278(2): 563-77. [DOI:10.1148/radiol.2015151169] [PMID] []
11. Yang X, He J, Wang J, Li W, Liu C, Gao D, et al. (2018) CT-based radiomics signature for differentiating solitary granulomatous nodules from solid lung adenocarcinoma. (Amsterdam, Netherlands) Lung cancer, 125: 109-14. [DOI:10.1016/j.lungcan.2018.09.013] [PMID]
12. Feng B, Chen X, Chen Y, Liu K, Li K, Liu X, et al. (2020) Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule. Eur J Radiol, 128: 109022. [DOI:10.1016/j.ejrad.2020.109022] [PMID]
13. Alvarez-Jimenez C, Sandino AA, Prasanna P, Gupta A, Viswanath SE, Romero E (2020) Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results. Cancers (Basel), 12(12). [DOI:10.3390/cancers12123663] [PMID] []
14. Wu G, Woodruff HC, Shen J, Refaee T, Sanduleanu S, Ibrahim A, et al. (2020) Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A multicenter study. Radiology, 297(2): E282. https://doi.org/10.1148/radiol.2020192431 [DOI:10.1148/radiol.2020209019] [PMID]
15. Wang X, Zhao X, Li Q, Xia W, Peng Z, Zhang R, et al. (2019) Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT? European radiology, 29(11): 6049-58. [DOI:10.1007/s00330-019-06084-0] [PMID]
16. Beig N, Khorrami M, Alilou M, Prasanna P, Braman N, Orooji M, et al. (2019) Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology, 290(3): 783-92. [DOI:10.1148/radiol.2018180910] [PMID] []
17. Wu S, Zhang N, Wu Z, Ren J, Lining E (2022) Can Peritumoral Radiomics Improve the Prediction of Malignancy of Solid Pulmonary Nodule Smaller Than 2 cm. Acad Radiol, 29(2): S47-S52. [DOI:10.1016/j.acra.2020.10.029] [PMID]
18. Wang R, Liu H, Liang P, Zhao H, Li L, Gao J (2021) Radiomics analysis of CT imaging for differentiating gastric neuroendocrine carcinomas from gastric adenocarcinomas. Eur J Radiol, 138: 109662. [DOI:10.1016/j.ejrad.2021.109662] [PMID]
19. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts H (2015) Machine Learning methods for Quantitative Radiomic Biomarkers. Sci Rep, 5: 13087. [DOI:10.1038/srep13087] [PMID] []
20. Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ (2015) Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol, 5: 272. [DOI:10.3389/fonc.2015.00272] [PMID] []
21. Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell, 27(8): 1226-38. [DOI:10.1109/TPAMI.2005.159] [PMID]
22. Lu X, Li M, Zhang H, Hua S, Meng F, Yang H, et al. (2020) A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma. Phys Med Biol, 65(5): 055012. [DOI:10.1088/1361-6560/ab6f98] [PMID]
23. Ren C, Zhang J, Qi M, Zhang J, Zhang Y, Song S, et al. (2021) Machine learning based on clinico-biological features integrated (18)F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung. Eur J Nucl Med Mol Imaging, 48(5):1538-49. [DOI:10.1007/s00259-020-05065-6] [PMID] []
24. Wu J, Sun X, Wang J, Cui Y, Kato F, Shirato H, et al. (2017) Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation. Journal of magnetic resonance imaging: JMRI, 46(4): 1017-27. [DOI:10.1002/jmri.25661] [PMID] []
25. Xiangmeng C, Bao F, Yehang C, Kunfeng L, Kunwei L, Xiaobei D, et al. (2020) A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules. %J Cancer imaging: The official publication of the International Cancer Imaging Society, 20(1). [DOI:10.1186/s40644-020-00320-3] [PMID] []
26. Zhuo Y, Zhan Y, Zhang, Shan F, Shen J, Wang D, Yu M (2021) Clinical and CT radiomics nomogram for preoperative differentiation of pulmonary adenocarcinoma from tuberculoma in solitary solid nodule. Frontiers in Oncology, 2021: 11. [DOI:10.3389/fonc.2021.701598] [PMID] []
27. Wu L, Gao C, Xiang P, Zheng S, Pang P, Xu M (2020) CT-Imaging based analysis of invasive lung adenocarcinoma presenting as ground glass nodules using Peri- and Intra-nodular radiomic features. Frontiers in Oncology, 2020: 10. [DOI:10.3389/fonc.2020.00838] [PMID] []
28. Salem A, Asselin MC, Reymen B, Jackson A, Lambin P, West CML, et al. (2018) Targeting hypoxia to improve Non-Small cell lung cancer outcome. J Natl Cancer Inst, 110(1). [DOI:10.1093/jnci/djx160] [PMID]
29. Cheng C, Yang Y, Yang W, Wang D, Yao C (2021) The diagnostic value of CEA for lung cancer-related malignant pleural effusion in China: a meta-analysis. Expert Rev Respir Med, 2021: 1-10. [DOI:10.1080/17476348.2021.1941885] [PMID]
30. Xie H, Kong YX, Zhang Q (2019) Value of Serum Tumor Marker Isocitrate Dehydrogenase 1 in the Diagnosis of Lung Cancer. Lung Cancer, 41(6): 813-7.
31. Rocco G, Morabito A, Leone A, Muto P, Fiore F, Budillon A (2016) Management of non-small cell lung cancer in the era of personalized medicine. Int J Biochem Cell Biol, 78: 173-9. [DOI:10.1016/j.biocel.2016.07.011] [PMID]
32. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. (2012) Radiomics: the process and the challenges. Magn Reson Imaging, 30(9): 1234-48. [DOI:10.1016/j.mri.2012.06.010] [PMID] []
33. Zwanenburg A (2019) Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. European Journal of Nuclear Medicine and Molecular Imaging, 46(13): 2638-55. [DOI:10.1007/s00259-019-04391-8] [PMID]
34. Lee G, Bak S, Lee H, Choi J, Park H, Lee S, et al. (2019) Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements Using Radiomics. Journal of Thoracic Imaging , 34(2): 103-15. [DOI:10.1097/RTI.0000000000000390] [PMID]
Send email to the article author

Add your comments about this article
Your username or Email:


XML     Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Geng Y, Sun L, Sun M, Zhang Z, Liu J. The significance of peritumoral 5mm regions features for radiomics model in distinguishing the lung adenocarcinomas and granulomas. Int J Radiat Res 2022; 20 (4) :737-745
URL: http://ijrr.com/article-1-4458-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 20, Issue 4 (10-2022) Back to browse issues page
International Journal of Radiation Research
Persian site map - English site map - Created in 0.06 seconds with 50 queries by YEKTAWEB 4657