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Original Research

A Nomogram Based on CT Deep Learning Signature: A Potential Tool for the Prediction of Overall Survival in Resected Non-Small Cell Lung Cancer Patients

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Pages 2897-2906 | Published online: 30 Mar 2021

References

  • Travis WD. Pathology of lung cancer. Clin Chest Med. 2011;32(4):669–692. doi:10.1016/j.ccm.2011.08.00522054879
  • Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30. doi:10.3322/caac.2159031912902
  • Chen W, Sun K, Zheng R, et al. Cancer incidence and mortality in China, 2014. Chin J Med Res. 2018;30(1):1–12. doi:10.21147/j.issn.1000-9604.2018.01.01
  • Cheng YI, Davies MPA, Liu D, et al. Implementation planning for Lung cancer screening in China. Precis Clin Med. 2019;2(1):13–44. doi:10.1093/pcmedi/pbz002
  • Butts CA, Ding K, Seymour L, et al. Randomized Phase III trial of vinorelbine plus cisplatin compared with observation in completely resected stage IB and II non-small-cell lung cancer: updated survival analysis of JBR-10. J Clin Oncol. 2010;28(1):29–34. doi:10.1200/JCO.2009.24.033319933915
  • Molinier O, Goupil F, Debieuvre D, et al. Five-year survival and prognostic factors according to histology in 6101 non-small-cell lung cancer patients. Respir Med Res. 2019;77:46–54. doi:10.1016/j.resmer.2019.10.00132036284
  • Ettinger DS, Wood DE, Aggarwal C, et al. NCCN guidelines insights: non-small cell lung cancer, version 1.2020. J Natl Compr Canc Netw. 2019;17(12):1464–1472. doi:10.6004/jnccn.2019.005931805526
  • Kawachi R, Tsukada H, Nakazato Y, et al. Early recurrence after surgical resection in patients with pathological stage I non-small cell lung cancer. Thorac Cardiovasc Surg. 2009;57(8):472–475. doi:10.1055/s-0029-118573420013621
  • Lindenmann J, Fink-Neuboeck N, Taucher V, et al. Prediction of postoperative clinical outcomes in resected stage i non-small cell lung cancer focusing on the preoperative glasgow prognostic score. Cancers. 2020;12(1):152. doi:10.3390/cancers12010152
  • Amin MB, Greene FL, Edge SB, et al. The eighth edition AJCC cancer staging manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA Cancer J Clin. 2017;67(2):93–99. doi:10.3322/caac.2138828094848
  • Zhu L, Chen S, Ma S, et al. Glasgow prognostic score predicts prognosis of non-small cell lung cancer: a meta-analysis. SpringerPlus. 2016;5(1):439. doi:10.1186/s40064-016-2093-927104127
  • Weissman SM. Personalized medicine: a new horizon for medical therapy. Precis Clin Med. 2018;1(1):1–2. doi:10.1093/pcmedi/pby001
  • Li W. Precision medicine: to cure and relieve more. Precis Clin Med. 2018;1(1):3–4. doi:10.1093/pcmedi/pby002
  • Lee G, Lee HY, Park H, et al. Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: state of the art. Eur J Radiol. 2017;86:297–307. doi:10.1016/j.ejrad.2016.09.00527638103
  • Coroller TP, Grossmann P, Hou Y, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiat Oncol. 2015;114(3):345–350. doi:10.1016/j.radonc.2015.02.015
  • Hassani C, Varghese BA, Nieva J, et al. Radiomics in pulmonary lesion Imaging. AJR Am J Roentgenol. 2019;212(3):497–504. doi:10.2214/AJR.18.2062330620678
  • Ninomiya K, Arimura H. Homological radiomics analysis for prognostic prediction in lung cancer patients. Phys Med. 2020;69:90–100. doi:10.1016/j.ejmp.2019.11.02631855844
  • Wang L, Dong T, Xin B, et al. Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer. Eur Radiol. 2019;29(6):2958–2967. doi:10.1007/s00330-018-5949-230643940
  • van Timmeren JE, Leijenaar RTH, van Elmpt W, et al. Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. Radiat Oncol. 2017;123(3):363–369. doi:10.1016/j.radonc.2017.04.016
  • Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–762. doi:10.1038/nrclinonc.2017.14128975929
  • Arimura H, Soufi M, Kamezawa H, et al. Radiomics with artificial intelligence for precision medicine in radiation therapy. J Radiat Res. 2019;60(1):150–157. doi:10.1093/jrr/rry07730247662
  • Wu L, Yang X, Cao W, et al. Multiple level CT radiomics features preoperatively predict lymph node metastasis in esophageal cancer: a Multicentre Retrospective Study. Front Oncol. 2019;9:1548. doi:10.3389/fonc.2019.0154832039021
  • Akay A, Hess H. Deep learning: current and emerging applications in medicine and technology. IEEE J Biomed Health Inform. 2019;23(3):906–920. doi:10.1109/JBHI.2019.289471330676989
  • Mazurowski MA, Buda M, Saha A, et al. Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging. 2019;49(4):939–954. doi:10.1002/jmri.2653430575178
  • Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal. 2015;35:18–31. doi:10.1016/j.media.2016.05.004
  • Wang X, Yang W, Weinreb J, et al. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning. Sci Rep. 2017;7(1):15415. doi:10.1038/s41598-017-15720-y29133818
  • Hosny A, Parmar C, Coroller TP, et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med. 2018;15(11):e1002711. doi:10.1371/journal.pmed.100271130500819
  • Gstoettner M, Sekyra K, Walochnik N, et al. Inter- and intraobserver reliability assessment of the Cobb angle: manual versus digital measurement tools. Eur Spine J. 2007;16(10):1587–1592. doi:10.1007/s00586-007-0401-317549526
  • He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. Paper presented at: IEEE Conference on Computer Vision and Pattern Recognition; Las Vegas, USA; 2016;p. 770–778.
  • Yang X, Wu L, Ye W, et al. Deep learning signature based on staging CT for preoperative prediction of sentinel lymph node metastasis in breast cancer. Acad Radiol. 2020;27(9):1226–1233. doi:10.1016/j.acra.2019.11.00731818648
  • Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–387. doi:10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-48668867
  • Shi M, Zhan C, Shi J, et al. Prediction of overall survival of patients with completely resected non-small cell lung cancer: analyses of preoperative spirometry, preoperative blood tests, and other clinicopathological data. Cancer Manag Res. 2019;11:10487–10497. doi:10.2147/CMAR.S23221931853200
  • Hortobagyi GN, Edge SB, Giuliano A. New and important changes in the TNM staging system for breast cancer. Am Soc Clin Oncol Educ Book. 2018;38:457–467. doi:10.1200/EDBK_20131330231399
  • Kattan MW. Judging new markers by their ability to improve predictive accuracy. J Natl Cancer Inst. 2003;95(9):634–635. doi:10.1093/jnci/95.9.63412734304
  • 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. doi:10.1148/radiol.201615223427347764
  • Chen X, Fang M, Dong D, et al. A radiomics signature in preoperative predicting degree of tumor differentiation in patients with non-small cell lung cancer. Acad Radiol. 2018;25(12):1548–1555. doi:10.1016/j.acra.2018.02.01929572049
  • Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5(1):4006. doi:10.1038/ncomms500624892406
  • de Jong EEC, van Elmpt W, Rizzo S, et al. Applicability of a prognostic CT-based radiomic signature model trained on stage I-III non-small cell lung cancer in stage IV non-small cell lung cancer. Lung Cancer. 2018;124:6–11. doi:10.1016/j.lungcan.2018.07.02330268481
  • Lubner MG, Stabo N, Lubner SJ, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging. 2015;40(7):2331–2337. doi:10.1007/s00261-015-0438-425968046
  • Shen C, Liu Z, Guan M, et al. 2D and 3D CT radiomics features prognostic performance comparison in non-small cell lung cancer. Transl Oncol. 2017;10(6):886–894. doi:10.1016/j.tranon.2017.08.00728930698