Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer

References

• Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359–E386. doi:10.1002/ijc.2921025220842
   PubMed Web of Science ®Google Scholar
• Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7–34. doi:10.3322/caac.2155130620402
   PubMed Web of Science ®Google Scholar
• Bashir U, Kawa B, Siddique M, et al. Non-invasive classification of non-small cell lung cancer: a comparison between random forest models utilising radiomic and semantic features. Br J Radiol. 2019;92(1099):20190159. doi:10.1259/bjr.2019015931166787
   PubMed Web of Science ®Google Scholar
• Castello A, Grizzi F, Toschi L, et al. Tumor heterogeneity, hypoxia, and immune markers in surgically resected non-small-cell lung cancer. Nucl Med Commun. 2018;39(7):636–644. doi:10.1097/MNM.000000000000083229608508
   PubMed Web of Science ®Google Scholar
• Castello A, Russo C, Grizzi F, Qehajaj D, Lopci E. Prognostic impact of intratumoral heterogeneity based on fractal geometry analysis in operated NSCLC patients. Mol Imaging Biol. 2019;21(5):965–972. doi:10.1007/s11307-018-1299-330478506
   PubMed Web of Science ®Google Scholar
• Dong X, Sun X, Sun L, et al. Early change in metabolic tumor heterogeneity during chemoradiotherapy and its prognostic value for patients with locally advanced non-small cell lung cancer. PLoS One. 2016;11(6):e0157836. doi:10.1371/journal.pone.015783627322376
   PubMed Web of Science ®Google Scholar
• Herbst RS, Heymach JV, Lippman SM. Lung cancer. N Engl J Med. 2008;359(13):1367–1380. doi:10.1056/NEJMra080271418815398
   PubMed Web of Science ®Google Scholar
• Zhu X, Dong D, Chen Z, et al. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol. 2018;28(7):2772–2778. doi:10.1007/s00330-017-5221-129450713
   PubMed Web of Science ®Google Scholar
• Travis WD, Brambilla E, Noguchi M, et al. Diagnosis of lung adenocarcinoma in resected specimens: implications of the 2011 international association for the study of lung cancer/American thoracic society/European respiratory society classification. Arch Pathol Lab Med. 2013;137(5):685–705. doi:10.5858/arpa.2012-0264-RA22913371
   PubMed Web of Science ®Google Scholar
• Bashir U, Siddique MM, Mclean E, Goh V, Cook GJ. Imaging heterogeneity in lung cancer: techniques, applications, and challenges. AJR Am J Roentgenol. 2016;207(3):534–543. doi:10.2214/AJR.15.1586427305342
   PubMed Web of Science ®Google Scholar
• Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol. 2012;67(2):157–164. doi:10.1016/j.crad.2011.08.01221943720
   PubMed Web of Science ®Google Scholar
• Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. 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. doi:10.1102/1470-7330.2010.002120605762
   PubMed Web of Science ®Google Scholar
• Gevaert O, Xu J, Hoang CD, et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data–methods and preliminary results. Radiology. 2012;264(2):387–396. doi:10.1148/radiol.1211160722723499
   PubMed Web of Science ®Google Scholar
• Fave X, Zhang L, Yang J, et al. Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep. 2017;7(1):588. doi:10.1038/s41598-017-00665-z28373718
   PubMed Web of Science ®Google Scholar
• 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. doi:10.1016/j.ijrobp.2014.07.02025220716
   PubMed Web of Science ®Google Scholar
• Ahn SY, Park CM, Park SJ, et al. Prognostic value of computed tomography texture features in non-small cell lung cancers treated with definitive concomitant chemoradiotherapy. Invest Radiol. 2015;50(10):719–725. doi:10.1097/RLI.000000000000017426020832
   PubMed Web of Science ®Google Scholar
• Kirienko M, Cozzi L, Antunovic L, et al. Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging. 2018;45(2):207–217. doi:10.1007/s00259-017-3837-728944403
   PubMed Web of Science ®Google Scholar
• Ahn HK, Lee H, Kim SG, Hyun SH. Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer. Clin Radiol. 2019;74(6):467–473. doi:10.1016/j.crad.2019.02.00830898382
   PubMed Web of Science ®Google Scholar
• Harmon S, Seder CW, Chen S, Traynor A, Jeraj R, Blasberg JD. Quantitative FDG PET/CT may help risk-stratify early-stage non-small cell lung cancer patients at risk for recurrence following anatomic resection. J Thorac Dis. 2019;11(4):1106–1116. doi:10.21037/jtd.2019.04.4631179052
   PubMed Web of Science ®Google Scholar
• Nakajo M, Jinguji M, Shinaji T, et al. A pilot study of texture analysis of primary tumor [18F]FDG uptake to predict recurrence in surgically treated patients with non-small cell lung cancer. Mol Imaging Biol. 2019;21(4):771–780. doi:10.1007/s11307-018-1290-z30397859
   PubMed Web of Science ®Google Scholar
• Cook GJ, O’Brien ME, Siddique M, et al. Non-small cell lung cancer treated with erlotinib: heterogeneity of (18)F-FDG uptake at PET-association with treatment response and prognosis. Radiology. 2015;276(3):883–893. doi:10.1148/radiol.201514130925897473
   PubMed Web of Science ®Google Scholar
• Fried DV, Mawlawi O, Zhang L, et al. Stage III non-small cell lung cancer: prognostic value of FDG PET quantitative imaging features combined with clinical prognostic factors. Radiology. 2016;278(1):214–222. doi:10.1148/radiol.201514292026176655
   PubMed Web of Science ®Google Scholar
• Pyka T, Bundschuh RA, Andratschke N, et al. Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy. Radiat Oncol. 2015;10:100. doi:10.1186/s13014-015-0407-725900186
   PubMed Web of Science ®Google Scholar
• Liu W, Sun X, Qi Y, et al. Integrated texture parameter of 18F-FDG PET may be a stratification factor for the survival of nonoperative patients with locally advanced non-small-cell lung cancer. Nucl Med Commun. 2018;39(8):732–740. doi:10.1097/MNM.000000000000086030001264
   PubMed Web of Science ®Google Scholar
• Ha S, Choi H, Cheon GJ, et al. Autoclustering of non-small cell lung carcinoma subtypes on (18)F-FDG PET using texture analysis: a Preliminary result. Nucl Med Mol Imaging. 2014;48(4):278–286. doi:10.1007/s13139-014-0283-326396632
   PubMedGoogle Scholar
• Fang YH, Lin CY, Shih MJ, et al. Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. Biomed Res Int. 2014;2014:248505. doi:10.1155/2014/24850524757667
   PubMed Web of Science ®Google Scholar
• Kohutek ZA, Wu AJ, Zhang Z, et al. FDG-PET maximum standardized uptake value is prognostic for recurrence and survival after stereotactic body radiotherapy for non-small cell lung cancer. Lung Cancer. 2015;89(2):115–120. doi:10.1016/j.lungcan.2015.05.01926078260
   PubMed Web of Science ®Google Scholar
• Domachevsky L, Groshar D, Galili R, Saute M, Bernstine H. Survival prognostic value of morphological and metabolic variables in patients with stage I and II non-small cell lung cancer. Eur Radiol. 2015;25(11):3361–3367. doi:10.1007/s00330-015-3754-825929940
   PubMed Web of Science ®Google Scholar
• Hattori A, Matsunaga T, Takamochi K, Oh S, Suzuki K. Clinical significance of positron emission tomography in subcentimeter non-small cell lung cancer. Ann Thorac Surg. 2017;103(5):1614–1620. doi:10.1016/j.athoracsur.2016.09.05927964917
   PubMed Web of Science ®Google Scholar
• Lopci E, Toschi L, Grizzi F, et al. Correlation of metabolic information on FDG-PET with tissue expression of immune markers in patients with non-small cell lung cancer (NSCLC) who are candidates for upfront surgery. Eur J Nucl Med Mol Imaging. 2016;43(11):1954–1961. doi:10.1007/s00259-016-3425-227251642
   PubMed Web of Science ®Google Scholar
• Kim E, Wu HG, Keam B, et al. Significance of 18F-FDG PET parameters according to histologic subtype in the treatment outcome of stage III Non-small-cell lung cancer undergoing definitive concurrent chemoradiotherapy. Clin Lung Cancer. 2019;20(1):e9–e23. doi:10.1016/j.cllc.2018.08.01830266585
   PubMed Web of Science ®Google Scholar
• Koh YW, Lee SJ, Park SY. Differential expression and prognostic significance of GLUT1 according to histologic type of non-small-cell lung cancer and its association with volume-dependent parameters. Lung Cancer. 2017;104:31–37. doi:10.1016/j.lungcan.2016.12.00328212997
   PubMed Web of Science ®Google Scholar
• Desseroit MC, Visvikis D, Tixier F, et al. Development of a nomogram combining clinical staging with (18)F-FDG PET/CT image features in non-small-cell lung cancer stage I-III [published correction appears in Eur J Nucl Med Mol Imaging. 2016 Sep;43(10):1933]. Eur J Nucl Med Mol Imaging. 2016;43(8):1477–1485. doi:10.1007/s00259-016-3325-526896298
   PubMed Web of Science ®Google Scholar
• Takeda K, Takanami K, Shirata Y, et al. Clinical utility of texture analysis of 18F-FDG PET/CT in patients with stage I lung cancer treated with stereotactic body radiotherapy. J Radiat Res. 2017;58(6):862–869. doi:10.1093/jrr/rrx05029036692
   PubMed Web of Science ®Google Scholar
• Lovinfosse P, Janvary ZL, Coucke P, et al. FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging. 2016;43(8):1453–1460. doi:10.1007/s00259-016-3314-826830299
   PubMed Web of Science ®Google Scholar
• Cook GJ, Yip C, Siddique M, et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54(1):19–26. doi:10.2967/jnumed.112.10737523204495
   PubMed Web of Science ®Google Scholar
• Ba-Ssalamah A, Muin D, Schernthaner R, et al. Texture-based classification of different gastric tumors at contrast-enhanced CT. Eur J Radiol. 2013;82(10):e537–e543. doi:10.1016/j.ejrad.2013.06.02423910996
   PubMed Web of Science ®Google Scholar
• Tang X, Xu X, Han Z, et al. Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer. Biomed Eng Online. 2020;19(1):5. doi:10.1186/s12938-019-0744-031964407
   PubMed Web of Science ®Google Scholar
• Ferreira-Junior JR, Koenigkam-Santos M, Magalhães Tenório AP, et al. CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. Int J Comput Assist Radiol Surg. 2020;15(1):163–172. doi:10.1007/s11548-019-02093-y31722085
   PubMed Web of Science ®Google Scholar
• Hyun SH, Ahn MS, Koh YW, Lee SJ. A machine-learning approach using PET-based radiomics to predict the histological subtypes of lung cancer. Clin Nucl Med. 2019;44(12):956–960. doi:10.1097/RLU.000000000000281031689276
   PubMed Web of Science ®Google Scholar