References
- Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019; 69: 7-34. doi: 10.3322/caac.21551
- Hoos A. Development of immuno-oncology drugs – from CTLA4 to PD1 to the next generations. Nat Rev Drug Discov 2016; 15: 235-47. doi: 10.1038/nrd.2015.35
- Reck M, Rodríguez-Abreu D, Robinson AG, Hui R, Csőszi T, Fülöp A; KEYNOTE-024 investigators, et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N Engl J Med 2016; 375: 1823-33. doi: 10.1056/NEJMoa1606774
- Vrankar M, Unk M. Immune RECIST criteria and symptomatic pseudoprogression in non-small cell lung cancer patients treated with immunotherapy. Radiol Oncol 2018; 52: 365-9. doi:10.2478/raon-2018-0037
- Seymour L, Bogaerts J, Perrone A, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol 2017; 18: e143-52. doi: 10.1016/S1470-2045(17)30074-8
- Tazdait M, Mezquita L, Lahmar J, Ferrara R, Bidault F, Ammari S, et al. Patterns of responses in metastatic NSCLC during PD-1 or PDL-1 inhibitor therapy: comparison of RECIST 1.1, irRECIST and iRECIST criteria. Eur J Cancer 2018; 88: 38-47. doi: 10.1016/j.ejca.2017.10.017
- Mushti SL, Mulkey F, Sridhara R. Evaluation of overall response rate and progression-free survival as potential surrogate endpoints for overall survival in immunotherapy trials. Clin Cancer Res 2018; 24: 2268-2275. doi: 10.1158/1078-0432.CCR-17-1902
- Nie RC, Chen FP, Yuan SQ, Luo YS, Chen S, Chen YM, et al. Evaluation of objective response, disease control and progression-free survival as surrogate end-points for overall survival in anti-programmed death-1 and anti-programmed death ligand 1 trials. Eur J Cancer 2019; 106: 1-11. doi: 10.1016/j.ejca.2018.10.011
- Cho SY, Lipson EJ, Im HJ, Rowe SP, Gonzalez EM, Blackford A, et al. Prediction of response to immune checkpoint inhibitor therapy using early-time-point 18F-FDG PET/CT imaging in patients with advanced melanoma. J Nucl Med 2017; 58: 1421-8. doi: 10.2967/jnumed.116.188839
- Anwar H, Sachpekidis C, Winkler J, Kopp-Schneider A, Haberkorn U, Hassel JC, et al. Absolute number of new lesions on 18F-FDG PET/CT is more predictive of clinical response than SUV changes in metastatic melanoma patients receiving ipilimumab. Eur J Nucl Med Mol Imaging 2018; 45: 376-83. doi: 10.1007/s00259-017-3870-6
- Goldfarb L, Duchemann B, Chouahnia K, Zelek L, Soussan M. Monitoring anti-PD-1-based immunotherapy in non-small cell lung cancer with FDG PET: introduction of iPERCIST. EJNMMI Res 2019; 9: 8. doi: 10.1186/s13550-019-0473-1
- Ito K, Teng R, Schöder H, Humm JL, Ni A, Michaud L, et al. 18 F-FDG PET/ CT for monitoring of ipilimumab therapy in patients with metastatic melanoma. J Nucl Med 2019; 60: 335-41. doi: 10.2967/jnumed.118.213652
- Kaira K, Higuchi T, Naruse I, Arisaka Y, Tokue A, Altan B, et al. Metabolic activity by 18F–FDG-PET/CT is predictive of early response after nivolumab in previously treated NSCLC. Eur J Nucl Med Mol Imaging 2018; 45: 56-66. doi: 10.1007/s00259-017-3806-1
- Aide N, Hicks RJ, Le Tourneau C, Lheureux S, Fanti S, Lopci E. FDG PET/CT for assessing tumour response to immunotherapy. Eur J Nucl Med Mol Imaging 2019; 46: 238-50. doi: 10.1007/s00259-018-4171-4
- Rossi G, Bauckneht M, Genova C, Rijavec E, Biello F, Mennella S, et al. Comparison between 18F-FDG-PET- and CT-based criteria in non-small cell lung cancer (NSCLC) patients treated with Nivolumab. J Nucl Med 2019; [Ahead of print]. doi: 10.2967/jnumed.119.233056
- Yi M, Jiao D, Xu H, Liu Q, Zhao W, Xinwei Han H, et al. Biomarkers for predicting efficacy of PD-1/PD-L1 inhibitors. Mol Cancer 2018; 17: 129. doi: 10.1186/s12943-018-0864-3
- Zou W, Wolchok JD, Chen L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: Mechanisms, response biomarkers, and combinations. Sci Transl Med 2016; 8: 328rv4. doi: 10.1126/scitranslmed.aad7118
- Shukuya T, Carbone DP. Predictive markers for the efficacy of anti–PD-1/PD-L1 antibodies in lung cancer. J Thorac Oncol 2016; 11: 976-88. doi: 10.1016/j.jtho.2016.02.015
- Evangelista L, Cuppari L, Menis J, Bonanno L, Reccia P, Frega S, et al. 18F-FDG PET/CT in non-small-cell lung cancer patients: a potential predictive bio-marker of response to immunotherapy. Nucl Med Commun 2019; 40: 802-7. doi: 10.1097/MNM.0000000000001025
- Takada K, Toyokawa G, Yoneshima Y, Tanaka K, Okamoto I, Shimokawa M, et al. 18F-FDG uptake in PET/CT is a potential predictive biomarker of response to anti-PD-1 antibody therapy in non-small cell lung cancer. Sci Rep 2019; 9: 1-7. doi: 10.1038/s41598-019-50079-2
- Polverari, G. Ceci F, Bertaglia V, Reale MC, Rampado O, Gallio E, et al. 18F-FDG PET parameters and radiomics features analysis in advanced NSCLC treated with immunotherapy as predictors of therapy response and survival. Cancers 2020;. 12: 1163. doi: 10.3390/cancers12051163
- Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48: 441-6. doi: 10.1016/j.ejca.2011.11.036
- Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, et al. Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung Cancer 2018; 115: 34-41. doi: 10.1016/j.lungcan.2017.10.015
- Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, 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: 1180-91. doi: 10.1016/S1470-2045(18)30413-3
- Tunali I, Gray JE, Qi J, Abdalah M, Jeong DK, Guvenis A, et al. Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: an early report. Lung Cancer 2019; 129: 75-9. doi: 10.1016/j.lungcan.2019.01.010
- Dercle L, Fronheiser M, Lu L, Du S, Hayes W, Leung DK, et al. Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics. Clin Cancer Res 2020. [Aheqad of print]. doi: 10.1158/1078-0432.CCR-19-2942
- Mu W, Tunali I, Gray JE, Qi J, Schabath MB, Gillies RJ. Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy. Eur J Nucl Med Mol Imaging 2020; 47: 1168-82. doi: 10.1007/s00259-019-04625-9
- Desseroit MC, Tixier F, Weber WA, Siegel BA, Le Rest CC, Visvikis D, et al. Reliability of PET/CT shape and heterogeneity features in functional and morphologic components of non-small cell lung cancer tumors: a repeatability analysis in a prospective multicenter cohort. J Nucl Med 2017; 58: 406-11. doi: 10.2967/jnumed.116.180919
- Tang X. Texture information in run-length matrices. IEEE Trans image Process 1998; 7: 1602-9. doi: 10.1109/83.725367
- Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973; 3: 610-21. doi: 10.1109/TSMC.1973.4309314
- Lin C, Harmon S, Bradshaw T, Eickhoff J, Perlman S, Liu G, et al. Response-to-repeatability of quantitative imaging features for longitudinal response assessment. Phys Med Biol 2019; 64: 025019. doi: 10.1088/1361-6560/aafa0a
- Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol 2010; 49: 1012-6. doi: 10.3109/0284186X.2010.498437
- Chen S, Harmon S, Perk T, et al. Diagnostic classification of solitary pulmonary nodules using dual time 18F-FDG PET/CT image texture features in granuloma-endemic regions. Sci Rep 2017; 7: 9370. doi: 10.1038/s41598-017-08764-7
- Herbst RS, Baas P, Kim D-W, Felip E, Pérez-Gracia JL, Han JY, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet 2016; 387: 1540-50. doi: 10.1016/S0140-6736(15)01281-7
- Kickingereder P, Burth S, Wick A, Götz M, Eidel O, Schlemmer HP, et al. Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016; 280: 880-9. doi: 10.1148/radiol.2016160845
- Gubens MA, Davies M. NCCN guidelines updates: new immunotherapy strategies for improving outcomes in non-small cell lung cancer. J Natl Compr Canc Netw 2019; 17: 574-8. doi: 10.6004/jnccn.2019.5005
- McLaughlin J, Han G, Schalper KA, Carvajal-Hausdorf D, Pelekanou V, Rehman J, et al. Quantitative assessment of the heterogeneity of PD-L1 expression in non-small-cell lung cancer. JAMA Oncol 2016; 2: 46. doi: 10.1001/jamaoncol.2015.3638
- Galon J, Mlecnik B, Bindea G, Angell HK, Berger A, Lagorce C, et al. Towards the introduction of the ‘immunoscore’ in the classification of malignant tumours. J Pathol 2014; 232: 199-209. doi: 10.1002/path.4287
- Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5: 4006. doi: 10.1038/ncomms5006
- Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Phys Med Biol 2016; 61: R150-66. doi: 10.1088/0031-9155/61/13/R150
- Chen DS, Mellman I. Oncology meets immunology: the cancer-immunity cycle. Immunity 2013; 39: 1-10. doi: 10.1016/j.immuni.2013.07.012
- Santos TA, Maistro CEB, Silva CB, Oliveira MS, Franca MC, Castellano G. MRI texture analysis reveals bulbar abnormalities in Friedreich ataxia. Am J Neuroradiol 2015; 36: 2214-8. doi: 10.3174/ajnr.A4455
- Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process 1975; 4: 172-9. doi: 10.1016/s0146-664x(75)80008-6