This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019; 69: 7-34. doi: 10.3322/caac.21551SiegelRLMillerKDJemalACancer statistics, 201920196973410.3322/caac.21551Open DOISearch in Google Scholar
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.2015.35HoosADevelopment of immuno-oncology drugs – from CTLA4 to PD1 to the next generations2016152354710.1038/nrd.2015.35Open DOISearch in Google Scholar
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/NEJMoa1606774ReckMRodríguez-AbreuDRobinsonAGHuiRCsősziTFülöpAKEYNOTE-024investigatorset alPembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer201637518233310.1056/NEJMoa1606774Open DOISearch in Google Scholar
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-2018-0037VrankarMUnkMImmune RECIST criteria and symptomatic pseudoprogression in non-small cell lung cancer patients treated with immunotherapy201852365910.2478/raon-2018-0037Open DOISearch in Google Scholar
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)30074-8SeymourLBogaertsJPerroneAet aliRECIST: guidelines for response criteria for use in trials testing immunotherapeutics201718e1435210.1016/S1470-2045(17)30074-8Open DOISearch in Google Scholar
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.2017.10.017TazdaitMMezquitaLLahmarJFerraraRBidaultFAmmariSet alPatterns of responses in metastatic NSCLC during PD-1 or PDL-1 inhibitor therapy: comparison of RECIST 1.1, irRECIST and iRECIST criteria201888384710.1016/j.ejca.2017.10.017Open DOISearch in Google Scholar
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-17-1902MushtiSLMulkeyFSridharaREvaluation of overall response rate and progression-free survival as potential surrogate endpoints for overall survival in immunotherapy trials2018242268227510.1158/1078-0432.CCR-17-1902Open DOISearch in Google Scholar
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.2018.10.011NieRCChenFPYuanSQLuoYSChenSChenYMet alEvaluation 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 trials201910611110.1016/j.ejca.2018.10.011Open DOISearch in Google Scholar
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.116.188839ChoSYLipsonEJImHJRoweSPGonzalezEMBlackfordAet alPrediction of response to immune checkpoint inhibitor therapy using early-time-point 18F-FDG PET/CT imaging in patients with advanced melanoma2017581421810.2967/jnumed.116.188839Open DOISearch in Google Scholar
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/s00259-017-3870-6AnwarHSachpekidisCWinklerJKopp-SchneiderAHaberkornUHasselJCet alAbsolute number of new lesions on 18F-FDG PET/CT is more predictive of clinical response than SUV changes in metastatic melanoma patients receiving ipilimumab2018453768310.1007/s00259-017-3870-6Open DOISearch in Google Scholar
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/s13550-019-0473-1GoldfarbLDuchemannBChouahniaKZelekLSoussanMMonitoring anti-PD-1-based immunotherapy in non-small cell lung cancer with FDG PET: introduction of iPERCIST20199810.1186/s13550-019-0473-1Open DOISearch in Google Scholar
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.118.213652ItoKTengRSchöderHHummJLNiAMichaudLet al18 F-FDG PET/ CT for monitoring of ipilimumab therapy in patients with metastatic melanoma2019603354110.2967/jnumed.118.213652Open DOISearch in Google Scholar
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/s00259-017-3806-1KairaKHiguchiTNaruseIArisakaYTokueAAltanBet alMetabolic activity by 18F–FDG-PET/CT is predictive of early response after nivolumab in previously treated NSCLC201845566610.1007/s00259-017-3806-1Open DOISearch in Google Scholar
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/s00259-018-4171-4AideNHicksRJLeTourneau CLheureuxSFantiSLopciEFDG PET/CT for assessing tumour response to immunotherapy2019462385010.1007/s00259-018-4171-4Open DOISearch in Google Scholar
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.119.233056RossiGBaucknehtMGenovaCRijavecEBielloFMennellaSet alComparison between 18F-FDG-PET- and CT-based criteria in non-small cell lung cancer (NSCLC) patients treated with Nivolumab2019[Ahead of print]10.2967/jnumed.119.233056Open DOISearch in Google Scholar
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/s12943-018-0864-3YiMJiaoDXuHLiuQZhaoWXinweiHan Het alBiomarkers for predicting efficacy of PD-1/PD-L1 inhibitors20181712910.1186/s12943-018-0864-3Open DOISearch in Google Scholar
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.aad7118ZouWWolchokJDChenLPD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: Mechanisms, response biomarkers, and combinations20168328rv410.1126/scitranslmed.aad7118Open DOISearch in Google Scholar
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.2016.02.015ShukuyaTCarboneDPPredictive markers for the efficacy of anti–PD-1/PD-L1 antibodies in lung cancer2016119768810.1016/j.jtho.2016.02.015Open DOISearch in Google Scholar
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.0000000000001025EvangelistaLCuppariLMenisJBonannoLRecciaPFregaSet al18F-FDG PET/CT in non-small-cell lung cancer patients: a potential predictive bio-marker of response to immunotherapy201940802710.1097/MNM.0000000000001025Open DOISearch in Google Scholar
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/s41598-019-50079-2TakadaKToyokawaGYoneshimaYTanakaKOkamotoIShimokawaMet al18F-FDG uptake in PET/CT is a potential predictive biomarker of response to anti-PD-1 antibody therapy in non-small cell lung cancer201991710.1038/s41598-019-50079-2Open DOISearch in Google Scholar
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/cancers12051163PolverariG.CeciFBertagliaVRealeMCRampadoOGallioEet al18F-FDG PET parameters and radiomics features analysis in advanced NSCLC treated with immunotherapy as predictors of therapy response and survival202012116310.3390/cancers12051163Open DOISearch in Google Scholar
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.2011.11.036LambinPRios-VelazquezELeijenaarRCarvalhoSvan StiphoutRGGrantonPet alRadiomics: extracting more information from medical images using advanced feature analysis201248441610.1016/j.ejca.2011.11.036Open DOISearch in Google Scholar
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.2017.10.015ThawaniRMcLaneMBeigNGhoseSPrasannaPVelchetiVet alRadiomics and radiogenomics in lung cancer: a review for the clinician2018115344110.1016/j.lungcan.2017.10.015Open DOISearch in Google Scholar
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)30413-3SunRLimkinEJVakalopoulouMDercleLChampiatSHanSRet alA radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study20181911809110.1016/S1470-2045(18)30413-3Open DOISearch in Google Scholar
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.2019.01.010TunaliIGrayJEQiJAbdalahMJeongDKGuvenisAet alNovel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: an early report201912975910.1016/j.lungcan.2019.01.010Open DOISearch in Google Scholar
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-19-2942DercleLFronheiserMLuLDuSHayesWLeungDKet alIdentification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics2020[Aheqad of print]10.1158/1078-0432.CCR-19-2942Open DOISearch in Google Scholar
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/s00259-019-04625-9MuWTunaliIGrayJEQiJSchabathMBGilliesRJRadiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy20204711688210.1007/s00259-019-04625-9Open DOISearch in Google Scholar
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.116.180919DesseroitMCTixierFWeberWASiegelBALe RestCCVisvikisDet alReliability 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 cohort2017584061110.2967/jnumed.116.180919Open DOISearch in Google Scholar
Tang X. Texture information in run-length matrices. IEEE Trans image Process 1998; 7: 1602-9. doi: 10.1109/83.725367TangXTexture information in run-length matrices199871602910.1109/83.725367Open DOISearch in Google Scholar
Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973; 3: 610-21. doi: 10.1109/TSMC.1973.4309314HaralickRMShanmugamKDinsteinITextural features for image classification197336102110.1109/TSMC.1973.4309314Open DOISearch in Google Scholar
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/aafa0aLinCHarmonSBradshawTEickhoffJPerlmanSLiuGet alResponse-to-repeatability of quantitative imaging features for longitudinal response assessment20196402501910.1088/1361-6560/aafa0aOpen DOISearch in Google Scholar
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.2010.498437GalavisPEHollensenCJallowNPaliwalBJerajRVariability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters2010491012610.3109/0284186X.2010.498437Open DOISearch in Google Scholar
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/s41598-017-08764-7ChenSHarmonSPerkTet alDiagnostic classification of solitary pulmonary nodules using dual time 18F-FDG PET/CT image texture features in granuloma-endemic regions20177937010.1038/s41598-017-08764-7Open DOISearch in Google Scholar
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)01281-7HerbstRSBaasPKimD-WFelipEPérez-GraciaJLHanJYet alPembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial201638715405010.1016/S0140-6736(15)01281-7Open DOISearch in Google Scholar
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.2016160845KickingerederPBurthSWickAGötzMEidelOSchlemmerHPet alRadiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models2016280880910.1148/radiol.2016160845Open DOISearch in Google Scholar
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.2019.5005GubensMADaviesMNCCN guidelines updates: new immunotherapy strategies for improving outcomes in non-small cell lung cancer201917574810.6004/jnccn.2019.5005Open DOISearch in Google Scholar
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.2015.3638McLaughlinJHanGSchalperKACarvajal-HausdorfDPelekanouVRehmanJet alQuantitative assessment of the heterogeneity of PD-L1 expression in non-small-cell lung cancer201624610.1001/jamaoncol.2015.3638Open DOISearch in Google Scholar
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.4287GalonJMlecnikBBindeaGAngellHKBergerALagorceCet alTowards the introduction of the ‘immunoscore’ in the classification of malignant tumours201423219920910.1002/path.4287Open DOISearch in Google Scholar
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/ncomms5006AertsHJWLVelazquezERLeijenaarRTHParmarCGrossmannPCarvalhoSet alDecoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach20145400610.1038/ncomms5006Open DOISearch in Google Scholar
Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Phys Med Biol 2016; 61: R150-66. doi: 10.1088/0031-9155/61/13/R150YipSSFAertsHJWLApplications and limitations of radiomics201661R1506610.1088/0031-9155/61/13/R150Open DOISearch in Google Scholar
Chen DS, Mellman I. Oncology meets immunology: the cancer-immunity cycle. Immunity 2013; 39: 1-10. doi: 10.1016/j.immuni.2013.07.012ChenDSMellmanIOncology meets immunology: the cancer-immunity cycle20133911010.1016/j.immuni.2013.07.012Open DOISearch in Google Scholar
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.A4455SantosTAMaistroCEBSilvaCBOliveiraMSFrancaMCCastellanoGMRI texture analysis reveals bulbar abnormalities in Friedreich ataxia2015362214810.3174/ajnr.A4455Open DOISearch in Google Scholar
Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process 1975; 4: 172-9. doi: 10.1016/s0146-664x(75)80008-6GallowayMMTexture analysis using gray level run lengths19754172910.1016/s0146-664x(75)80008-6Open DOISearch in Google Scholar