Hatt M, Tixier F, Pierce L, Kinahan P, Cheze Le Rest C, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging. 2017;44:151–65.

Article  Google Scholar 

Cook G, Goh V. A role for FDG PET radiomics in personalized medicine. Semin Nucl Med. 2020;50:532–40.

Article  Google Scholar 

Deleu A, Sathekge M Jr, Maes A, De Spiegeleer B, Sathekge M Sr, Van de Wiele C. Characterization of FDG PET images using texture analysis in tumors of the gastro-intestinal tract: a review. Biomedicines. 2020;8(9):304. https://doi.org/10.3390/biomedicines8090304.

Article  PubMed Central  Google Scholar 

Orlhac F, Soussan M, Maisonobe J, Garcia C, Vanderlinden B, Buvat I. Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med. 2014;55:414–22.

CAS  Article  Google Scholar 

Hatt M, Majdoub M, Vallières M, Tixier F, Cheze le Rest C, Groheux D, et al. 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J Nucl Med. 2015;56:38–44.

CAS  Article  Google Scholar 

Babyak M. What you see may not be what you get: a brief, non-technical introduction to overfitting in regression-type models. Psychosom Med. 2004;66:411–21.

PubMed  Google Scholar 

Peduzzi P, Concato J, Kemper E, Holford T, Feinstein A. A simulation study of number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373–9.

CAS  Article  Google Scholar 

Chalkidou A, O’Doherty M, Marsden P. False discovery rates in PET and CT studies with texture features: a systematic review. PLoS ONE. 2015. https://doi.org/10.1371/journal.pone0124165.

Article  PubMed  PubMed Central  Google Scholar 

Kiers H, Smilde A. A comparison of various methods for multivariate regression with collinear variables. Stat Methods Appl. 2007;16:193–228.

Article  Google Scholar 

Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78(16):4786–9.

CAS  Article  Google Scholar 

Zwanenburg A, Vallières M, Abdalah M, Aerts H, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high throughput images-based phenotyping. Radiology. 2020. https://doi.org/10.1148/radiol.2020191145.

Article  PubMed  Google Scholar 

Fornacon-Wood I, Mistry H, Ackermann C, Blackhall F, McPartlin A, Faivre-Finn C, et al. Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform. Eur Radiol. 2020;30:6241–50.

Article  Google Scholar 

Van de Wiele C, Kruse V, Smeets P, Sathekge M, Maes A. Predictive and prognostic value of metabolic tumour volume and total lesion glycolysis in solid tumours. Eur J Nucl Med Mol Imaging. 2013;40:290–301.

CAS  Article  Google Scholar 

Zasadby K, Kison P, Francis R, Wahl R. FDG-PET determination of metabolically active tumor volume and comparison with CT. Clin Positron Imaging. 1998;1:123–9.

Article  Google Scholar 

Liao S, Penney B, Zhang H, Suzuki K, Pu Y. Prognostic value of the quantitative metabolic volumetric tumor burden on 18F-FDG PET/CT in stage IV nonsurgical small-cell lung cancer. Eur J Nucl Med Mol Imaging. 2012;39:27–38.

CAS  Article  Google Scholar 

Zhang H, Woblewski K, Appelbaum D, Pu Y. Independent prognostic value of whole-body metabolic tumor burden from FDG-PET in non-small cell lung cancer. Int J Comput Assist Radiol Surg. 2012. https://doi.org/10.1007/s11548-012-0749-7.

Article  PubMed  Google Scholar 

Lasnon C, Enilorac B, Popotte H, Aide N. Impact of the EARL harmonization program on automatic delineation of metabolic active tumour volumes (MATVs). EJNNMI Research. 2017;7:30.

Article  Google Scholar 

Devriese J, Beels L, Maes A, Van de Wiele C, Pottel H. Impact of PET reconstruction protocols on quantification of lesions that fulfill the PERCIST inclusion criteria. EJNMM Phys. 2018;5(1):35. https://doi.org/10.1186/s40658-018-0235-6.

Article  Google Scholar 

Tixier F, Le Reste C, Hatt M, Albarghach N, Pradier O, Metges J, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med. 2011;52:369–78.

Article  Google Scholar 

Orlhac F, Soussan M, Maisonobe J, Garcia C, Vanderlinden B, Buvat I. Tumor texture analysis in 18F-FDG PET: relationships between texture paramaters, histogram indices, standardized uptake values, metabolic volumes and total lesion glycolysis. J Nucl Med. 2014;55:414–22.

CAS  Article  Google Scholar 

Orlhah F, Nioche C, Soussan M, Buvat I. Understanding changes in tumor texture indices in PET: a comparison between visual assessment and index values in simulated and patient data. J Nucl Med. 2017;58:387–92.

Article  Google Scholar 

Berghmans T, Dusart M, Paesmans M, Hossein-Foucher C, Buvat I, Castaigne C, et al. Primary tumor standardized uptake value (SUVmax) measured on fluorodeoxyglucose positron emission tomographye (FDG-PET) is of prognostic value for survival in non-small cell lung cancer (NSCLC): a systematic review and meta-analysis (MA) by the European lung cancer working party for the IASLC lung cancer staging project. J Thorac Oncol. 2008;3:6–12.

Article  Google Scholar 

Lee M, Jung Y, Kim D, Lee S, Jung C, Kang S, et al. Prognostic value of SUVmax in breast cancer and comparative analyses of molecular subtypes: a systematic review and meta-analysis. Medicine (Baltimore). 2021;100(31): e26745.

CAS  Article  Google Scholar 

Ghooskhanei H, Treglia G, Sabouri G, Davoodi R, Sadeghi R. Risk stratification and prognosis determination using (18)F-FDG PET imaging in endometrial cancer patients: a systematic review and meta-analysis. Gynecol Oncol. 2014;132(3):669–76.

Article  Google Scholar 

Hughes N, Mou T, O’Regan N, Murphy P, O’Sullivan J, Wolsztunski E, et al. Tumor heterogeneity measurements using (18F)FDG PET/CT shows prognostic value in patients with non-small cell lung cancer. Eur J Hybrid Imaging. 2018. https://doi.org/10.1186/s41824-018-0043-1.

Article  Google Scholar 

van Gomez LO, Vicente A, Martinez A, Castrejon A, Londono G, Udias J, et al. Heterogeneity in (18F)fluorodeoxyglucose positron emission tomography of non-small cell lung carcinoma and its relationship to metabolic parameters and pathologic staging. Mol Imaging. 2014. https://doi.org/10.2310/7290.2014.00032.

Article  Google Scholar 

Shafiq-ul-Hassan M, Latifi K, Zhang G, Ullah G, Gillies R, Moros E. Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci Rep. 2018;8:10545. https://doi.org/10.1038/s41598-018-28895-9.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Welch M, McIntosh C, Haibe-Kains B, Milosevic M, Wee L, Dekker A, et al. Vulnerabilities of radiomic signature development: the need for safeguards. Radiother Oncol. 2019;130:2–9.

Article  Google Scholar 

Fathinul F, Nordin A, Lau W. 18(F)FDG -PET/CT is a useful molecular marker in evaluating tumour agressiveness: a revised understanding of an in-vivo FDG-PET imaging that alludes the alteration of cancer biology. Cell Biochem Biophys. 2013;66:37–43.

CAS  Article  Google Scholar 

Pantel A, Ackerman D, Lee S, Mankoff D, Gade T. Imaging cancer metabolism: underlying biology and emerging strategies. J Nucl Med. 2018;59:1340–9.

CAS  Article  Google Scholar 

Riester M, Xu Q, Moreira A, Michor A, Zheng J, Michor F, Downey R. The Warburg effect: persistence of stem-cell metabolism in cancers as a failure of differentiation. Ann Oncol. 2018;29:264–70.

CAS  Article  Google Scholar 

Lucia F, Visvikis D, Desseroit M, Miranda O, Malhaire J, Robin P, et al. Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. EJNNMI. 2018;45:768–86.

Google Scholar 

Sun Y, Qiao X, Jiang C, Liu S, Zhou Z. Texture analysis improves the value of pretreatment 18F-FDG PET/CT in predicting interim response of primary gastrointestinal diffuse large B-cell lymphoma. Contrast Media Mol Imaging. 2020. https://doi.org/10.1155/2020/2981585.

Article  PubMed  PubMed Central  Google Scholar 

Hatt M, Rixier F, Cheze le Rest C, Pradier O, Visvikis D. Robustness of intratumour 18F-FDG PET uptake heterogenity quantification for therapy response prediction in oesophageal carcinoma. EJNNMI. 2013;40:1662–71.

Google Scholar