Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2017;15:81–94.
Article
PubMed
CAS
Google Scholar
Jamal-Hanjani M, Quezada SA, Larkin J, Swanton C. Translational implications of tumor heterogeneity. Clin Cancer Res. 2015;21:1258–66.
Article
CAS
PubMed
PubMed Central
Google Scholar
Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev Cancer. 2012;12:323–34.
Article
CAS
PubMed
Google Scholar
Kleppe M, Levine RL. Tumor heterogeneity confounds and illuminates: assessing the implications. Nat Med. 2014;20:342–4.
Article
CAS
PubMed
Google Scholar
McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168:613–28.
Article
CAS
PubMed
Google Scholar
Bedard PL, Hansen AR, Ratain MJ, Siu LL. Tumour heterogeneity in the clinic. Nature. 2013;501:355–64.
Article
CAS
PubMed
PubMed Central
Google Scholar
Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472:90–4.
Article
CAS
PubMed
PubMed Central
Google Scholar
Jacoby MA, Duncavage EJ, Walter MJ. Implications of tumor clonal heterogeneity in the era of next-generation sequencing. Trends Cancer. 2015;1:231–41.
Article
PubMed
Google Scholar
Rajput A, Bocklage T, Greenbaum A, Lee J-H, Ness SA. Mutant-allele tumor heterogeneity scores correlate with risk of metastases in colon cancer. Clin Colorectal Cancer. 2017;16:e165–e70.
Article
PubMed
Google Scholar
Tixier F, Cheze-Le Rest C, Chezeaud S, Key S, Simon B, Potard G, et al. FDG PET derived quantitative heterogeneity features reflect gene expression profiles in head and neck cancer. J Nucl Med. 2014;55:450.
Article
CAS
Google Scholar
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–62.
Article
PubMed
Google Scholar
Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40:133–40.
Article
PubMed
Google Scholar
Cook GJ, O'Brien ME, Siddique M, Chicklore S, Loi HY, Sharma B, 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;276:883–93.
Article
PubMed
Google Scholar
Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, 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
PubMed
Google Scholar
Hyun SH, Kim HS, Choi SH, Choi DW, Lee JK, Lee KH, et al. Intratumoral heterogeneity of (18) F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging. 2016;43:1461–8.
Article
CAS
PubMed
Google Scholar
Folkert MR, Setton J, Apte AP, Grkovski M, Young RJ, Schoder H, et al. Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics. Phys Med Biol. 2017;62:5327–43.
Article
CAS
PubMed
PubMed Central
Google Scholar
Na K, Choi H. Tumor metabolic features identified by 18F-FDG PET correlate with gene networks of immune cell microenvironment in head and neck cancer. J Nucl Med. 2018;59:31–7.
Article
CAS
PubMed
Google Scholar
Choi H, Na K. Integrative analysis of imaging and transcriptomic data of the immune landscape associated with tumor metabolism in lung adenocarcinoma: clinical and prognostic implications. Theranostics. 2018;8:1956.
Article
CAS
PubMed
PubMed Central
Google Scholar
Choi H, Na K. Pan-cancer analysis of tumor metabolic landscape associated with genomic alterations. Mol Cancer. 2018;17:150.
Article
CAS
PubMed
PubMed Central
Google Scholar
Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, et al. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 2005;33:D428–D32.
Article
CAS
PubMed
Google Scholar
Nioche C, Orlhac F, Boughdad S, Reuze 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;78:4786–9.
Article
CAS
PubMed
Google Scholar
Han S, Kim YJ, Woo S, Suh CH, Lee JJ. Prognostic value of volumetric parameters of pretreatment 18F-FDG PET/CT in esophageal cancer: a systematic review and meta-analysis. Clin Nucl Med. 2018;43:887–94.
Torizuka T, Tanizaki Y, Kanno T, Futatsubashi M, Naitou K, Ueda Y, et al. Prognostic value of 18F-FDG PET in patients with head and neck squamous cell cancer. AJR Am J Roentgenol. 2009;192:W156–W60.
Article
PubMed
Google Scholar
Wang L, Bai J, Duan P. Prognostic value of 18F-FDG PET/CT functional parameters in patients with head and neck cancer: a meta-analysis. Nucl Med Commun. 2019;40:361–9.
Article
CAS
PubMed
Google Scholar
Bailly C, Bodet-Milin C, Couespel S, Necib H, Kraeber-Bodéré F, Ansquer C, et al. Revisiting the robustness of PET-based textural features in the context of multi-centric trials. PLoS One. 2016;11:e0159984.
Article
PubMed
PubMed Central
CAS
Google Scholar
Forgacs A, Jonsson HP, Dahlbom M, Daver F, DiFranco MD, Opposits G, et al. A study on the basic criteria for selecting heterogeneity parameters of F18-FDG PET images. PLoS One. 2016;11:e0164113.
Article
PubMed
PubMed Central
CAS
Google Scholar
Mayakonda A, Lin D-C, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28:1747–56.
Article
CAS
PubMed
PubMed Central
Google Scholar
Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, et al. An integrated TCGA Pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell. 2018;173:400–16.
Article
CAS
PubMed
PubMed Central
Google Scholar
Chen R-Y, Lin Y-C, Shen W-C, Hsieh T-C, Yen K-Y, Chen S-W, et al. Associations of tumor PD-1 ligands, Immunohistochemical studies, and textural features in 18F-FDG PET in squamous cell carcinoma of the head and neck. Sci Rep. 2018;8:105.
Article
PubMed
PubMed Central
CAS
Google Scholar
Wilson GD, Thibodeau BJ, Fortier LE, Pruetz BL, Galoforo S, Baschnagel AM, et al. Glucose metabolism gene expression patterns and tumor uptake of 18F-Fluorodeoxyglucose after radiation treatment. Int J Radiat Oncol Biol Phys. 2014;90:620–7.
Article
CAS
PubMed
Google Scholar
Nair VS, Gevaert O, Davidzon G, Plevritis SK, West R. NF-κB protein expression associates with 18F-FDG PET tumor uptake in non-small cell lung cancer: a radiogenomics validation study to understand tumor metabolism. Lung Cancer. 2014;83:189–96.
Article
PubMed
Google Scholar
Hong EK, Choi SH, Shin DJ, Jo SW, Yoo R-E, Kang KM, et al. Radiogenomics correlation between MR imaging features and major genetic profiles in glioblastoma. Eur Radiol. 2018;28:4350–61.
Article
PubMed
Google Scholar
Tohma T, Okazumi S, Makino H, Cho A, Mochiduki R, Shuto K, et al. Relationship between glucose transporter, hexokinase and FDG-PET in esophageal cancer. Hepatogastroenterology. 2005;52:486–90.
CAS
PubMed
Google Scholar
Hamada K, Tomita Y, Qiu Y, Zhang B, Ueda T, Myoui A, et al. 18 F-FDG-PET of musculoskeletal tumors: a correlation with the expression of glucose transporter 1 and hexokinase II. Ann Nucl Med. 2008;22:699–705.
Article
PubMed
Google Scholar
Higashi T, Saga T, Nakamoto Y, Ishimori T, Mamede MH, Wada M, et al. Relationship between retention index in dual-phase 18F-FDG PET, and hexokinase-II and glucose transporter-1 expression in pancreatic cancer. J Nucl Med. 2002;43:173–80.
CAS
PubMed
Google Scholar
Mroz EA, Tward AD, Hammon RJ, Ren Y, Rocco JW. Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the cancer genome atlas. PLoS Med. 2015;12:e1001786.
Article
PubMed
PubMed Central
CAS
Google Scholar
Mroz EA, Tward AD, Pickering CR, Myers JN, Ferris RL, Rocco JW. High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma. Cancer. 2013;119:3034–42.
Article
PubMed
Google Scholar
Moon SH, Kim J, Joung J-G, Cha H, Park W-Y, Ahn JS, et al. Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer. Eur J Nucl Med Mol Imaging. 2019;46:446–54.
Article
PubMed
CAS
Google Scholar
Na F, Wang J, Li C, Deng L, Xue J, Lu Y. Primary tumor standardized uptake value measured on F18-Fluorodeoxyglucose positron emission tomography is of prediction value for survival and local control in non–small-cell lung cancer receiving radiotherapy: meta-analysis. J Thorac Oncol. 2014;9:834–42.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sarker A, Im HJ, Cheon GJ, Chung HH, Kang KW, Chung JK, et al. Prognostic implications of the SUVmax of primary tumors and metastatic lymph node measured by 18F-FDG PET in patients with uterine cervical cancer: a meta-analysis. Clin Nucl Med. 2016;41:34–40.
Article
PubMed
Google Scholar
Im HJ, Kim TS, Park SY, Min HS, Kim JH, Kang HG, et al. Prediction of tumour necrosis fractions using metabolic and volumetric 18F-FDG PET/CT indices, after one course and at the completion of neoadjuvant chemotherapy, in children and young adults with osteosarcoma. Eur J Nucl Med Mol Imaging. 2012;39:39–49.
Article
CAS
PubMed
Google Scholar
Pak K, Cheon GJ, Nam HY, Kim SJ, Kang KW, Chung JK, et al. Prognostic value of metabolic tumor volume and Total lesion glycolysis in head and neck cancer: a systematic review and meta-analysis. J Nucl Med. 2014;55:884–90.
Article
CAS
PubMed
Google Scholar
Werner RA, Bundschuh RA, Higuchi T, Javadi MS, Rowe SP, Zsótér N, et al. Volumetric and texture analysis of pretherapeutic 18F-FDG PET can predict overall survival in medullary thyroid cancer patients treated with Vandetanib. Endocrine. 2019;63:293–300.
Article
PubMed
PubMed Central
CAS
Google Scholar
Bundschuh RA, Dinges J, Neumann L, Seyfried M, Zsoter N, Papp L, et al. Textural parameters of tumor heterogeneity in (1, 8) F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med. 2014;55:891–7.
Article
CAS
PubMed
Google Scholar
O'Connor JP, Rose CJ, Waterton JC, Carano RA, Parker GJ, Jackson A. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res. 2015;21:249–57.
Article
CAS
PubMed
Google Scholar
Im HJ, Kim YK, Kim YI, Lee JJ, Lee WW, Kim SE. Usefulness of combined metabolic-volumetric indices of (18) F-FDG PET/CT for the early prediction of Neoadjuvant chemotherapy outcomes in breast cancer. Nucl Med Mol Imaging. 2013;47:36–43.
Article
CAS
PubMed
Google Scholar
Lee JW, Kang CM, Choi HJ, Lee WJ, Song SY, Lee JH, et al. Prognostic value of metabolic tumor volume and total lesion glycolysis on preoperative 18F-FDG PET/CT in patients with pancreatic cancer. J Nucl Med. 2014;55:898–904.
Article
PubMed
CAS
Google Scholar
Hatt M, Majdoub M, Vallières M, Tixier F, Le Rest CC, 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.
Article
CAS
PubMed
Google Scholar
Orlhac F, Soussan M, Maisonobe J-A, Garcia CA, 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.
Article
CAS
PubMed
Google Scholar
Hatt M, Cheze-le Rest C, Van Baardwijk A, Lambin P, Pradier O, Visvikis D. Impact of tumor size and tracer uptake heterogeneity in (18) F-FDG PET and CT non-small cell lung cancer tumor delineation. J Nucl Med. 2011;52:1690–7.
Article
PubMed
Google Scholar
Cairns RA, Harris IS, Mak TW. Regulation of cancer cell metabolism. Nat Rev Cancer. 2011;11:85.
Article
CAS
PubMed
Google Scholar
Kumar D. Regulation of glycolysis in head and neck squamous cell carcinoma. Postdoc J. 2017;5:14–28.
Article
PubMed
PubMed Central
Google Scholar
Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2014;42:D472–7.
Article
CAS
PubMed
Google Scholar
Haberkorn U, Ziegler SI, Oberdorfer F, Trojan H, Haag D, Peschke P, et al. FDG uptake, tumor proliferation and expression of glycolysis associated genes in animal tumor models. Nucl Med Biol. 1994;21:827–34.
Article
CAS
PubMed
Google Scholar
Schoder H, Erdi YE, Chao K, Gonen M. Clinical implications of different image reconstruction parameters for interpretation of whole-body PET studies in cancer patients. J Nucl Med. 2004;45:559–66.
PubMed
Google Scholar