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Published in: European Journal of Nuclear Medicine and Molecular Imaging 1/2017

01-01-2017 | Review Article

Characterization of PET/CT images using texture analysis: the past, the present… any future?

Authors: Mathieu Hatt, Florent Tixier, Larry Pierce, Paul E. Kinahan, Catherine Cheze Le Rest, Dimitris Visvikis

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 1/2017

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Abstract

After seminal papers over the period 2009 – 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.
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Literature
1.
go back to reference Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883–92.PubMedPubMedCentralCrossRef Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883–92.PubMedPubMedCentralCrossRef
2.
go back to reference Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging. 2012;3:573–89.PubMedPubMedCentralCrossRef Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging. 2012;3:573–89.PubMedPubMedCentralCrossRef
3.
go back to reference 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.PubMedCrossRef 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.PubMedCrossRef
4.
go back to reference 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.PubMedCrossRef 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.PubMedCrossRef
5.
go back to reference Willaime JM, Turkheimer FE, Kenny LM, Aboagye EO. Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography. Phys Med Biol. 2013;58:187–203.PubMedCrossRef Willaime JM, Turkheimer FE, Kenny LM, Aboagye EO. Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography. Phys Med Biol. 2013;58:187–203.PubMedCrossRef
6.
go back to reference Weber WA, Schwaiger M, Avril N. Quantitative assessment of tumor metabolism using FDG-PET imaging. Nucl Med Biol. 2000;27:683–7.PubMedCrossRef Weber WA, Schwaiger M, Avril N. Quantitative assessment of tumor metabolism using FDG-PET imaging. Nucl Med Biol. 2000;27:683–7.PubMedCrossRef
7.
go back to reference Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Cavalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.PubMedPubMedCentral Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Cavalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.PubMedPubMedCentral
8.
go back to reference Win T, Miles KA, Janes SM, Ganeshan B, Shastry M, Endozo R, et al. Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer. Clin Cancer Res. 2013;19:3591–9.PubMedCrossRef Win T, Miles KA, Janes SM, Ganeshan B, Shastry M, Endozo R, et al. Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer. Clin Cancer Res. 2013;19:3591–9.PubMedCrossRef
9.
go back to reference Asselin MC, O’Connor JP, Boellaard R, Thacker NA, Jackson A. Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer. 2012;48:447–55.PubMedCrossRef Asselin MC, O’Connor JP, Boellaard R, Thacker NA, Jackson A. Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer. 2012;48:447–55.PubMedCrossRef
10.
go back to reference O’Connor JP, Rose CJ, Jackson A, Watson Y, Cheung S, Maders F, et al. DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6. Br J Cancer. 2011;105:139–45.PubMedPubMedCentralCrossRef O’Connor JP, Rose CJ, Jackson A, Watson Y, Cheung S, Maders F, et al. DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6. Br J Cancer. 2011;105:139–45.PubMedPubMedCentralCrossRef
11.
go back to reference Nicolasjilwan M, Hu Y, Yan C, Meerzaman D, Holder CA, Gutman D, et al. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. J Neuroradiol. 2015;42:212–21.PubMedCrossRef Nicolasjilwan M, Hu Y, Yan C, Meerzaman D, Holder CA, Gutman D, et al. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. J Neuroradiol. 2015;42:212–21.PubMedCrossRef
12.
go back to reference Yoon SH, Park CM, Park SJ, Yoon J-H, Hahn S, Goo JM. Tumor heterogeneity in lung cancer: assessment with dynamic contrast-enhanced MR imaging. Radiology. 2016. doi:10.1148/radiol.2016151367 Yoon SH, Park CM, Park SJ, Yoon J-H, Hahn S, Goo JM. Tumor heterogeneity in lung cancer: assessment with dynamic contrast-enhanced MR imaging. Radiology. 2016. doi:10.​1148/​radiol.​2016151367
13.
go back to reference Michallek F, Dewey M. Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review. Eur Radiol. 2014;24:60–9.PubMedCrossRef Michallek F, Dewey M. Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review. Eur Radiol. 2014;24:60–9.PubMedCrossRef
14.
go back to reference O’Sullivan F, Roy S, Eary J. A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data. Biostatistics. 2003;4:433–48.PubMedCrossRef O’Sullivan F, Roy S, Eary J. A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data. Biostatistics. 2003;4:433–48.PubMedCrossRef
15.
go back to reference El Naqa I, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009;42:1162–71.PubMedPubMedCentralCrossRef El Naqa I, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009;42:1162–71.PubMedPubMedCentralCrossRef
16.
go back to reference Gonzalez ME, Dinelle K, Vafai N, Heffernan N, McKenzie J, Appel-Cresswell S, et al. Novel spatial analysis method for PET images using 3D moment invariants: applications to Parkinson’s disease. Neuroimage. 2013;68:11–21.PubMedCrossRef Gonzalez ME, Dinelle K, Vafai N, Heffernan N, McKenzie J, Appel-Cresswell S, et al. Novel spatial analysis method for PET images using 3D moment invariants: applications to Parkinson’s disease. Neuroimage. 2013;68:11–21.PubMedCrossRef
17.
go back to reference van Velden FH, Cheebsumon P, Yaqub M, Smit EF, Hoekstra OS, Lammertsma AA, et al. Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur J Nucl Med Mol Imaging. 2011;38:1636–47.PubMedPubMedCentralCrossRef van Velden FH, Cheebsumon P, Yaqub M, Smit EF, Hoekstra OS, Lammertsma AA, et al. Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur J Nucl Med Mol Imaging. 2011;38:1636–47.PubMedPubMedCentralCrossRef
19.
go back to reference Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, Board on Health Care Services, Board on Health Sciences Policy, Institute of Medicine. Evolution of translational omics: lessons learned and the path forward. Micheel CM, Nass SJ, Omenn GS, editors. Washington (DC): National Academies Press (US); 2012. http://www.ncbi.nlm.nih.gov/books/NBK202168/ Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, Board on Health Care Services, Board on Health Sciences Policy, Institute of Medicine. Evolution of translational omics: lessons learned and the path forward. Micheel CM, Nass SJ, Omenn GS, editors. Washington (DC): National Academies Press (US); 2012. http://​www.​ncbi.​nlm.​nih.​gov/​books/​NBK202168/​
20.
go back to reference 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.PubMedPubMedCentralCrossRef 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.PubMedPubMedCentralCrossRef
21.
go back to reference Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563–77.PubMedCrossRef Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563–77.PubMedCrossRef
22.
go back to reference Mir AH, Hanmandlu M, Tandon SN. Texture analysis of CT-images for early detection of liver malignancy. Biomed Sci Instrum. 1995;31:213–7.PubMed Mir AH, Hanmandlu M, Tandon SN. Texture analysis of CT-images for early detection of liver malignancy. Biomed Sci Instrum. 1995;31:213–7.PubMed
23.
go back to reference Schad LR, Blüml S, Zuna I. MR tissue characterization of intracranial tumors by means of texture analysis. Magn Reson Imaging. 1993;11:889–96.PubMedCrossRef Schad LR, Blüml S, Zuna I. MR tissue characterization of intracranial tumors by means of texture analysis. Magn Reson Imaging. 1993;11:889–96.PubMedCrossRef
24.
go back to reference 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.PubMedPubMedCentralCrossRef 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.PubMedPubMedCentralCrossRef
25.
go back to reference Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP, 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.PubMedPubMedCentralCrossRef Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP, 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.PubMedPubMedCentralCrossRef
26.
go back to reference Hatt M, Hanzouli H, Rest CCL, Visvikis D. Comparison of edge-preserving filters for unbiased quantification in 18F-FDG PET imaging. J Nucl Med. 2015;56:1828.CrossRef Hatt M, Hanzouli H, Rest CCL, Visvikis D. Comparison of edge-preserving filters for unbiased quantification in 18F-FDG PET imaging. J Nucl Med. 2015;56:1828.CrossRef
27.
go back to reference Vaquero JJ, Kinahan P. Positron emission tomography: current challenges and opportunities for technological advances in clinical and preclinical imaging systems. Annu Rev Biomed Eng. 2015;17:385–414.PubMedCrossRef Vaquero JJ, Kinahan P. Positron emission tomography: current challenges and opportunities for technological advances in clinical and preclinical imaging systems. Annu Rev Biomed Eng. 2015;17:385–414.PubMedCrossRef
28.
go back to reference Chalkidou A, O’Doherty MJ, Marsden PK. False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One. 2015;10, e0124165.PubMedPubMedCentralCrossRef Chalkidou A, O’Doherty MJ, Marsden PK. False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One. 2015;10, e0124165.PubMedPubMedCentralCrossRef
29.
go back to reference Dwan K, Gamble C, Williamson PR, Kirkham JJ, Reporting Bias Group. Systematic review of the empirical evidence of study publication bias and outcome reporting bias – an updated review. PLoS One. 2013;8:e66844.PubMedPubMedCentralCrossRef Dwan K, Gamble C, Williamson PR, Kirkham JJ, Reporting Bias Group. Systematic review of the empirical evidence of study publication bias and outcome reporting bias – an updated review. PLoS One. 2013;8:e66844.PubMedPubMedCentralCrossRef
31.
go back to reference Basu S, Kwee TC, Gatenby R, Saboury B, Torigian DA, Alavi A. Evolving role of molecular imaging with PET in detecting and characterizing heterogeneity of cancer tissue at the primary and metastatic sites, a plausible explanation for failed attempts to cure malignant disorders. Eur J Nucl Med Mol Imaging. 2011;38:987–91.PubMedCrossRef Basu S, Kwee TC, Gatenby R, Saboury B, Torigian DA, Alavi A. Evolving role of molecular imaging with PET in detecting and characterizing heterogeneity of cancer tissue at the primary and metastatic sites, a plausible explanation for failed attempts to cure malignant disorders. Eur J Nucl Med Mol Imaging. 2011;38:987–91.PubMedCrossRef
33.
go back to reference Watabe T, Tatsumi M, Watabe H, Isohashi K, Kato H, Yanagawa M, et al. Intratumoral heterogeneity of F-18 FDG uptake differentiates between gastrointestinal stromal tumors and abdominal malignant lymphomas on PET/CT. Ann Nucl Med. 2012;26:222–7.PubMedCrossRef Watabe T, Tatsumi M, Watabe H, Isohashi K, Kato H, Yanagawa M, et al. Intratumoral heterogeneity of F-18 FDG uptake differentiates between gastrointestinal stromal tumors and abdominal malignant lymphomas on PET/CT. Ann Nucl Med. 2012;26:222–7.PubMedCrossRef
34.
go back to reference Kim DH, Jung JH, Son SH, Kim CY, Jeong SY, Lee SW, et al. Quantification of intratumoral metabolic macroheterogeneity on 18F-FDG PET/CT and its prognostic significance in pathologic N0 squamous cell lung carcinoma. Clin Nucl Med. 2016;41:e70–5.PubMedCrossRef Kim DH, Jung JH, Son SH, Kim CY, Jeong SY, Lee SW, et al. Quantification of intratumoral metabolic macroheterogeneity on 18F-FDG PET/CT and its prognostic significance in pathologic N0 squamous cell lung carcinoma. Clin Nucl Med. 2016;41:e70–5.PubMedCrossRef
35.
go back to reference Tixier F, Hatt M, Valla C, Fleury V, Lamour C, Ezzouhri S, et al. Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer. J Nucl Med. 2014;55:1235–41.PubMedCrossRef Tixier F, Hatt M, Valla C, Fleury V, Lamour C, Ezzouhri S, et al. Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer. J Nucl Med. 2014;55:1235–41.PubMedCrossRef
36.
go back to reference Pyka T, Gempt J, Hiob D, Ringel F, Schlegel J, Bette S, et al. Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas. Eur J Nucl Med Mol Imaging. 2016;43:133–41.PubMedCrossRef Pyka T, Gempt J, Hiob D, Ringel F, Schlegel J, Bette S, et al. Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas. Eur J Nucl Med Mol Imaging. 2016;43:133–41.PubMedCrossRef
37.
go back to reference Majdoub M, Visvikis D, Tixier F, Hoeben B, Visser E, Cheze Le Rest C, et al. Proliferative 18F-FLT PET tumor volumes characterization for prediction of locoregional recurrence and disease-free survival in head and neck cancer. Presented at the Society of Nuclear Medicine and Molecular Imaging Annual Meeting. 8–12 June 2013. Vancouver, Canada. Majdoub M, Visvikis D, Tixier F, Hoeben B, Visser E, Cheze Le Rest C, et al. Proliferative 18F-FLT PET tumor volumes characterization for prediction of locoregional recurrence and disease-free survival in head and neck cancer. Presented at the Society of Nuclear Medicine and Molecular Imaging Annual Meeting. 8–12 June 2013. Vancouver, Canada.
38.
go back to reference Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol. 2007;25:675–80.PubMedCrossRef Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol. 2007;25:675–80.PubMedCrossRef
39.
go back to reference Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, et al. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology. 2014;273:168–74.PubMedPubMedCentralCrossRef Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, et al. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology. 2014;273:168–74.PubMedPubMedCentralCrossRef
40.
go back to reference Wan T, Bloch BN, Plecha D, Thompson CL, Gilmore H, Jaffe C, et al. A radio-genomics approach for identifying high risk estrogen receptor-positive breast cancers on DCE-MRI: preliminary results in predicting OncotypeDX risk scores. Sci Rep. 2016;6:21394. doi:10.1038/srep21394.PubMedPubMedCentralCrossRef Wan T, Bloch BN, Plecha D, Thompson CL, Gilmore H, Jaffe C, et al. A radio-genomics approach for identifying high risk estrogen receptor-positive breast cancers on DCE-MRI: preliminary results in predicting OncotypeDX risk scores. Sci Rep. 2016;6:21394. doi:10.​1038/​srep21394.PubMedPubMedCentralCrossRef
41.
go back to reference Tixier F, Groves AM, Goh V, Hatt M, Ingrand P, Le Rest CC, et al. Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer. PLoS One. 2014;9, e99567.PubMedPubMedCentralCrossRef Tixier F, Groves AM, Goh V, Hatt M, Ingrand P, Le Rest CC, et al. Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer. PLoS One. 2014;9, e99567.PubMedPubMedCentralCrossRef
42.
go back to reference Tixier F, Hatt M, Rest CCL, Simon B, Key S, Corcos L, et al. Signaling pathways alteration involved in head and neck cancer can be identified through textural features analysis in 18F-FDG PET images: a prospective study. J Nucl Med. 2015;56:449. Tixier F, Hatt M, Rest CCL, Simon B, Key S, Corcos L, et al. Signaling pathways alteration involved in head and neck cancer can be identified through textural features analysis in 18F-FDG PET images: a prospective study. J Nucl Med. 2015;56:449.
43.
go back to reference Klyuzhin IS, Gonzalez M, Shahinfard E, Vafai N, Sossi V. Exploring the use of shape and texture descriptors of positron emission tomography tracer distribution in imaging studies of neurodegenerative disease. J Cereb Blood Flow Metab. 2015. doi:10.1177/0271678X15606718 PubMed Klyuzhin IS, Gonzalez M, Shahinfard E, Vafai N, Sossi V. Exploring the use of shape and texture descriptors of positron emission tomography tracer distribution in imaging studies of neurodegenerative disease. J Cereb Blood Flow Metab. 2015. doi:10.​1177/​0271678X15606718​ PubMed
44.
go back to reference Rahmim A, Salimpour Y, Jain S, Blinder SA, Klyuzhin IS, Smith GS, et al. Application of texture analysis to DAT SPECT imaging: relationship to clinical assessments. Neuroimage Clin. 2016. doi:10.1016/j.nicl.2016.02.012 Rahmim A, Salimpour Y, Jain S, Blinder SA, Klyuzhin IS, Smith GS, et al. Application of texture analysis to DAT SPECT imaging: relationship to clinical assessments. Neuroimage Clin. 2016. doi:10.​1016/​j.​nicl.​2016.​02.​012
45.
go back to reference Hatt M, Tixier F, Rest CLC, Visvikis D. Nouveaux indices en TEP/TDM: mythe et réalités. Med Nucl. 2015;39:331–8. Hatt M, Tixier F, Rest CLC, Visvikis D. Nouveaux indices en TEP/TDM: mythe et réalités. Med Nucl. 2015;39:331–8.
46.
go back to reference Carlier T, Bailly C. State-of-the-art and recent advances in quantification for therapeutic follow-up in oncology using PET. Front Med. 2015;2:18.CrossRef Carlier T, Bailly C. State-of-the-art and recent advances in quantification for therapeutic follow-up in oncology using PET. Front Med. 2015;2:18.CrossRef
47.
go back to reference Houshmand S, Salavati A, Hess S, Werner TJ, Alavi A, Zaidi H. An update on novel quantitative techniques in the context of evolving whole-body PET imaging. PET Clin. 2015;10:45–58.PubMedCrossRef Houshmand S, Salavati A, Hess S, Werner TJ, Alavi A, Zaidi H. An update on novel quantitative techniques in the context of evolving whole-body PET imaging. PET Clin. 2015;10:45–58.PubMedCrossRef
48.
go back to reference Rahim MK, Kim SE, So H, Kim HJ, Cheon GJ, Lee ES, et al. Recent trends in PET image interpretations using volumetric and texture-based quantification methods in nuclear oncology. Nucl Med Mol Imaging. 2014;48:1–15.PubMedPubMedCentralCrossRef Rahim MK, Kim SE, So H, Kim HJ, Cheon GJ, Lee ES, et al. Recent trends in PET image interpretations using volumetric and texture-based quantification methods in nuclear oncology. Nucl Med Mol Imaging. 2014;48:1–15.PubMedPubMedCentralCrossRef
49.
go back to reference Cheng NM, Fang YH, Yen TC. The promise and limits of PET texture analysis. Ann Nucl Med. 2013;27:867–9.PubMedCrossRef Cheng NM, Fang YH, Yen TC. The promise and limits of PET texture analysis. Ann Nucl Med. 2013;27:867–9.PubMedCrossRef
50.
go back to reference Bundschuh RA, Dinges J, Neumann L, Seyfried M, Zsótér N, Papp L, et al. Textural parameters of tumor heterogeneity in 18F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med. 2014;55:891–7.PubMedCrossRef Bundschuh RA, Dinges J, Neumann L, Seyfried M, Zsótér N, Papp L, et al. Textural parameters of tumor heterogeneity in 18F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med. 2014;55:891–7.PubMedCrossRef
51.
go back to reference Tixier F, Hatt M, Le Rest CC, Le Pogam A, Corcos L, Visvikis D. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med. 2012;53:693–700.PubMedPubMedCentralCrossRef Tixier F, Hatt M, Le Rest CC, Le Pogam A, Corcos L, Visvikis D. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med. 2012;53:693–700.PubMedPubMedCentralCrossRef
52.
go back to reference van Velden FHP, Kramer GM, Frings V, Nissen IA, Mulder ER, de Langen AJ, et al. Repeatability of radiomic features in non-small-cell lung cancer [(18)F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol. 2016. doi:10.1007/s11307-016-0940-2 van Velden FHP, Kramer GM, Frings V, Nissen IA, Mulder ER, de Langen AJ, et al. Repeatability of radiomic features in non-small-cell lung cancer [(18)F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol. 2016. doi:10.​1007/​s11307-016-0940-2
53.
go back to reference Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys. 2015;42:1341–53.PubMedCrossRef Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys. 2015;42:1341–53.PubMedCrossRef
54.
go back to reference Fang YH, Lin CY, Shih MJ, Wang HM, Ho TY, Liao CT, 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.PubMedPubMedCentral Fang YH, Lin CY, Shih MJ, Wang HM, Ho TY, Liao CT, 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.PubMedPubMedCentral
55.
go back to reference Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol. 2015;60:5471–96.PubMedCrossRef Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol. 2015;60:5471–96.PubMedCrossRef
56.
go back to reference Leijenaar RT, Nalbantov G, Carvalho S, van Elmpt WJ, Troost EG, Boellaard R, et al. The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep. 2015;5:11075.PubMedPubMedCentralCrossRef Leijenaar RT, Nalbantov G, Carvalho S, van Elmpt WJ, Troost EG, Boellaard R, et al. The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep. 2015;5:11075.PubMedPubMedCentralCrossRef
57.
go back to reference Leijenaar RT, Carvalho S, Velazquez ER, van Elmpt WJ, Parmar C, Hoekstra OS, et al. Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol. 2013;52:1391–7.PubMedCrossRef Leijenaar RT, Carvalho S, Velazquez ER, van Elmpt WJ, Parmar C, Hoekstra OS, et al. Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol. 2013;52:1391–7.PubMedCrossRef
58.
go back to reference Doumou G, Siddique M, Tsoumpas C, Goh V, Cook GJ. The precision of textural analysis in (18)F-FDG-PET scans of oesophageal cancer. Eur Radiol. 2015;25:2805–12.PubMedCrossRef Doumou G, Siddique M, Tsoumpas C, Goh V, Cook GJ. The precision of textural analysis in (18)F-FDG-PET scans of oesophageal cancer. Eur Radiol. 2015;25:2805–12.PubMedCrossRef
59.
go back to reference Orlhac F, Soussan M, Maisonobe JA, 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.PubMedCrossRef Orlhac F, Soussan M, Maisonobe JA, 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.PubMedCrossRef
60.
go back to reference 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.PubMedCrossRef 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.PubMedCrossRef
61.
go back to reference 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.PubMedPubMedCentralCrossRef 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.PubMedPubMedCentralCrossRef
62.
go back to reference Dong X, Wu P, Sun X, Li W, Wan H, Yu J, et al. Intra-tumour 18F-FDG uptake heterogeneity decreases the reliability on target volume definition with positron emission tomography/computed tomography imaging. J Med Imaging Radiat Oncol. 2015;59:338–45.PubMedCrossRef Dong X, Wu P, Sun X, Li W, Wan H, Yu J, et al. Intra-tumour 18F-FDG uptake heterogeneity decreases the reliability on target volume definition with positron emission tomography/computed tomography imaging. J Med Imaging Radiat Oncol. 2015;59:338–45.PubMedCrossRef
63.
go back to reference Geets X, Lee JA, Bol A, Lonneux M, Gregoire V. A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur J Nucl Med Mol Imaging. 2007;34:1427–38.PubMedCrossRef Geets X, Lee JA, Bol A, Lonneux M, Gregoire V. A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur J Nucl Med Mol Imaging. 2007;34:1427–38.PubMedCrossRef
64.
go back to reference Nelson A, Brockway K, Nelson A, Piper J. PET tumor segmentation: validation of a gradient-based method using a NSCLC PET phantom. J Nucl Med. 2009;50 Suppl 2:1659 Nelson A, Brockway K, Nelson A, Piper J. PET tumor segmentation: validation of a gradient-based method using a NSCLC PET phantom. J Nucl Med. 2009;50 Suppl 2:1659
65.
go back to reference Hofheinz F, Langner J, Petr J, Beuthien-Baumann B, Steinbach J, Kotzerke J, et al. An automatic method for accurate volume delineation of heterogeneous tumors in PET. Med Phys. 2013;40:082503.PubMedCrossRef Hofheinz F, Langner J, Petr J, Beuthien-Baumann B, Steinbach J, Kotzerke J, et al. An automatic method for accurate volume delineation of heterogeneous tumors in PET. Med Phys. 2013;40:082503.PubMedCrossRef
66.
go back to reference Hatt M, Rest C l C, Descourt P, Dekker A, De Ruysscher D, Oellers M, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys. 2010;77:301–8.PubMedCrossRef Hatt M, Rest C l C, Descourt P, Dekker A, De Ruysscher D, Oellers M, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys. 2010;77:301–8.PubMedCrossRef
67.
68.
go back to reference Groheux D, Majdoub M, Tixier F, Le Rest CC, Martineau A, Merlet P, et al. Do clinical, histological or immunohistochemical primary tumour characteristics translate into different (18)F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer? Eur J Nucl Med Mol Imaging. 2015;42:1682–91.PubMedCrossRef Groheux D, Majdoub M, Tixier F, Le Rest CC, Martineau A, Merlet P, et al. Do clinical, histological or immunohistochemical primary tumour characteristics translate into different (18)F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer? Eur J Nucl Med Mol Imaging. 2015;42:1682–91.PubMedCrossRef
69.
go back to reference Brooks FJ, Grigsby PW. FDG uptake heterogeneity in FIGO IIb cervical carcinoma does not predict pelvic lymph node involvement. Radiat Oncol. 2013;8:294.PubMedPubMedCentralCrossRef Brooks FJ, Grigsby PW. FDG uptake heterogeneity in FIGO IIb cervical carcinoma does not predict pelvic lymph node involvement. Radiat Oncol. 2013;8:294.PubMedPubMedCentralCrossRef
70.
go back to reference Kidd EA, Grigsby PW. Intratumoral metabolic heterogeneity of cervical cancer. Clin Cancer Res. 2008;14:5236–41.PubMedCrossRef Kidd EA, Grigsby PW. Intratumoral metabolic heterogeneity of cervical cancer. Clin Cancer Res. 2008;14:5236–41.PubMedCrossRef
71.
go back to reference Yang F, Thomas MA, Dehdashti F, Grigsby PW. Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer. Eur J Nucl Med Mol Imaging. 2013;40:716–27.PubMedPubMedCentralCrossRef Yang F, Thomas MA, Dehdashti F, Grigsby PW. Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer. Eur J Nucl Med Mol Imaging. 2013;40:716–27.PubMedPubMedCentralCrossRef
72.
go back to reference Soussan M, Orlhac F, Boubaya M, Zelek L, Ziol M, Eder V, et al. Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. PLoS One. 2014;9, e94017.PubMedPubMedCentralCrossRef Soussan M, Orlhac F, Boubaya M, Zelek L, Ziol M, Eder V, et al. Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. PLoS One. 2014;9, e94017.PubMedPubMedCentralCrossRef
73.
go back to reference Larson SM, Erdi Y, Akhurst T, Mazumdar M, Macapinlac HA, Finn RD, et al. Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging. The visual response score and the change in total lesion glycolysis. Clin Positron Imaging. 1999;2:159–71.PubMedCrossRef Larson SM, Erdi Y, Akhurst T, Mazumdar M, Macapinlac HA, Finn RD, et al. Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging. The visual response score and the change in total lesion glycolysis. Clin Positron Imaging. 1999;2:159–71.PubMedCrossRef
74.
go back to reference Yan J, Lim JC-S, Loi HY, Khor LK, Sinha AK, Quek ST, et al. Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med. 2015;56:1667–73.PubMedCrossRef Yan J, Lim JC-S, Loi HY, Khor LK, Sinha AK, Quek ST, et al. Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med. 2015;56:1667–73.PubMedCrossRef
75.
go back to reference Hatt M, Tixier F, Rest CLC, Pradier O, Visvikis D. Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging. 2013;40:1662–71.PubMedCrossRef Hatt M, Tixier F, Rest CLC, Pradier O, Visvikis D. Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging. 2013;40:1662–71.PubMedCrossRef
76.
go back to reference Brooks FJ, Grigsby PW. The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake. J Nucl Med. 2014;55:37–42.PubMedCrossRef Brooks FJ, Grigsby PW. The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake. J Nucl Med. 2014;55:37–42.PubMedCrossRef
77.
78.
go back to reference Mi H, Petitjean C, Dubray B, Vera P, Ruan S. Robust feature selection to predict tumor treatment outcome. Artif Intell Med. 2015;64:195–204.PubMedCrossRef Mi H, Petitjean C, Dubray B, Vera P, Ruan S. Robust feature selection to predict tumor treatment outcome. Artif Intell Med. 2015;64:195–204.PubMedCrossRef
79.
go back to reference Sullivan DC, Obuchowski NA, Kessler LG, Raunig DL, Gatsonis C, Huang EP, et al. Metrology standards for quantitative imaging biomarkers. Radiology. 2015;277:813–25.PubMedCrossRef Sullivan DC, Obuchowski NA, Kessler LG, Raunig DL, Gatsonis C, Huang EP, et al. Metrology standards for quantitative imaging biomarkers. Radiology. 2015;277:813–25.PubMedCrossRef
80.
go back to reference Quantitative Imaging Biomarkers Alliance, FDG-PET/CT Technical Committee. FDG-PET/CT as an imaging biomarker measuring response to cancer therapy, version 1.05. RSNA; 2013. Quantitative Imaging Biomarkers Alliance, FDG-PET/CT Technical Committee. FDG-PET/CT as an imaging biomarker measuring response to cancer therapy, version 1.05. RSNA; 2013.
81.
go back to reference Yip S, McCall K, Aristophanous M, Chen AB, Aerts HJ, Berbeco R. Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer. PLoS One. 2014;9, e115510.PubMedPubMedCentralCrossRef Yip S, McCall K, Aristophanous M, Chen AB, Aerts HJ, Berbeco R. Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer. PLoS One. 2014;9, e115510.PubMedPubMedCentralCrossRef
82.
go back to reference Oliver JA, Budzevich M, Zhang GG, Dilling TJ, Latifi K, Moros EG. Variability of image features computed from conventional and respiratory-gated PET/CT images of lung cancer. Transl Oncol. 2015;8:524–34.PubMedPubMedCentralCrossRef Oliver JA, Budzevich M, Zhang GG, Dilling TJ, Latifi K, Moros EG. Variability of image features computed from conventional and respiratory-gated PET/CT images of lung cancer. Transl Oncol. 2015;8:524–34.PubMedPubMedCentralCrossRef
83.
go back to reference Cheng NM, Fang YH, Tsan DL, Hsu CH, Yen TC. Respiration-averaged CT for attenuation correction of PET images – impact on PET texture features in non-small cell lung cancer patients. PLoS One. 2016;11, e0150509.PubMedPubMedCentralCrossRef Cheng NM, Fang YH, Tsan DL, Hsu CH, Yen TC. Respiration-averaged CT for attenuation correction of PET images – impact on PET texture features in non-small cell lung cancer patients. PLoS One. 2016;11, e0150509.PubMedPubMedCentralCrossRef
84.
go back to reference Tixier F, Vriens D, Cheze-Le Rest C, Hatt M, Disselhorst JA, Oyen WJ, et al. Comparison of tumor uptake heterogeneity characterization between static and parametric 18F-FDG PET images in non-small cell lung cancer. J Nucl Med. 2016. doi:10.2967/jnumed.115.166918. Tixier F, Vriens D, Cheze-Le Rest C, Hatt M, Disselhorst JA, Oyen WJ, et al. Comparison of tumor uptake heterogeneity characterization between static and parametric 18F-FDG PET images in non-small cell lung cancer. J Nucl Med. 2016. doi:10.​2967/​jnumed.​115.​166918.
85.
go back to reference Nyflot MJ, Yang F, Byrd D, Bowen SR, Sandison GA, Kinahan PE. Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards. J Med Imaging (Bellingham). 2015;2:041002.CrossRef Nyflot MJ, Yang F, Byrd D, Bowen SR, Sandison GA, Kinahan PE. Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards. J Med Imaging (Bellingham). 2015;2:041002.CrossRef
86.
go back to reference Pyka T, Bundschuh RA, Andratschke N, Mayer B, Specht HM, Papp L, 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.PubMedPubMedCentralCrossRef Pyka T, Bundschuh RA, Andratschke N, Mayer B, Specht HM, Papp L, 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.PubMedPubMedCentralCrossRef
87.
go back to reference 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.PubMedCrossRef 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.PubMedCrossRef
88.
go back to reference Mu W, Chen Z, Liang Y, Shen W, Yang F, Dai R, et al. Staging of cervical cancer based on tumor heterogeneity characterized by texture features on (18)F-FDG PET images. Phys Med Biol. 2015;60:5123–39.PubMedCrossRef Mu W, Chen Z, Liang Y, Shen W, Yang F, Dai R, et al. Staging of cervical cancer based on tumor heterogeneity characterized by texture features on (18)F-FDG PET images. Phys Med Biol. 2015;60:5123–39.PubMedCrossRef
89.
go back to reference Oh JS, Kang BC, Roh JL, Kim JS, Cho KJ, Lee SW, et al. Intratumor textural heterogeneity on pretreatment (18)F-FDG PET images predicts response and survival after chemoradiotherapy for hypopharyngeal cancer. Ann Surg Oncol. 2015;22:2746–54.PubMedCrossRef Oh JS, Kang BC, Roh JL, Kim JS, Cho KJ, Lee SW, et al. Intratumor textural heterogeneity on pretreatment (18)F-FDG PET images predicts response and survival after chemoradiotherapy for hypopharyngeal cancer. Ann Surg Oncol. 2015;22:2746–54.PubMedCrossRef
90.
go back to reference Cheng NM, Fang YH, Chang JT, Huang CG, Tsan DL, Ng SH, et al. Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma. J Nucl Med. 2013;54:1703–9.PubMedCrossRef Cheng NM, Fang YH, Chang JT, Huang CG, Tsan DL, Ng SH, et al. Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma. J Nucl Med. 2013;54:1703–9.PubMedCrossRef
91.
go back to reference Lovinfosse P, Janvary ZL, Coucke P, Jodogne S, Bernard C, Hatt M, 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. doi:10.1007/s00259-016-3314-8 PubMed Lovinfosse P, Janvary ZL, Coucke P, Jodogne S, Bernard C, Hatt M, 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. doi:10.​1007/​s00259-016-3314-8 PubMed
92.
go back to reference Yip SS, Coroller TP, Sanford NN, Mamon H, Aerts HJ, Berbeco RI. Relationship between the temporal changes in positron-emission-tomography-imaging-based textural features and pathologic response and survival in esophageal cancer patients. Front Oncol. 2016;6:72.PubMedPubMedCentralCrossRef Yip SS, Coroller TP, Sanford NN, Mamon H, Aerts HJ, Berbeco RI. Relationship between the temporal changes in positron-emission-tomography-imaging-based textural features and pathologic response and survival in esophageal cancer patients. Front Oncol. 2016;6:72.PubMedPubMedCentralCrossRef
93.
go back to reference Cheng N-M, Fang Y-HD, Lee L, Chang JT-C, Tsan D-L, Ng S-H, et al. Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging. 2015;42:419–28.PubMedCrossRef Cheng N-M, Fang Y-HD, Lee L, Chang JT-C, Tsan D-L, Ng S-H, et al. Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging. 2015;42:419–28.PubMedCrossRef
94.
go back to reference Xu R, Kido S, Suga K, Hirano Y, Tachibana R, Muramatsu K, et al. Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions. Ann Nucl Med. 2014;28:926–35.PubMedCrossRef Xu R, Kido S, Suga K, Hirano Y, Tachibana R, Muramatsu K, et al. Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions. Ann Nucl Med. 2014;28:926–35.PubMedCrossRef
95.
go back to reference Wu J, Aguilera T, Shultz D, Gudur M, Rubin DL, Loo BW, et al. Early-stage non-small cell lung cancer: quantitative imaging characteristics of (18)F Fluorodeoxyglucose PET/CT allow prediction of distant metastasis. Radiology. 2016. doi:10.1148/radiol.2016151829. Wu J, Aguilera T, Shultz D, Gudur M, Rubin DL, Loo BW, et al. Early-stage non-small cell lung cancer: quantitative imaging characteristics of (18)F Fluorodeoxyglucose PET/CT allow prediction of distant metastasis. Radiology. 2016. doi:10.​1148/​radiol.​2016151829.
96.
go back to reference Ypsilantis PP, Siddique M, Sohn HM, Davies A, Cook G, Goh V, et al. Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks. PLoS One. 2015;10, e0137036.PubMedPubMedCentralCrossRef Ypsilantis PP, Siddique M, Sohn HM, Davies A, Cook G, Goh V, et al. Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks. PLoS One. 2015;10, e0137036.PubMedPubMedCentralCrossRef
97.
go back to reference van Rossum PS, Fried DV, Zhang L, Hofstetter WL, van Vulpen M, Meijer GJ, et al. The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer. J Nucl Med. 2016;57:691–700.PubMedCrossRef van Rossum PS, Fried DV, Zhang L, Hofstetter WL, van Vulpen M, Meijer GJ, et al. The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer. J Nucl Med. 2016;57:691–700.PubMedCrossRef
98.
go back to reference Gao X, Chu C, Li Y, Lu P, Wang W, Liu W, et al. The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from (18)F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer. Eur J Radiol. 2015;84:312–7.PubMedCrossRef Gao X, Chu C, Li Y, Lu P, Wang W, Liu W, et al. The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from (18)F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer. Eur J Radiol. 2015;84:312–7.PubMedCrossRef
99.
go back to reference Desseroit MC, Visvikis D, Tixier F, Majdoub M, Guillevin R, Perdrisot R, et al. Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I-III. Eur J Nucl Med Mol Imaging. 2016. doi:10.1007/s00259-016-3325-5. Desseroit MC, Visvikis D, Tixier F, Majdoub M, Guillevin R, Perdrisot R, et al. Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I-III. Eur J Nucl Med Mol Imaging. 2016. doi:10.​1007/​s00259-016-3325-5.
100.
go back to reference Fried DV, Mawlawi O, Zhang L, Fave X, Zhou S, Ibbott G, et al. Stage III non-small cell lung cancer: prognostic value of FDG PET quantitative imaging features combined with clinical prognostic factors. Radiology. 2016;278:214–22.PubMedCrossRef Fried DV, Mawlawi O, Zhang L, Fave X, Zhou S, Ibbott G, et al. Stage III non-small cell lung cancer: prognostic value of FDG PET quantitative imaging features combined with clinical prognostic factors. Radiology. 2016;278:214–22.PubMedCrossRef
102.
go back to reference Hyun SH, Kim HS, Choi SH, Choi DW, Lee JK, Lee KH, et al. Intratumoral heterogeneity of 18F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging. 2016. doi:10.1007/s00259-016-3316-6 Hyun SH, Kim HS, Choi SH, Choi DW, Lee JK, Lee KH, et al. Intratumoral heterogeneity of 18F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging. 2016. doi:10.​1007/​s00259-016-3316-6
103.
go back to reference Lartizien C, Rogez M, Niaf E, Ricard F. Computer-aided staging of lymphoma patients with FDG PET/CT imaging based on textural information. IEEE J Biomed Health Inform. 2014;18:946–55.PubMedCrossRef Lartizien C, Rogez M, Niaf E, Ricard F. Computer-aided staging of lymphoma patients with FDG PET/CT imaging based on textural information. IEEE J Biomed Health Inform. 2014;18:946–55.PubMedCrossRef
104.
go back to reference Bang JI, Ha S, Kang SB, Lee KW, Lee HS, Kim JS, et al. Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [(18)F]FDG PET/CT scans in locally advanced rectal cancer. Eur J Nucl Med Mol Imaging. 2016:43:422–31. Bang JI, Ha S, Kang SB, Lee KW, Lee HS, Kim JS, et al. Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [(18)F]FDG PET/CT scans in locally advanced rectal cancer. Eur J Nucl Med Mol Imaging. 2016:43:422–31.
105.
go back to reference Vaidya M, Creach KM, Frye J, Dehdashti F, Bradley JD, El Naqa I. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol. 2012;102:239–45.PubMedCrossRef Vaidya M, Creach KM, Frye J, Dehdashti F, Bradley JD, El Naqa I. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol. 2012;102:239–45.PubMedCrossRef
106.
go back to reference Antunes J, Viswanath S, Rusu M, Valls L, Hoimes C, Avril N, et al. Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study. Transl Oncol. 2016;9:155–62.PubMedPubMedCentralCrossRef Antunes J, Viswanath S, Rusu M, Valls L, Hoimes C, Avril N, et al. Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study. Transl Oncol. 2016;9:155–62.PubMedPubMedCentralCrossRef
107.
go back to reference Wang HM, Cheng NM, Lee LY, Fang YH, Chang JT, Tsan DL, et al. Heterogeneity of (18) F-FDG PET combined with expression of EGFR may improve the prognostic stratification of advanced oropharyngeal carcinoma. Int J Cancer. 2016;138:731–8.PubMedCrossRef Wang HM, Cheng NM, Lee LY, Fang YH, Chang JT, Tsan DL, et al. Heterogeneity of (18) F-FDG PET combined with expression of EGFR may improve the prognostic stratification of advanced oropharyngeal carcinoma. Int J Cancer. 2016;138:731–8.PubMedCrossRef
108.
go back to reference Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ. Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol. 2015;5:272. Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ. Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol. 2015;5:272.
109.
go back to reference Yoon HJ, Sohn I, Cho JH, Lee HY, Kim JH, Choi YL, et al. Decoding tumor phenotypes for ALK, ROS1, and RET fusions in lung adenocarcinoma using a radiomics approach. Medicine (Baltimore). 2015;94, e1753.CrossRef Yoon HJ, Sohn I, Cho JH, Lee HY, Kim JH, Choi YL, et al. Decoding tumor phenotypes for ALK, ROS1, and RET fusions in lung adenocarcinoma using a radiomics approach. Medicine (Baltimore). 2015;94, e1753.CrossRef
110.
go back to reference Upadhaya T, Morvan Y, Stindel E, Le Reste PJ, Hatt M. A framework for multimodal imaging-based prognostic model building: preliminary study on multimodal MRI in glioblastoma multiforme. IRBM, 2015;36:345–50.CrossRef Upadhaya T, Morvan Y, Stindel E, Le Reste PJ, Hatt M. A framework for multimodal imaging-based prognostic model building: preliminary study on multimodal MRI in glioblastoma multiforme. IRBM, 2015;36:345–50.CrossRef
111.
go back to reference Wang J, Kato F, Oyama-Manabe N, Li R, Cui Y, Tha KK, et al. Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI: a pilot radiomics study. PLoS One. 2015;10, e0143308.PubMedPubMedCentralCrossRef Wang J, Kato F, Oyama-Manabe N, Li R, Cui Y, Tha KK, et al. Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI: a pilot radiomics study. PLoS One. 2015;10, e0143308.PubMedPubMedCentralCrossRef
112.
go back to reference Cameron A, Khalvati F, Haider M, Wong A. MAPS: a quantitative radiomics approach for prostate cancer detection. IEEE Trans Biomed Eng. 2016;63:1145–56. Cameron A, Khalvati F, Haider M, Wong A. MAPS: a quantitative radiomics approach for prostate cancer detection. IEEE Trans Biomed Eng. 2016;63:1145–56.
113.
go back to reference Khalvati F, Wong A, Haider MA. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models. BMC Med Imaging. 2015;15:27.PubMedPubMedCentralCrossRef Khalvati F, Wong A, Haider MA. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models. BMC Med Imaging. 2015;15:27.PubMedPubMedCentralCrossRef
114.
go back to reference Sharma RR, Marikkannu P. Hybrid RGSA and support vector machine framework for three-dimensional magnetic resonance brain tumor classification. ScientificWorldJournal. 2015;2015, 184350.PubMed Sharma RR, Marikkannu P. Hybrid RGSA and support vector machine framework for three-dimensional magnetic resonance brain tumor classification. ScientificWorldJournal. 2015;2015, 184350.PubMed
Metadata
Title
Characterization of PET/CT images using texture analysis: the past, the present… any future?
Authors
Mathieu Hatt
Florent Tixier
Larry Pierce
Paul E. Kinahan
Catherine Cheze Le Rest
Dimitris Visvikis
Publication date
01-01-2017
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Nuclear Medicine and Molecular Imaging / Issue 1/2017
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-016-3427-0

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