18F-FDG PET/CT heterogeneity quantification through textural features in the era of harmonisation programs: a focus on lung cancer

European Journal of Nuclear Medicine and Molecular Imaging

Volumn 43, Issue 13, 2016, Pages 2324-2335

  Lasnon, Charline ^a,b,c   Majdoub, Mohamed ^d   Lavigne, Brice ^d   Do, Pascal ^e   Madelaine, Jeannick ^a   Visvikis, Dimitris ^d   Hatt, Mathieu ^d,f   Aide, Nicolas ^a,b,c  

^a CAEN UNIVERSITY HOSPITAL   (France)

^b UNIVERSITÉ DE CAEN   (France)

^c NORMANDIE UNIVERSITÉ   (France)

^d INSERM   (France)

^e François Baclesse Cancer Centre   (France)

^f Universite de Bretagne Occidentale   (France)

Author keywords

EARL accreditation program; FDG PET CT; Harmonisation; Heterogeneity; Lung cancer; Quantification

Indexed keywords

FLUORODEOXYGLUCOSE F 18; RADIOPHARMACEUTICAL AGENT;

ACCREDITATION; ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; BAYES THEOREM; CANCER DIAGNOSIS; COMPUTER ASSISTED EMISSION TOMOGRAPHY; ENTROPY; FEMALE; FILTER; HISTOGRAM; HUMAN; IMAGE RECONSTRUCTION; LARGE CELL LUNG CARCINOMA; LUNG ADENOCARCINOMA; LUNG BIOPSY; LUNG CANCER; MAJOR CLINICAL STUDY; MALE; NON SMALL CELL LUNG CANCER; POSITRON EMISSION TOMOGRAPHY; POTENTIAL DIFFERENCE; PROSPECTIVE STUDY; SMALL CELL LUNG CANCER; SQUAMOUS CELL LUNG CARCINOMA; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; EUROPE; LUNG TUMOR; MIDDLE AGED; POSITRON EMISSION TOMOGRAPHY-COMPUTED TOMOGRAPHY; PRACTICE GUIDELINE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STANDARDS; THREE DIMENSIONAL IMAGING; TUMOR VOLUME; VERY ELDERLY;

ADULT; AGED; AGED, 80 AND OVER; EUROPE; FEMALE; FLUORODEOXYGLUCOSE F18; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; LUNG NEOPLASMS; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; POSITRON EMISSION TOMOGRAPHY COMPUTED TOMOGRAPHY; PRACTICE GUIDELINES AS TOPIC; RADIOPHARMACEUTICALS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TUMOR BURDEN;

EID: 84975319282     PISSN: 16197070     EISSN: 16197089     Source Type: Journal    
DOI: 10.1007/s00259-016-3441-2     Document Type: Article

References (44)

1. Boellaard R. Standards for PET image acquisition and quantitative data analysis. J Nucl Med. 2009;50 Suppl 1:11S–20S. doi:10.2967/jnumed.108.057182.
2. Boellaard R. Methodological aspects of multicenter studies with quantitative PET. Methods Mol Biol. 2011;727:335–49. doi:10.1007/978-1-61779-062-1_18.
3. Boellaard R. Mutatis mutandis: harmonize the standard! J Nucl Med. 2012;53:1–3. doi:10.2967/jnumed.111.094763.
4. American College of Radiology, Nuclear Medicine and PET accreditation program http://www.acraccreditation.org/Modalities/Nuclear-Medicine-and-PET.
5. European Association of Nuclear Medicine. EARL FDG-PET/CT accreditation. 2015. http://earl.eanm.org/cms/website.php?id=/en/projects/fdg_pet_ct_accreditation.htm.
6. The Society of Nuclear Medicine and Molecular Imaging Clinical trials network (SNMMI-CTN). Scanner validation program. http://www.snmmi.org/Research/Content.aspx?ItemNumber=5482&navItemNumber=6834.
7. Makris NE, Huisman MC, Kinahan PE, Lammertsma AA, Boellaard R. Evaluation of strategies towards harmonization of FDG PET/CT studies in multicentre trials: comparison of scanner validation phantoms and data analysis procedures. Eur J Nucl Med Mol Imaging. 2013;40:1507–15. doi:10.1007/s00259-013-2465-0.
8. Panin VY, Kehren F, Michel C, Casey M. Fully 3-D PET reconstruction with system matrix derived from point source measurements. IEEE Trans Med Imaging. 2006;25:907–21.
9. Teoh EJ, McGowan DR, Macpherson RE, Bradley KM, Gleeson FV. Phantom and clinical evaluation of the Bayesian penalized likelihood reconstruction algorithm Q.Clear on an LYSO PET/CT system. J Nucl Med. 2015. doi:10.2967/jnumed.115.159301.
10. 18F-FDG PET: how small are the metastases that we can detect with new generation clinical PET systems? Eur J Nucl Med Mol Imaging. 2014;41:1103–12. doi:10.1007/s00259-014-2689-7.
11. Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54. doi:10.1007/s00259-014-2961-x.
12. Lasnon C, Desmonts C, Quak E, Gervais R, Do P, Dubos-Arvis C, et al. Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients. Eur J Nucl Med Mol Imaging. 2013;40:985–96. doi:10.1007/s00259-013-2391-1.
13. Quak E, Le Roux PY, Hofman MS, Robin P, Bourhis D, Callahan J, et al. Harmonizing FDG PET quantification while maintaining optimal lesion detection: prospective multicentre validation in 517 oncology patients. Eur J Nucl Med Mol Imaging. 2015. doi:10.1007/s00259-015-3128-0.
14. Mertens J, Dobbeleir A, Ham H, D’Asseler Y, Goethals I, Van de Wiele C. Standardized added metabolic activity (SAM): a partial volume independent marker of total lesion glycolysis in liver metastases. Eur J Nucl Med Mol Imaging. 2012;39:1441–8. doi:10.1007/s00259-012-2166-0.
15. Visvikis D, Hatt M, Tixier F, Cheze Le Rest C. The age of reason for FDG PET image-derived indices. Eur J Nucl Med Mol Imaging. 2012;39:1670–2. doi:10.1007/s00259-012-2239-0.
16. 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 Recogn. 2009;42:1162–71. doi:10.1016/j.patcog.2008.08.011.
17. Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol. 2010;49:1012–6. doi:10.3109/0284186X.2010.498437.
18. Yan J, Chu-Shern JL, 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. doi:10.2967/jnumed.115.156927.
19. Hatt M, Majdoub M, Vallieres 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. doi:10.2967/jnumed.114.144055.
20. Calais J, Dubray B, Nkhali L, Thureau S, Lemarignier C, Modzelewski R, et al. High FDG uptake areas on pre-radiotherapy PET/CT identify preferential sites of local relapse after chemoradiotherapy for locally advanced oesophageal cancer. Eur J Nucl Med Mol Imaging. 2015;42:858–67. doi:10.1007/s00259-015-3004-y.
21. Sauter AW, Schwenzer N, Divine MR, Pichler BJ, Pfannenberg C. Image-derived biomarkers and multimodal imaging strategies for lung cancer management. Eur J Nucl Med Mol Imaging. 2015;42:634–43. doi:10.1007/s00259-014-2974-5.
22. Takeuchi S, Khiewvan B, Fox PS, Swisher SG, Rohren EM, Bassett Jr RL, et al. Impact of initial PET/CT staging in terms of clinical stage, management plan, and prognosis in 592 patients with non-small-cell lung cancer. Eur J Nucl Med Mol Imaging. 2014;41:906–14. doi:10.1007/s00259-013-2672-8.
23. Vera P, Mezzani-Saillard S, Edet-Sanson A, Menard JF, Modzelewski R, Thureau S, et al. FDG PET during radiochemotherapy is predictive of outcome at 1 year in non-small-cell lung cancer patients: a prospective multicentre study (RTEP2). Eur J Nucl Med Mol Imaging. 2014;41:1057–65. doi:10.1007/s00259-014-2687-9.
24. 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. doi:10.1148/radiol.2015142920.
25. 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.
26. Aide N, Desmonts C, Beauregard JM, Beyer T, Kinross K, Roselt P, et al. High throughput static and dynamic small animal imaging using clinical PET/CT: potential preclinical applications. Eur J Nucl Med Mol Imaging. 2010;37:991–1001. doi:10.1007/s00259-009-1352-1.
27. Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging. 2009;28:881–93. doi:10.1109/TMI.2008.2012036.
28. Hatt M, Cheze Le Rest C, Albarghach N, Pradier O, Visvikis D. PET functional volume delineation: a robustness and repeatability study. Eur J Nucl Med Mol Imaging. 2011;38:663–72. doi:10.1007/s00259-010-1688-6.
29. Hatt M, Cheze-Le Rest C, Aboagye EO, Kenny LM, Rosso L, Turkheimer FE, et al. Reproducibility of 18F-FDG and 3′-deoxy-3′-18F-fluorothymidine PET tumor volume measurements. J Nucl Med. 2010;51:1368–76. doi:10.2967/jnumed.110.078501.
30. Hatt M, Cheze le Rest 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. doi:10.1016/j.ijrobp.2009.08.018.
31. Arens AI, Troost EG, Hoeben BA, Grootjans W, Lee JA, Gregoire V, et al. Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome. Eur J Nucl Med Mol Imaging. 2014;41:915–24. doi:10.1007/s00259-013-2651-0.
32. 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. doi:10.1007/s00259-011-1845-6.
33. Hatt M, Tixier F, Cheze Le Rest C, Pradier O, Visvikis D. Robustness of intratumour (1)(8)F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging. 2013;40:1662–71. doi:10.1007/s00259-013-2486-8.
34. 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. doi:10.2967/jnumed.111.099127.
35. Vallieres 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. doi:10.1088/0031-9155/60/14/5471.
36. Webb AG. Introduction to biomedical imaging. Wiley-IEEE Press; 2003.
37. van Velden FH, Kramer GM, Frings V, Nissen IA, Mulder ER, de Langen AJ, et al. Repeatability of radiomic features in non-small-cell lung cancer [F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol Off Publ Acad Mol Imaging. 2016. doi:10.1007/s11307-016-0940-2.
38. 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. doi:10.1117/1.JMI.2.4.041002.
39. Sunderland JJ, Christian PE. Quantitative PET/CT scanner performance characterization based upon the society of nuclear medicine and molecular imaging clinical trials network oncology clinical simulator phantom. J Nucl Med. 2015;56:145–52. doi:10.2967/jnumed.114.148056.
40. 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. doi:10.1007/s00259-015-3110-x.
41. Ha S, Choi H, Cheon GJ, Kang KW, Chung JK, Kim EE, et al. Autoclustering of non-small cell lung carcinoma subtypes on (18)F-FDG PET using texture analysis: a preliminary result. Nucl Med Mol Imaging. 2014;48:278–86. doi:10.1007/s13139-014-0283-3.
42. Chung C. Tyrosine kinase inhibitors for epidermal growth factor receptor gene mutation-positive non-small cell lung cancers: an update for recent advances in therapeutics. J Oncol Pharm Pract. 2015. doi:10.1177/1078155215577810.
43. Caicedo C, Garcia-Velloso MJ, Lozano MD, Labiano T, Vigil Diaz C, Lopez-Picazo JM, et al. Role of [(1)(8)F]FDG PET in prediction of KRAS and EGFR mutation status in patients with advanced non-small-cell lung cancer. Eur J Nucl Med Mol Imaging. 2014;41:2058–65. doi:10.1007/s00259-014-2833-4.
44. Ko KH, Hsu HH, Huang TW, Gao HW, Shen DH, Chang WC, et al. Value of (1)(8)F-FDG uptake on PET/CT and CEA level to predict epidermal growth factor receptor mutations in pulmonary adenocarcinoma. Eur J Nucl Med Mol Imaging. 2014;41:1889–97. doi:10.1007/s00259-014-2802-y.