PET Radiomic Features Variability: A Phantom Study on the Influence of Reconstruction and Discretization Method

Authors

  • Marcos Antônio Dórea Machado São Rafael Hospital https://orcid.org/0000-0002-0106-9769
  • Jean Lucas Pereira Pita Complexo Hospitalar Universitário Professor Edgard Santos/UFBA
  • Eduardo Martins Netto Universidade Federal da Bahia

DOI:

https://doi.org/10.29384/rbfm.2021.v15.19849001598

Keywords:

PET Radiomic Features, Variability, Quantification, Standardization

Abstract

We aimed to explore the variability of PET radiomic features for varying reconstruction methods and quantification settings. The IQ-NEMA phantom was scanned 5 times with a sphere to background F-18 concentration ratio of 10:1. The activity and the image duration were matched to result typical counting statistics for 18F-FDG oncologic examinations. The images were reconstructed with OSEM and PSF reconstructions, then 99 radiomic features were extracted using two discretization methods: fixed bin number (FBN = 16, 32 and 64 gray levels) and fixed bin width (FBW=0.25). This scheme resulted in a total of 1,188 features, classified as having low (<5.0%), intermediate (5-29.9%) or high (≥30%) variability. In general, FBW discretization yielded more stable features. A total of 499, 558 and 131 features had low, intermediate and high variability, respectively. First order features such as energy and entropy and textural features such as entropy (GLCM), long run emphasis and short run emphasis (GLRLM) were more likely to present low variability, regardless the reconstruction and discretization method. Other textural features such as large area emphasis (GLSZM), zone percentage (GLSZM) and complexity (NGTDM) had more frequently intermediate or high variability.These findings could facilitate features’ selection for further PET radiomic applications.

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References

1. SCHREVENS L, LORENT N, DOOMS C, VANSTEENKISTE J. The Role of PET Scan in Diagnosis, Staging, and Management of Non-Small Cell Lung Cancer. Oncologist. 2004;9:633-643.
2. Barrington SF, Mikhaeel NG, Kostakoglu L, et al. Role of imaging in the staging and response assessment of lymphoma: Consensus of the international conference on malignant lymphomas imaging working group. J Clin Oncol. 2014;32(27):3048-3058. doi:10.1200/JCO.2013.53.5229
3. Wahl RL, Jacene H, Kasamon Y, Lodge MA. Response Criteria in Solid Tumors. J Nucl Med. 2009;50(Suppl 1):122-150. doi:10.2967/jnumed.108.057307.From
4. Kanoun S, Rossi C, Casasnovas O. [18F]FDG-PET/CT in hodgkin lymphoma: Current usefulness and perspectives. Cancers (Basel). 2018;10(5):1-11. doi:10.3390/cancers10050145
5. Cook GJR, Yip C, Siddique M, et al. Are Pretreatment 18F-FDG PET Tumor Textural Features in Non-Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy? J Nucl Med. 2013;54(1):19-26. doi:10.2967/jnumed.112.107375
6. Cook GJR, Siddique M, Taylor BP, Yip C, Chicklore S, Goh V. Radiomics in PET: Principles and applications. Clin Transl Imaging. 2014;2(3):269-276. doi:10.1007/s40336-014-0064-0
7. De Geus-Oei LF, Van Der Heijden HFM, Corstens FHM, Oyen WJG. Predictive and prognostic value of FDG-PET in nonsmall-cell lung cancer. A systematic review. Cancer. 2007;110(8):1654-1664. doi:10.1002/cncr.22979
8. Vallieres M, Zwanenburg A, Badic B, Cheze-Le Rest C, Visvikis D, Hatt M. Responsible Radiomics Research for Faster Clinical Translation. J Nucl Med. Published online 2017:jnumed.117.200501. doi:10.2967/jnumed.117.200501
9. 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(1):151-165. doi:10.1007/s00259-016-3427-0
10. 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(3):414-422. doi:10.2967/jnumed.113.129858
11. Tixier F, Le Rest CC, Hatt M, 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(3):369-378. doi:10.2967/jnumed.110.082404
12. Zwanenburg A. Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging. Published online 2019. doi:10.1007/s00259-019-04391-8
13. Pfaehler E, Jong JR De, Boellaard R. Repeatability of 18 F-FDG PET radiomic features : A phantom study to explore sensitivity to image reconstruction settings , noise , and delineation method. 2018;0(0):1-14. doi:10.1002/mp.13322
14. Traverso A, Wee L, Dekker A, Gillies R. Repeatability and Reproducibility of Radiomic Features : A Systematic Review. Radiat Oncol Biol. Published online 2018. doi:10.1016/j.ijrobp.2018.05.053
15. Galavis P, Hollensen C, Jallow N, Paliwal B, Jeraj R. NIH Public Access. Acta Oncol. 2010;49(7):1012-1016. doi:10.3109/0284186X.2010.498437
16. 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. 2015;2(4):041002. doi:10.1117/1.JMI.2.4.041002
17. Boellaard R, Delgado-bolton R, Oyen WJG, et al. FDG PET / CT : EANM procedure guidelines for tumour imaging : version 2 . 0. Published online 2015:328-354. doi:10.1007/s00259-014-2961-x
18. Machado MAD, Menezes VO, Namías M, et al. Protocols for Harmonized Quantification and Noise Reduction in Low Dose Oncological 18 F-FDG PET/CT Imaging. J Nucl Med Technol. Published online 2018:jnmt.118.213405. doi:10.2967/jnmt.118.213405
19. Kanoun S, Tal I, Berriolo-riedinger A, Rossi C. Influence of Software Tool and Methodological Aspects of Total Metabolic Tumor Volume Calculation on Baseline [ 18F ] FDG PET to Predict Survival in Hodgkin Lymphoma. 2015;C:1-15. doi:10.1371/journal.pone.0140830
20. Mettler J. Metabolic Tumour Volume for Response Prediction in Advanced-Stage Hodgkin Lymphoma. J Med Imaging. 2018;i:1-25. doi:10.2967/jnumed.118.210047
21. Griethuysen J, Fedorov A, Parmar C, Hosny A, Narayan V. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2018;77(21):1-8. doi:10.1158/0008-5472.CAN-17-0339.Computational
22. Hatt M, Majdoub M, Vallieres M, 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(1):38-44. doi:10.2967/jnumed.114.144055
23. 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 Heal Informatics. 2014;18(3):946-955. doi:10.1109/JBHI.2013.2283658
24. Kaalep A, Burggraaff CN, Pieplenbosch S, et al. Quantitative implications of the updated EARL 2019 PET – CT performance standards. Published online 2019:1-16. doi:https://doi.org/10.1186/s40658-019-0257-8
25. Menezes VO, Machado MAD, Queiroz CC, et al. Optimization of oncological 18 F-FDG PET/CT imaging based on a multiparameter analysis. Med Phys. 2016;43(2):930-938. doi:10.1118/1.4940354
26. Kaalep A, Sera T, Rijnsdorp S, et al. Feasibility of state of the art PET / CT systems performance harmonisation. Published online 2018:1344-1361.
27. Ypsilantis P, Siddique M, Sohn H, Davies A. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks. PLoS One. Published online 2015:1-18. doi:10.1371/journal.pone.0137036
28. Bernardi E De, Buda A, Guerra L, et al. Radiomics of the primary tumour as a tool to improve 18 F-FDG-PET sensitivity in detecting nodal metastases in endometrial cancer. Published online 2018:1-9.
29. Collarino A, Ph D, Garganese G, et al. Radiomics in vulvar cancer : first clinical experience using 18 F-FDG PET / CT images. 2018;31(0). doi:10.2967/jnumed.118.215889
30. Zwanenburg, Alex et al. The image biomarker standardisation initiative.
31. Aide N, Lasnon C, Veit-haibach P, Sera T, Sattler B, Boellaard R. EANM / EARL harmonization strategies in PET quantification : from daily practice to multicentre oncological studies. Published online 2017. doi:10.1007/s00259-017-3740-2
32. Orlhac F, Boughdad S, Philippe C, et al. A post-reconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med. 2018;2(Md):jnumed.117.199935. doi:10.2967/jnumed.117.199935

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Published

2021-06-21

How to Cite

Antônio Dórea Machado, M. ., Pereira Pita, J. L., & Martins Netto, E. . (2021). PET Radiomic Features Variability: A Phantom Study on the Influence of Reconstruction and Discretization Method. Brazilian Journal of Medical Physics, 15, 598. https://doi.org/10.29384/rbfm.2021.v15.19849001598

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