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The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies

European Radiology volume 27, pages 4498–4509 (2017)Cite this article

Abstract

Objectives

The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings.

Methods

Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV).

Results

Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively.

Conclusions

Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies.

Key Points

• PET/CT image radiomics is a quantitative approach assessing different aspects of tumour uptake.

• Radiomic features robustness is an important issue over different image reconstruction settings.

• Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent.

• Robust radiomic features can be considered as good candidates for tumour quantification

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Abbreviations

PET:

Positron Emission Tomography

CT:

Computed Tomography

SUV:

Standard Uptake Value

NSCLC:

Non-Small Cell Lung Carcinoma

MRI:

Magnetic Resonance Imaging

NEMA:

National Electrical Manufacturers Association

FDG:

Fluoro-Deoxy-Glucose

KBq:

Kilo-Becquerel

MBq:

Mega-Becquerel

LBR:

Lesions to Background Ratio

GE:

General Electric

OSEM:

Ordered Subset Expectation Maximization

PSF:

Point Spread Function

TOF:

Time of Flight

FWHM:

Full Width at Half Maximum

VOI:

Volume of Interest

GLCM:

Gray Level Co-occurrence Matrix

GLRLM:

Gray-Level Run-Length Matrix

GLSZM:

Gray-Level Size Zone Matrix

NGLD:

Neighboring Gray Level Dependence

NGTDM:

Neighborhood Gray-Tone Difference Matrix

TFC:

Texture Feature Coding

TS:

Texture Spectrum

COV:

Coefficient Of Variation

ICC:

Inter-Class Correlation

FBP:

Filtered Back Projection

RECIST:

Response Evaluation Criteria in Solid Tumours

PERCIST:

PET Response Criteria in Solid Tumours

References

  1. Wahl RL (2008) Principles and practice of PET and PET/CT. Lippincott Williams & Wilkins, Philadelphia

    Google Scholar 

  2. Rahmim A, Wahl R (2006) An overview of clinical PET/CT. Iran J Nucl Med 14:1–14

    Google Scholar 

  3. Hatt M, Majdoub M, Vallieres M, Tixier F, Le Rest CC, Groheux D et al (2015) F-18-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 56:38–44

    CAS  Article  Google Scholar 

  4. Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP et al (2011) Intratumor heterogeneity characterized by textural features on baseline (18)F-FDG pet images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52:369–378

    Article  Google Scholar 

  5. Cook GJR, Siddique M, Taylor BP, Yip C, Chicklore S, Goh V (2014) Radiomics in PET: principles and applications. Clin Transl Imaging 2:269–276

    Article  Google Scholar 

  6. Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    CAS  Article  Google Scholar 

  7. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  Google Scholar 

  8. Kumar V, Gu YH, Basu S, Berglund A, Eschrich SA, Schabath MB et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248

    Article  Google Scholar 

  9. Lu L, Lv W, Jiang J, Ma J, Feng Q, Rahmim A et al (2016) Robustness of radiomic features in [11C]Choline and [18F]FDG PET/CT imaging of nasopharyngeal carcinoma: impact of segmentation and discretization. Mol Imaging Biol 18:935–945

    CAS  Article  Google Scholar 

  10. Oh J, Apte A, Folkerts M, Kohutek Z, Wu A, Rimmer A, Lee N, Deasy J. (2014) FDG-PET-based radiomics to predict local control and survival following radiotherapy. Annual Meeting of The American Association of Physicists in Medicine 2014

  11. Leijenaar RTH, Carvalho S, Velazquez ER, Van Elmpt WJC, Parmar C, Hoekstra OS et al (2013) Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol 52:1391–1397

    CAS  Article  Google Scholar 

  12. Soufi M, Kamali-Asl A, Geramifar P, Rahmim A (2016) A novel framework for automated segmentation and labeling of homogeneous versus heterogeneous lung tumors in [18F]FDG PET imaging. Molec Imag Biol. In Press. doi:10.1007/s11307-016-1015-0

    Article  Google Scholar 

  13. Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJR (2013) Quantifying tumour heterogeneity in F-18-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 40:133–140

    Article  Google Scholar 

  14. El Naqa I, Grigsby PW, Apte A, Kidd E, Donnelly E, Khullar D et al (2009) Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recogn 42:1162–1171

    Article  Google Scholar 

  15. Hatt M, Le Pogam A, Visvikis D, Pradier O, Le Rest CC (2012) Impact of partial-volume effect correction on the predictive and prognostic value of baseline F-18-FDG PET images in esophageal cancer. J Nucl Med 53:12–20

    CAS  Article  Google Scholar 

  16. Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D (2017) Characterization of PET/CT images using texture analysis: the past, the presenta… any future? Eur J Nucl Med Mol Imaging 44:151–165

    Article  Google Scholar 

  17. Rahmim A, Salimpour Y, Jain S, Blinder S, Klyuzhin IS, Smith G, et al. (2016) Application of texture analysis to DAT SPECT imaging: relationship to clinical assesments. NeuroImage: Clin 12. doi: 10.1016/j.nicl.2016.02.012

    Article  Google Scholar 

  18. Vallières M, Freeman C, Skamene S, El Naqa I (2015) 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 60:5471

    Article  Google Scholar 

  19. Yang F, Thomas MA, Dehdashti F, Grigsby PW (2013) Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer. Eur J Nucl Med Mol Imaging 40:716–727

    CAS  Article  Google Scholar 

  20. Tan S, Kligerman S, Chen W, Lu M, Kim G, Feigenberg S et al (2013) Spatial-temporal [18 F] FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. Int J Radiat Oncol Biol Phys 85:1375–1382

    Article  Google Scholar 

  21. Ashrafinia S, Gonzalez EM, Mohy-ud-Din H, Jha A, Subramaniam RM, Rahmim A (2016) Adaptive PSF modeling for enhanced heterogeneity quantification in oncologic PET imaging. Proc Soc Nuc Med Med Imag Ann Meet 57:497

    Google Scholar 

  22. Shiri IRA, Abdollahi H, Ghafarian P, Bitarafan-Rajabi A, AY MR, BakhshaieshKaram M, (Suppl 1) (2016) Radiomics texture features variability and reproducibility in advance image reconstruction setting of oncological PET/CT. Eur J Nucl Med Mol Imaging 43:S1-S734

    Google Scholar 

  23. Leijenaar RT, Nalbantov G, Carvalho S, van Elmpt WJ, Troost EG, Boellaard R et al (2015) The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep 5:11075

    CAS  Article  Google Scholar 

  24. van Velden FH, Kramer GM, Frings V, Nissen IA, Mulder ER, de Langen AJ et al (2016) Repeatability of radiomic features in non-small-cell lung cancer [18F] FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol 18:788–795

    Article  Google Scholar 

  25. Oliver JA, Budzevich M, Zhang GG, Dilling TJ, Latifi K, Moros EG (2015) Variability of image features computed from conventional and respiratory-gated PET/CT images of lung cancer. Transl Oncol 8:524–534

    Article  Google Scholar 

  26. Rahmim A, Qi J, Sossi V (2013) Resolution modeling in PET imaging: theory, practice, benefits, and pitfalls. Med Phys 40:064301

    Article  Google Scholar 

  27. Tong S, Alessio AM, Kinahan PE (2010) Noise and signal properties in PSF-based fully 3D PET image reconstruction: an experimental evaluation. Phys Med Biol 55:1453–1473

    CAS  Article  Google Scholar 

  28. Alessio A, Rahmim A, Orton CG (2013) Resolution modeling enhances PET imaging (point/counterpoint). Med Phys 40:120601

    Article  Google Scholar 

  29. Schaefferkoetter J, Casey M, Townsend D, El Fakhri G (2013) Clinical impact of time-of-flight and point response modeling in PET reconstructions: a lesion detection study. Phys Med Biol 58:1465–1478

    Article  Google Scholar 

  30. Kadrmas DJ, Casey ME, Conti M, Jakoby BW, Lois C, Townsend DW (2009) Impact of time-of-flight on PET tumor detection. J Nucl Med 50:1315–1323

    Article  Google Scholar 

  31. Moses WW (2003) Time of flight in PET revisited. IEEE Trans Nucl Sci 50:1325–1330

    Article  Google Scholar 

  32. Surti S (2015) Update on time-of-Flight PET imaging. J Nucl Med 56:98–105

    Article  Google Scholar 

  33. Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2:1636–1642

    Article  Google Scholar 

  34. Kotasidis FA, Tsoumpas C, Rahmim A (2014) Advanced kinetic modelling strategies: towards adoption in clinical PET imaging. Clin Transl Imaging 2:219–237

    Article  Google Scholar 

  35. Karakatsanis NA, Lodge MA, Tahari AK, Zhou Y, Wahl RL, Rahmim A (2013) Dynamic whole body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application. Phys Med Bio 58:7391–7418

    Article  Google Scholar 

  36. Huang S-C (2000) Anatomy of SUV. Nucl Med Biol 27:643–646

    CAS  Article  Google Scholar 

  37. Nyflot MJ, Yang F, Byrd D, Bowen SR, Sandison GA, Kinahan PE (2015) Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards. J Med Imaging 2:041002

    Article  Google Scholar 

  38. Cheng N-M, Fang Y-HD, Tsan D-L, Hsu C-H, Yen T-C (2016) Respiration-averaged CT for attenuation correction of PET images–impact on PET texture features in non-small cell lung cancer patients. PLoS One 11, e0150509

    Article  Google Scholar 

  39. Doumou G, Siddique M, Tsoumpas C, Goh V, Cook GJ (2015) The precision of textural analysis in 18F-FDG-PET scans of oesophageal cancer. Eur Radiol 25:2805–2812

    Article  Google Scholar 

  40. Yan J, Chu-Shern JL, Loi HY, Khor LK, Sinha AK, Quek ST et al (2015) Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med 56:1667–1673

    CAS  Article  Google Scholar 

  41. Bailly C, Bodet-Milin C, Couespel S, Necib H, Kraeber-Bodéré F, Ansquer C et al (2016) Revisiting the robustness of PET-based textural features in the context of multi-centric trials. PLoS One 11, e0159984

    Article  Google Scholar 

  42. Cortes-Rodicio J, Sanchez-Merino G, Garcia-Fidalgo M, Tobalina-Larrea I (2016) Identification of low variability textural features for heterogeneity quantification of 18 F-FDG PET/CT imaging. Rev Esp Med Nucl Imagen Mol 35:379–384

    CAS  PubMed  Google Scholar 

  43. Forgacs A, Jonsson HP, Dahlbom M, Daver F, DiFranco MD, Opposits G et al (2016) A study on the basic criteria for selecting heterogeneity parameters of F18-FDG PET images. PLoS One 11, e0164113

    Article  Google Scholar 

  44. Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R (2010) Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol 49:1012–1016

    Article  Google Scholar 

  45. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247

    CAS  Article  Google Scholar 

  46. Wahl RL, Jacene H, Kasamon Y, Lodge MA, Suppl_1 (2009) From RECIST to PERCIST: evolving considerations for pet response criteria in solid tumors. J Nucl Med 50:122S-50S

    Article  Google Scholar 

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Acknowledgements

The authors sincerely thank the PET/CT Departments at Masih Daneshvari and Shariati Hospitals for their collaboration and facilities.

Author information

Affiliations

  1. Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Junction of Shahid Hemmat and Shahid Chamran Expressways, Tehran, Iran

    Isaac Shiri, Hamid Abdollahi & Ahmad Bitarafan-Rajabi

  2. Department of Radiology, Johns Hopkins University, Baltimore, MD, 21287, USA

    Arman Rahmim

  3. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA

    Arman Rahmim

  4. Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Pardis Ghaffarian

  5. PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Pardis Ghaffarian

  6. Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

    Parham Geramifar

  7. Department of Nuclear Medicine, Rajaei Cardiovascular, Medical and Research Center, Iran University of Medical Sciences, Vali-Asr Avenue, Niyayesh Blvd, Tehran, Iran

    Ahmad Bitarafan-Rajabi

Authors
  1. Isaac Shiri
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  2. Arman Rahmim
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Corresponding authors

Correspondence to Hamid Abdollahi or Ahmad Bitarafan-Rajabi.

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Guarantor

The scientific guarantor of this publication is Hamid Abdollahi, BS, MS, PhD.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Funding

This study has received funding by the Iran University of Medical Sciences, Tehran, Iran with the grant number 27870.

Statistics and biometry

All authors kindly provided statistical advice for this manuscript.

One of the authors has significant statistical expertise.

Ethical approval

Institutional Review Board approval was obtained.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Methodology

• prospective

• diagnostic or prognostic study/experimental

• multicenter study

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Shiri, I., Rahmim, A., Ghaffarian, P. et al. The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies. Eur Radiol 27, 4498–4509 (2017). https://doi.org/10.1007/s00330-017-4859-z

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  • DOI: https://doi.org/10.1007/s00330-017-4859-z

Keywords

  • PET/CT
  • Radiomics
  • Robustness
  • Reconstruction settings
  • Quantification