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Multiparametric voxel-based analyses of standardized uptake values and apparent diffusion coefficients of soft-tissue tumours with a positron emission tomography/magnetic resonance system: Preliminary results

  • Musculoskeletal
  • Published:
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Abstract

Objectives

To investigate the usefulness of voxel-based analysis of standardized uptake values (SUVs) and apparent diffusion coefficients (ADCs) for evaluating soft-tissue tumour malignancy with a PET/MR system.

Methods

Thirty-five subjects with either ten low/intermediate-grade tumours or 25 high-grade tumours were prospectively enrolled. Zoomed diffusion-weighted and fluorodeoxyglucose (18FDG)-PET images were acquired along with fat-suppressed T2-weighted images (FST2WIs). Regions of interest (ROIs) were drawn on FST2WIs including the tumour in all slices. ROIs were pasted onto PET and ADC-maps to measure SUVs and ADCs within tumour ROIs. Tumour volume, SUVmax, ADCminimum, the heterogeneity and the correlation coefficients of SUV and ADC were recorded. The parameters of high- and low/intermediate-grade groups were compared, and receiver operating characteristic (ROC) analysis was also performed.

Results

The mean correlation coefficient for SUV and ADC in high-grade sarcomas was lower than that of low/intermediate-grade tumours (−0.41 ± 0.25 vs. −0.08 ± 0.34, P < 0.01). Other parameters did not differ significantly. ROC analysis demonstrated that correlation coefficient showed the best diagnostic performance for differentiating the two groups (AUC 0.79, sensitivity 96.0%, specificity 60%, accuracy 85.7%).

Conclusions

SUV and ADC determined via PET/MR may be useful for differentiating between high-grade and low/intermediate-grade soft tissue tumours.

Key Points

• PET/MR allows voxel-based comparison of SUVs and ADCs in soft-tissue tumours.

• A comprehensive assessment of internal heterogeneity was performed with scatter plots.

• SUVmax or ADCminimum could not differentiate high-grade sarcoma from low/intermediate-grade tumours.

• Only the correlation coefficient between SUV and ADC differentiated the two groups.

• The correlation coefficient showed the best diagnostic performance by ROC analysis.

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References

  1. Clark MA, Fisher C, Judson I, Thomas JM (2005) Soft-tissue sarcomas in adults. N Engl J Med 353:701–711

    Article  CAS  PubMed  Google Scholar 

  2. Fletcher CDM (2013) WHO Classification of tumours of soft tissue and bone: IARC Press

  3. Fayad LM, Jacobs MA, Wang X, Carrino JA, Bluemke DA (2012) Musculoskeletal tumors: how to use anatomic, functional, and metabolic MR techniques. Radiology 265:340–356

    Article  PubMed  PubMed Central  Google Scholar 

  4. De Schepper AM, De Beuckeleer L, Vandevenne J, Somville J (2000) Magnetic resonance imaging of soft tissue tumors. Eur Radiol 10:213–223

    Article  PubMed  Google Scholar 

  5. Zhao F, Ahlawat S, Farahani SJ et al (2014) Can MR imaging be used to predict tumor grade in soft-tissue sarcoma? Radiology 272:192–201

    Article  PubMed  Google Scholar 

  6. Charest M, Hickeson M, Lisbona R, Novales-Diaz JA, Derbekyan V, Turcotte R (2009) FDG PET/CT imaging in primary osseous and soft tissue sarcomas: a retrospective review of 212 cases. Eur J Nucl Med Mol Imaging 36:1944–1951

    Article  PubMed  Google Scholar 

  7. Bastiaannet E, Groen H, Jager PL et al (2004) The value of FDG-PET in the detection, grading and response to therapy of soft tissue and bone sarcomas; a systematic review and meta-analysis. Cancer Treat Rev 30:83–101

    Article  CAS  PubMed  Google Scholar 

  8. Schulte M, Brecht-Krauss D, Heymer B et al (2000) Grading of tumors and tumorlike lesions of bone: evaluation by FDG PET. J Nucl Med 41:1695–701

    CAS  PubMed  Google Scholar 

  9. Aoki J, Watanabe H, Shinozaki T, Tokunaga M, Inoue T, Endo K (1999) FDG-PET in differential diagnosis and grading of chondrosarcomas. J Comput Assist Tomogr 23:603–8

    Article  CAS  PubMed  Google Scholar 

  10. Gibbs P, Liney GP, Pickles MD, Zelhof B, Rodrigues G, Turnbull LW (2009) Correlation of ADC and T2 measurements with cell density in prostate cancer at 3.0 Tesla. Investig Radiol 44:572–576

    Article  Google Scholar 

  11. Koral K, Mathis D, Gimi B et al (2013) Common Pediatric Cerebellar Tumors: Correlation between Cell Densities and Apparent Diffusion Coefficient Metrics. Radiology 268:532–537

    Article  PubMed  Google Scholar 

  12. Del Grande F, Subhawong T, Weber K, Aro M, Mugera C, Fayad LM (2014) Detection of soft-tissue sarcoma recurrence: added value of functional MR imaging techniques at 3.0 T. Radiology 271:499–511

    Article  PubMed  Google Scholar 

  13. Dudeck O, Zeile M, Pink D et al (2008) Diffusion-weighted magnetic resonance imaging allows monitoring of anticancer treatment effects in patients with soft-tissue sarcomas. J Magn Reson Imaging 27:1109–1113

    Article  PubMed  Google Scholar 

  14. Wang X, Jacobs MA, Fayad L (2011) Therapeutic response in musculoskeletal soft tissue sarcomas: evaluation by MRI. NMR Biomed 24:750–763

    Article  PubMed  PubMed Central  Google Scholar 

  15. Soldatos T, Ahlawat S, Montgomery E, Chalian M, Jacobs MA, Fayad LM (2016) Multiparametric assessment of treatment response in high-grade soft-tissue sarcomas with anatomic and functional MR imaging sequences. Radiology 278:831–840

    Article  PubMed  Google Scholar 

  16. Baba S, Isoda T, Maruoka Y et al (2014) Diagnostic and prognostic value of pretreatment SUV in 18F-FDG/PET in breast cancer: comparison with apparent diffusion coefficient from diffusion-weighted MR imaging. J Nucl Med 55:736–742

    Article  CAS  PubMed  Google Scholar 

  17. Regier M, Derlin T, Schwarz D et al (2012) Diffusion weighted MRI and 18F-FDG PET/CT in non-small cell lung cancer (NSCLC): does the apparent diffusion coefficient (ADC) correlate with tracer uptake (SUV)? Eur J Radiol 81:2913–2918

    Article  CAS  PubMed  Google Scholar 

  18. Preda L, Conte G, Bonello L et al (2016) Combining standardized uptake value of FDG-PET and apparent diffusion coefficient of DW-MRI improves risk stratification in head and neck squamous cell carcinoma. Eur Radiol 26:4432–4441

    Article  PubMed  Google Scholar 

  19. Sagiyama K, Watanabe Y, Kamei R, Baba S, Honda H (2016) Comparison of positron emission tomography diffusion-weighted imaging (PET/DWI) registration quality in a PET/MR scanner: Zoomed DWI vs Conventional DWI. J Magn Reson Imaging 43:853–858

    Article  PubMed  Google Scholar 

  20. Sagiyama K, Watanabe Y, Kamei R, Shinyama D, Baba S, Honda H (2016) An improved MR sequence for attenuation correction in PET/MR hybrid imaging. Magn Reson Imaging 34:345–352

    Article  PubMed  Google Scholar 

  21. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16:187–198

    Article  CAS  PubMed  Google Scholar 

  22. Viola P, Wells WM III (1997) Alignment by Maximization of Mutual Information. Int J Comput Vis 24:137–154

    Article  Google Scholar 

  23. Helenius J, Soinne L, Perkio J et al (2002) Diffusion-weighted MR imaging in normal human brains in various age groups. AJNR Am J Neuroradiol 23:194–199

    PubMed  Google Scholar 

  24. Maeda M, Maier SE, Sakuma H, Ishida M, Takeda K (2006) Apparent diffusion coefficient in malignant lymphoma and carcinoma involving cavernous sinus evaluated by line scan diffusion-weighted imaging. J Magn Reson Imaging 24:543–548

    Article  PubMed  Google Scholar 

  25. Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35

    Article  CAS  PubMed  Google Scholar 

  26. Schmidt H, Brendle C, Schraml C et al (2013) Correlation of simultaneously acquired diffusion-weighted imaging and 2-deoxy-[18F] fluoro-2-D-glucose positron emission tomography of pulmonary lesions in a dedicated whole-body magnetic resonance/positron emission tomography system. Investig Radiol 48:247–255

    Article  Google Scholar 

  27. Donati OF, Mazaheri Y, Afaq A et al (2014) Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient. Radiology 271:143–152

    Article  PubMed  Google Scholar 

  28. Pope WB, Kim HJ, Huo J et al (2009) Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 252:182–189

    Article  PubMed  Google Scholar 

  29. Burger IA, Vargas HA, Apte A et al (2014) PET quantification with a histogram derived total activity metric: superior quantitative consistency compared to total lesion glycolysis with absolute or relative SUV thresholds in phantoms and lung cancer patients. Nucl Med Biol 41:410–418

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Muzic RF, DiFilippo FP (2014) PET/MRI – Technical Review. Semin Roentgenol 49:242–54

    Article  PubMed  PubMed Central  Google Scholar 

  31. Costelloe CM, Chuang HH, Madewell JE (2014) FDG PET/CT of primary bone tumors. AJR Am J Roentgenol 202:W521–W531

    Article  PubMed  Google Scholar 

  32. Iima M, Le Bihan D (2016) Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future. Radiology 278:13–32

    Article  PubMed  Google Scholar 

  33. Heusch P, Buchbender C, Beiderwellen K et al (2013) Standardized uptake values for [18F] FDG in normal organ tissues: comparison of whole-body PET/CT and PET/MRI. Eur J Radiol 82:870–876

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan

    Koji Sagiyama, Ryotaro Kamei & Hiroshi Honda

  2. Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan

    Yuji Watanabe & Satoshi Kawanami

  3. Healthcare, Philips Electronics Japan, Minato-ku, Tokyo, Japan

    Sungtak Hong

  4. Departmant of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan

    Yoshihiro Matsumoto

Authors
  1. Koji Sagiyama
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  2. Yuji Watanabe
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  3. Ryotaro Kamei
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  4. Sungtak Hong
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  5. Satoshi Kawanami
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  6. Yoshihiro Matsumoto
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  7. Hiroshi Honda
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Corresponding author

Correspondence to Yuji Watanabe.

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Guarantor

The scientific guarantor of this publication is Yuji Watanabe

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Y. Watanabe and S. Kawanami: Bayer Healthcare Japan, Modest, Research Grant; Philips Electronics Japan, Modest, Research Grant.

Funding

This study received funding by JSPS KAKENHI Grant Number JP16K19827 and the Fukuoka Foundation for Sound Health Cancer Research Fund.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• diagnostic

• performed at one institution

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Sagiyama, K., Watanabe, Y., Kamei, R. et al. Multiparametric voxel-based analyses of standardized uptake values and apparent diffusion coefficients of soft-tissue tumours with a positron emission tomography/magnetic resonance system: Preliminary results. Eur Radiol 27, 5024–5033 (2017). https://doi.org/10.1007/s00330-017-4912-y

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  • DOI: https://doi.org/10.1007/s00330-017-4912-y

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