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Diffusion-weighted magnetic resonance imaging for the initial characterization of non-fatty soft tissue tumors: correlation between T2 signal intensity and ADC values

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Abstract

Objective

To evaluate the performance of quantitative diffusion-weighted imaging (DWI) correlated with T2 signal in differentiating non-fatty benign from malignant tumors.

Material and methods

A total of 76 patients with a histologically confirmed non-fatty soft tissue tumors (46 benign and 30 malignant) were prospectively included in this ethics committee approved study. All patients signed an informed consent and underwent MRI with DWI with two b values (0 and 600). ADC values from the solid components of these tumors were obtained and were correlated with the lesion’s signal intensity on T2-weighted fat-saturated sequences. ADC values were obtained from adjacent normal muscle to allow calculation of tumor/muscle ADC ratios.

Results

There were 58 hyperintense and 18 iso or hypointense lesions. All hypointense lesions were benign. The mean ADC values for benign and malignant tumors were 1.47 ± 0.54 × 10−3 and 1.17 ± 0.38 × 10−3 mm2/s respectively (p < 0.005). The mean ADC ratio in benign iso or hypointense tumors was significantly lower than that of hyperintense ones (0.76 ± 0.21 versus 1.58 ± 0.82 – p < 0.0001). An ADC ratio lower than 0.915 was highly specific for malignancy (96.4 %), whereas an ADC ratio higher than 1.32 was highly sensitive for benign lesions (90 %).

Conclusion

ADC analysis can be useful in the initial characterization of T2 hyperintense non-fatty soft tissue masses, although this technique alone is not likely to change patient management.

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References

  1. Costa FM, Ferreira EC, Vianna EM. Diffusion-weighted magnetic resonance imaging for the evaluation of musculoskeletal tumors. Magn Reson Imaging Clin N Am. 2011;19:159–80.

    Article  PubMed  Google Scholar 

  2. Charles-Edwards EM, deSouza NM. Diffusion-weighted magnetic resonance imaging and its application to cancer. Cancer Imaging. 2006;6:135–43.

    Article  PubMed Central  PubMed  Google Scholar 

  3. Gielen JL, De Schepper AM, Vanhoenacker F, et al. Accuracy of MRI in characterization of soft tissue tumors and tumor-like lesions: a prospective study in 548 patients. Eur Radiol. 2004;14:2320–30.

    Article  PubMed  Google Scholar 

  4. Wu JS, Hochman MG. Soft-tissue tumors and tumorlike lesions: a systematic imaging approach. Radiology. 2009;253:297–316.

    Article  PubMed  Google Scholar 

  5. Fletcher CD. The evolving classification of soft tissue tumours: an update based on the new WHO classification. Histopathology. 2006;48:3–12.

    Article  PubMed  CAS  Google Scholar 

  6. Koh DM, Collins DJ. Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol. 2007;188:1622–35.

    Article  PubMed  Google Scholar 

  7. Koh DM, Takahara T, Imai Y, Collins DJ. Practical aspects of assessing tumors using clinical diffusion-weighted imaging in the body. Magn Reson Med Sci. 2007;6:211–24.

    Article  PubMed  Google Scholar 

  8. Vermoolen MA, Kwee TC, Nievelstein RA. Apparent diffusion coefficient measurements in the differentiation between benign and malignant lesions: a systematic review. Insights Imaging. 2012;3:395–409.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  9. Fayad LM, Jacobs MA, Wang X, Carrino JA, Bluemke DA. Musculoskeletal tumors: how to use anatomic, functional, and metabolic MR techniques. Radiology. 2012;265:340–56.

    Article  PubMed Central  PubMed  Google Scholar 

  10. Subhawong TK, Jacobs MA, Fayad LM. Insights into quantitative diffusion-weighted MRI for musculoskeletal tumor imaging. AJR Am J Roentgenol. 2014;203:560–72.

    Article  PubMed  Google Scholar 

  11. Bley TA, Wieben O, Uhl M. Diffusion-weighted MR imaging in musculoskeletal radiology: applications in trauma, tumors, and inflammation. Magn Reson Imaging Clin N Am. 2009;17:263–75.

    Article  PubMed  Google Scholar 

  12. Colagrande S, Carbone SF, Carusi LM, Cova M, Villari N. Magnetic resonance diffusion-weighted imaging: extraneurological applications. Radiol Med. 2006;111:392–419.

    Article  PubMed  CAS  Google Scholar 

  13. Del Grande F, Subhawong T, Flammang A, Fayad LM. Chemical shift imaging at 3 Tesla: effect of echo time on assessing bone marrow abnormalities. Skeletal Radiol. 2014;43:1139–47.

    Article  PubMed  Google Scholar 

  14. Herneth AM, Guccione S, Bednarski M. Apparent diffusion coefficient: a quantitative parameter for in vivo tumor characterization. Eur J Radiol. 2003;45:208–13.

    Article  PubMed  Google Scholar 

  15. van Rijswijk CS, Kunz P, Hogendoorn PC, Taminiau AH, Doornbos J, Bloem JL. Diffusion-weighted MRI in the characterization of soft-tissue tumors. J Magn Reson Imaging. 2002;15:302–7.

    Article  PubMed  Google Scholar 

  16. Nagata S, Nishimura H, Uchida M, et al. Diffusion-weighted imaging of soft tissue tumors: usefulness of the apparent diffusion coefficient for differential diagnosis. Radiat Med. 2008;26:287–95.

    Article  PubMed  Google Scholar 

  17. Costa FM, Canella C, Gasparetto E. Advanced magnetic resonance imaging techniques in the evaluation of musculoskeletal tumors. Radiol Clin North Am. 2011;49:1325–58. vii–viii.

    Article  PubMed  Google Scholar 

  18. Koyama H, Ohno Y, Seki S, et al. Value of diffusion-weighted MR imaging using various parameters for assessment and characterization of solitary pulmonary nodules. Eur J Radiol. 2015;84:509–15.

    Article  PubMed  Google Scholar 

  19. Noij DP, Pouwels PJ, Ljumanovic R, et al. Predictive value of diffusion-weighted imaging without and with including contrast-enhanced magnetic resonance imaging in image analysis of head and neck squamous cell carcinoma. Eur J Radiol. 2015;84:108–16.

    Article  PubMed  Google Scholar 

  20. Kang TW, Kim SH, Jang KM, et al. Gastrointestinal stromal tumours: correlation of modified NIH risk stratification with diffusion-weighted MR imaging as an imaging biomarker. Eur J Radiol. 2015;84:33–40.

    Article  PubMed  Google Scholar 

  21. Wu LM, Chen XX, Li YL, et al. On the utility of quantitative diffusion-weighted MR imaging as a tool in differentiation between malignant and benign thyroid nodules. Acad Radiol. 2014;21:355–63.

    Article  PubMed  Google Scholar 

  22. Wu LM, Xu JR, Ye YQ, Lu Q, Hu JN. The clinical value of diffusion-weighted imaging in combination with T2-weighted imaging in diagnosing prostate carcinoma: a systematic review and meta-analysis. AJR Am J Roentgenol. 2012;199:103–10.

    Article  PubMed  Google Scholar 

  23. Oka K, Yakushiji T, Sato H, et al. Usefulness of diffusion-weighted imaging for differentiating between desmoid tumors and malignant soft tissue tumors. J Magn Reson Imaging. 2011;33:189–93.

    Article  PubMed  Google Scholar 

  24. Oka K, Yakushiji T, Sato H, et al. Ability of diffusion-weighted imaging for the differential diagnosis between chronic expanding hematomas and malignant soft tissue tumors. J Magn Reson Imaging. 2008;28:1195–200.

    Article  PubMed  Google Scholar 

  25. Einarsdottir H, Karlsson M, Wejde J, Bauer HC. Diffusion-weighted MRI of soft tissue tumours. Eur Radiol. 2004;14:959–63.

    Article  PubMed  Google Scholar 

  26. Chen CK, Wu HT, Chiou HJ, et al. Differentiating benign and malignant soft tissue masses by magnetic resonance imaging: role of tissue component analysis. J Chin Med Assoc JCMA. 2009;72:194–201.

    Article  Google Scholar 

  27. Maeda M, Matsumine A, Kato H, et al. Soft-tissue tumors evaluated by line-scan diffusion-weighted imaging: influence of myxoid matrix on the apparent diffusion coefficient. J Magn Reson Imaging. 2007;25:1199–204.

    Article  PubMed  Google Scholar 

  28. Lang P, Wendland MF, Saeed M, et al. Osteogenic sarcoma: noninvasive in vivo assessment of tumor necrosis with diffusion-weighted MR imaging. Radiology. 1998;206:227–35.

    Article  PubMed  CAS  Google Scholar 

  29. Schmidt H, Gatidis S, Schwenzer NF, Martirosian P. Impact of measurement parameters on apparent diffusion coefficient quantification in diffusion-weighted-magnetic resonance imaging. Investig Radiol. 2015;50:6–56.

    Article  Google Scholar 

  30. Chung BM, Park SB, Lee JB, Park HJ, Kim YS, Oh YJ. Magnetic resonance imaging features of ovarian fibroma, fibrothecoma, and thecoma. Abdom Imaging. 2015;40:1263–72.

    Article  PubMed  Google Scholar 

  31. Padhani AR, Liu G, Koh DM, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11:102–25.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  32. Bonarelli C, Teixeira PAG, Hossu G, Meyer JB, Bailiang C, Gay F, et al. Impact of ROI positioning and lesion morphology on ADC value analysis for the differentiation between benign and malignant non-fatty soft tissue lesions. AJR Am J Roentgenol. 2015;205:W106–13.

    Article  PubMed  Google Scholar 

  33. Hirano M, Satake H, Ishigaki S, Ikeda M, Kawai H, Naganawa S. Diffusion-weighted imaging of breast masses: comparison of diagnostic performance using various apparent diffusion coefficient parameters. AJR Am J Roentgenol. 2012;198:717–22.

    Article  PubMed  Google Scholar 

  34. Obuchowski NA. Receiver operating characteristic curves and their use in radiology. Radiology. 2003;229:3–8.

    Article  PubMed  Google Scholar 

  35. Geith T, Schmidt G, Biffar A, et al. Comparison of qualitative and quantitative evaluation of diffusion-weighted MRI and chemical-shift imaging in the differentiation of benign and malignant vertebral body fractures. AJR Am J Roentgenol. 2012;199:1083–92.

    Article  PubMed  Google Scholar 

  36. Neubauer H, Evangelista L, Hassold N, et al. Diffusion-weighted MRI for detection and differentiation of musculoskeletal tumorous and tumor-like lesions in pediatric patients. World J Pediatr WJP. 2012;8:342–9.

    Article  Google Scholar 

  37. Subhawong TK, Jacobs MA, Fayad LM. Diffusion-weighted MR imaging for characterizing musculoskeletal lesions. Radiographics. 2014;34:1163–77.

    Article  PubMed Central  PubMed  Google Scholar 

  38. Sundaram M, McGuire MH, Schajowicz F. Soft-tissue masses: histologic basis for decreased signal (short T2) on T2-weighted MR images. AJR Am J Roentgenol. 1987;148:1247–50.

    Article  PubMed  CAS  Google Scholar 

  39. Petscavage-Thomas JM, Walker EA, Logie CI, et al. Soft-tissue myxomatous lesions: review of salient imaging features with pathologic comparison. Radiographics. 2014;34:964–80.

    Article  PubMed  Google Scholar 

  40. Baheti AD, Tirumani SH, Rosenthal MF, et al. Myxoid soft-tissue neoplasms: comprehensive update of the taxonomy and MRI features. AJR Am J Roentgenol. 2015;204:374–85.

    Article  PubMed  Google Scholar 

  41. Bogner W, Gruber S, Pinker K, et al. Diffusion-weighted MR for differentiation of breast lesions at 3.0 T: how does selection of diffusion protocols affect diagnosis? Radiology. 2009;253:341–51.

    Article  PubMed  Google Scholar 

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Acknowledgements

This work was supported by the French Society of Radiology (SFR) through a research grant.

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Authors and Affiliations

  1. Service d’Imagerie Guilloz, CHU Nancy, 29 Avenue du Maréchal de Lattre de Tassigny, Nancy, 54000, France

    Pedro Augusto Gondim Teixeira, Frederique Gay & Alain Blum

  2. Université de Lorraine, IADI, UMR S 947, Tour Drouet Rue du Morvan, 54511, Vandoeuvre-lès-Nancy, France

    Pedro Augusto Gondim Teixeira, Bailiang Chen & Jacques Felblinger

  3. University Versailles St-Quentin, 78035, Versailles, France

    Marie Zins

  4. Inserm, Centre for research in Epidemiology and Population Health (CESP), U1018, 94807, Villejuif, France

    Marie Zins

  5. Service de Chirurgie Traumatologique et Orthopédique, Centre Chirurgical Emile Gallé, 54000, Nancy, France

    François Sirveaux

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  1. Pedro Augusto Gondim Teixeira
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Correspondence to Pedro Augusto Gondim Teixeira.

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Gondim Teixeira, P.A., Gay, F., Chen, B. et al. Diffusion-weighted magnetic resonance imaging for the initial characterization of non-fatty soft tissue tumors: correlation between T2 signal intensity and ADC values. Skeletal Radiol 45, 263–271 (2016). https://doi.org/10.1007/s00256-015-2302-6

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  • DOI: https://doi.org/10.1007/s00256-015-2302-6

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