Skip to main content

Advertisement

SpringerLink
Log in

Comparison of mono-exponential, bi-exponential, kurtosis, and fractional-order calculus models of diffusion-weighted imaging in characterizing prostate lesions in transition zone

  • Pelvis
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To compare various models of diffusion-weighted imaging including mono-exponential, bi-exponential, diffusion kurtosis (DK) and fractional-order calculus (FROC) models in diagnosing prostate cancer (PCa) in transition zone (TZ) and distinguish the high-grade PCa [Gleason score (GS) ≥ 7] lesions from the total of low-grade PCa (GS ≤ 6) lesions and benign prostatic hyperplasia (BPH) in TZ.

Methods

80 Patients with 103 lesions were included in this study. Nine metrics [including apparent diffusion coefficient (ADC) derived from mono-exponential model, slow diffusion coefficient (Ds), fast diffusion coefficient (Df),, and f (the fraction of fast diffusion) from bi-exponential model; mean diffusivity (MD) and mean kurtosis (MK) from DK model; diffusion coefficient (D), fractional-order derivative in space (β), and spatial metric (μ) from FROC model] were calculated. Comparisons between BPH and PCa lesions as well as between clinically significant PCa (CsPCa) (GS ≥ 7, n = 31) and clinically insignificant lesions (Cins) (GS ≤ 6 and BPH, n = 72) of these metrics were conducted. Mann–Whitney U-test and receiver operating characteristic (ROC) analysis were used for statistical evaluations.

Results

The areas under the ROC curve (AUC) values of β derived from FROC model were 0.778 and 0.853 in differentiating PCa from BPH and in differentiating CS (GS ≥ 7) from Cins (GS ≤ 6 and BPH), both were the highest compared to other metrics. The AUC value of β was significantly higher than that of ADC (P = 0.009) in differentiating CS from Cins, while the differentiation between BPH and PCa did not reach the statistical significance when comparing with ADC (P = 0.089).

Conclusion

Although no significant difference was found in distinguishing PCa from BPH, the metric β derived from FROC model was superior to other diffusion metrics in differentiation between CS and Cins in TZ.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Culp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent Global Patterns in Prostate Cancer Incidence and Mortality Rates. Eur Urol 2020;77:38–52

    Article  Google Scholar 

  2. Wilt TJ, MacDonald R, Rutks I, Shamliyan TA, Taylor BC, Kane RL. Systematic review: comparative effectiveness and harms of treatments for clinically localized prostate cancer. Ann Intern Med 2008;148:435–448

    Article  Google Scholar 

  3. Bittencourt LK, Barentsz JO, de Miranda LC, Gasparetto EL. Prostate MRI: diffusion-weighted imaging at 1.5T correlates better with prostatectomy Gleason Grades than TRUS-guided biopsies in peripheral zone tumours. Eur Radiol 2012;22:468–475

  4. Tamada T, Prabhu V, Li J, Babb JS, Taneja SS, Rosenkrantz AB. Prostate Cancer: Diffusion-weighted MR Imaging for Detection and Assessment of Aggressiveness-Comparison between Conventional and Kurtosis Models. Radiology 2017;284:100–108

    Article  Google Scholar 

  5. deSouza NM, Riches SF, Vanas NJ, Morgan VA, Ashley SA, Fisher C, et al. Diffusion-weighted magnetic resonance imaging: a potential non-invasive marker of tumour aggressiveness in localized prostate cancer. Clin Radiol 2008;63:774–782

    Article  CAS  Google Scholar 

  6. Turkbey B, Shah VP, Pang Y, Bernardo M, Xu S, Kruecker J, et al. Is apparent diffusion coefficient associated with clinical risk scores for prostate cancers that are visible on 3-T MR images? Radiology 2011;258:488–495

    Article  Google Scholar 

  7. Barbieri S, Bronnimann M, Boxler S, Vermathen P, Thoeny HC. Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI. Eur Radiol 2017;27:1547–1555

    Article  Google Scholar 

  8. Itou Y, Nakanishi K, Narumi Y, Nishizawa Y, Tsukuma H. Clinical utility of apparent diffusion coefficient (ADC) values in patients with prostate cancer: can ADC values contribute to assess the aggressiveness of prostate cancer? J Magn Reson Imaging 2011;33:167–172

    Article  Google Scholar 

  9. Woo S, Kim SY, Cho JY, Kim SH. Preoperative Evaluation of Prostate Cancer Aggressiveness: Using ADC and ADC Ratio in Determining Gleason Score. AJR Am J Roentgenol 2016;207:114–120

    Article  Google Scholar 

  10. Bihan, D. Le, Robert Turner, and James R. Macfall. “Effects Of Intravoxel Incoherent Motions (Ivim) In Steady-State Free Precession (Ssfp) Imaging - Application To Molecular-Diffusion Imaging.” Magnetic Resonance in Medicine 1989;10: 324–337

  11. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005;53:1432–1440

    Article  Google Scholar 

  12. Zhou XJ, Gao Q, Abdullah O, Magin RL. Studies of anomalous diffusion in the human brain using fractional order calculus. Magn Reson Med 2010; 63: 562–569.

    Article  Google Scholar 

  13. Noda Y, Goshima S, Fujimoto K, Akamine Y, Kajita K, Kawai N, et al. Comparison of the Diagnostic Value of Mono-exponential, Bi-exponential, and Stretched Exponential Signal Models in Diffusion-weighted MR Imaging for Differentiating Benign and Malignant Hepatic Lesions. Magn Reson Med 2020.

  14. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988;168:497–505

    Article  Google Scholar 

  15. Van Cauter S, Veraart J, Sijbers J, Peeters RR, Himmelreich U, De Keyzer F, et al. Gliomas: diffusion kurtosis MR imaging in grading. Radiology 2012;263:492–501

    Article  Google Scholar 

  16. Liu X, Zhou L, Peng W, Wang C, Wang H. Differentiation of central gland prostate cancer from benign prostatic hyperplasia using mono-exponential and bi-exponential diffusion-weighted imaging. Magn Reson Imaging 2013;31:1318–1324

    Article  Google Scholar 

  17. Ding K, Yao Y, Gao Y, Lu X, Chen H, Tang Q, et al. Diagnostic evaluation of diffusion kurtosis imaging for prostate cancer: Detection in a biopsy population. Eur J Radiol 2019;118:138–146

    Article  Google Scholar 

  18. Si Y, Liu RB. Diagnostic Performance of Mono-exponential DWI Versus Diffusion Kurtosis Imaging in Prostate Cancer: A Systematic Review and Meta-Analysis. AJR Am J Roentgenol 2018;211:358–368

    Article  Google Scholar 

  19. Zhang P, Min X, Wang L, Feng Z, Ke Z, You H, et al. Bi-exponential versus mono-exponential diffusion-weighted imaging for evaluating prostate cancer aggressiveness after radical prostatectomy: a whole-tumor histogram analysis. Acta Radiol 2019;60:1566–1575

    Article  Google Scholar 

  20. Oto A, Kayhan A, Jiang Y, Tretiakova M, Yang C, Antic T, et al. Prostate cancer: differentiation of central gland cancer from benign prostatic hyperplasia by using diffusion-weighted and dynamic contrast-enhanced MR imaging. Radiology 2010;257:715–723

    Article  Google Scholar 

  21. Noworolski SM, Vigneron DB, Chen AP, Kurhanewicz J. Dynamic contrast-enhanced MRI and MR diffusion imaging to distinguish between glandular and stromal prostatic tissues. Magn Reson Imaging 2008;26:1071–1080

    Article  Google Scholar 

  22. Rosenkrantz AB, Kim S, Campbell N, Gaing B, Deng FM, Taneja SS. Transition zone prostate cancer: revisiting the role of multiparametric MRI at 3 T. AJR Am J Roentgenol 2015;204:W266–272.

    Article  Google Scholar 

  23. Pierorazio PM, Walsh PC, Partin AW, Epstein JI. Prognostic Gleason grade grouping: data based on the modified Gleason scoring system. BJU Int 2013;111:753–760

    Article  Google Scholar 

  24. Sorensen AG, Buonanno FS, Gonzalez RG, Schwamm LH, Lev MH, Huang-Hellinger FR, Reese TG, Weisskoff RM, Davis TL, Suwanwela N, Can U, Moreira JA, Copen WA, Look RB, Finklestein SP, Rosen BR, Koroshetz WJ. Hyperacute stroke: evaluation with combined multisection diffusion-weighted and hemodynamically weighted echo-planar MR imaging. Radiology 1996; 199:391–401

    Article  CAS  Google Scholar 

  25. Tang L, Sui Y, Zhong Z, Damen FC, Li J, Shen L, et al. Non-Gaussian diffusion imaging with a fractional order calculus model to predict response of gastrointestinal stromal tumor to second-line sunitinib therapy. Magn Reson Med 2018;79:1399–1406

    Article  CAS  Google Scholar 

  26. Tamura C, Shinmoto H, Soga S, Okamura T, Sato H, Okuaki T, et al. Diffusion Kurtosis Imaging Study of Prostate Cancer: Preliminary Findings. J Magn Reson Imaging 2014;40: 723–729

    Article  Google Scholar 

  27. Rosenkrantz AB, Sigmund EE, Johnson G, Babb JS, Mussi TC, Melamed J, et al. Prostate cancer: feasibility and preliminary experience of a diffusional kurtosis model for detection and assessment of aggressiveness of peripheral zone cancer. Radiology 2012;264:126–135

    Article  Google Scholar 

  28. Yang DM, Kim HC, Kim SW, Jahng GH, Won KY, Lim SJ, et al. Prostate cancer: correlation of intravoxel incoherent motion MR parameters with Gleason score. Clinical Imaging 2016;40: 445–450

    Article  Google Scholar 

  29. Zhang M, Milot L, Khalvati F, Sugar L, Downes M, Baig SM, et al. Value of Increasing Biopsy Cores per Target with Cognitive MRI-targeted Transrectal US Prostate Biopsy. Radiology 2019;291:83–89

    Article  Google Scholar 

  30. Sui Y, Wang H, Liu G, Damen FW, Wanamaker C, Li Y, et al. Differentiation of Low- and High-Grade Pediatric Brain Tumors with High b-Value Diffusion-weighted MR Imaging and a Fractional Order Calculus Model. Radiology 2015;277:489–496

    Article  Google Scholar 

  31. Sui Y, Xiong Y, Jiang J, Karaman MM, Xie KL, Zhu W, et al. Differentiation of Low- and High-Grade Gliomas Using High b-Value Diffusion Imaging with a Non-Gaussian Diffusion Model. AJNR Am J Neuroradiol 2016;37:1643–1649

    Article  CAS  Google Scholar 

  32. Gao Q, Srinivasan G, Magin RL, Zhou XJ. Anomalous diffusion measured by a twice-refocused spin echo pulse sequence: analysis using fractional order calculus. J Magn Reson Imaging 2011;33:1177–1183

    Article  Google Scholar 

  33. Ingo C, Magin RL, Colon-Perez L, Triplett W, Mareci TH. On random walks and entropy in diffusion-weighted magnetic resonance imaging studies of neural tissue. Magn Reson Med 2014;71:617–627

    Article  Google Scholar 

  34. Pang Y, Turkbey B, Bernardo M, Kruecker J, Kadoury S, Merino MJ, et al. Intravoxel incoherent motion MR imaging for prostate cancer: an evaluation of perfusion fraction and diffusion coefficient derived from different b-value combinations. Magn Reson Med 2013;69:553–562

    Article  Google Scholar 

  35. Alonzi R, Padhani AR, Allen C. Dynamic contrast enhanced MRI in prostate cancer. Eur J Radiol 2007;63:335–350

    Article  Google Scholar 

  36. Patel J, Sigmund EE, Rusinek H, Oei M, Babb JS, Taouli B. Diagnosis of cirrhosis with intravoxel incoherent motion diffusion MRI and dynamic contrast-enhanced MRI alone and in combination: preliminary experience. J Magn Reson Imaging 2010;31:589–600

    Article  Google Scholar 

  37. Pesapane F, Patella F, Fumarola EM, Panella S, Ierardi AM, Pompili GG, et al. Intravoxel Incoherent Motion (IVIM) Diffusion Weighted Imaging (DWI) in the Periferic Prostate Cancer Detection and Stratification. Med Oncol 2017;34:35

  38. Dennis LK, Lynch CF, Torner JC. Epidemiologic association between prostatitis and prostate cancer. Urology 2002;60:78–83

    Article  Google Scholar 

  39. Zhang YD, Wang Q, Wu CJ, Wang XN, Zhang J, Liu H, et al. The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the gleason grade of prostate cancer. Eur Radiol 2015;25:994–1004

    Article  Google Scholar 

  40. Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010;23:698–710

    Article  Google Scholar 

  41. Wang F, Chen HG, Zhang RY, Jin D, Xu SS, Wu GY, et al. Diffusion kurtosis imaging to assess correlations with clinicopathologic factors for bladder cancer: a comparison between the multi-b value method and the tensor method. Eur Radiol 2019;29:4447–4455

    Article  Google Scholar 

  42. Mehralivand S, Bednarova S, Shih JH, Mertan FV, Gaur S, Merino MJ, et al. Prospective Evaluation of PI-RADS Version 2 Using the International Society of Urological Pathology Prostate Cancer Grade Group System. J Urol 2017;198:583–590

    Article  Google Scholar 

Download references

Funding

This study has received funding from Shanghai Shenkang Project (16CR3024A), Shanghai Jiaotong University Medical-Engineering Cross Fund (YG2017MS48) and Shanghai Science and Technology Committee of Shanghai Municipality (17441902700/18DZ1930104).

Author information

Author notes

  1. Guiqin Liu and Yang Lu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, 160 Pujian Road, Shanghai, 200127, China

    Guiqin Liu, Yang Lu, Jianrong Xu & Guangyu Wu

  2. Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, 3663 N. Zhongshan Road, Shanghai, 200062, China

    Dongmei Wu

  3. United Imaging Healthcare, Shanghai, China

    Yongming Dai, Ke Xue & Yangyang Yi

Authors
  1. Guiqin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongming Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ke Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yangyang Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jianrong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dongmei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Guangyu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Dongmei Wu or Guangyu Wu.

Ethics declarations

Conflict of interest

We certify that each author has made substantive intellectual contributions to the paper and has no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

(TIF 1388 kb)

(TIF 1526 kb)

(DOCX 16 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, G., Lu, Y., Dai, Y. et al. Comparison of mono-exponential, bi-exponential, kurtosis, and fractional-order calculus models of diffusion-weighted imaging in characterizing prostate lesions in transition zone. Abdom Radiol 46, 2740–2750 (2021). https://doi.org/10.1007/s00261-020-02903-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-020-02903-x

Keywords

Search

Navigation