European Radiology

, Volume 27, Issue 7, pp 3050–3059 | Cite as

T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results

  • Gabriel Nketiah
  • Mattijs Elschot
  • Eugene Kim
  • Jose R. Teruel
  • Tom W. Scheenen
  • Tone F. Bathen
  • Kirsten M. Selnæs
Magnetic Resonance



To evaluate the diagnostic relevance of T2-weighted (T2W) MRI-derived textural features relative to quantitative physiological parameters derived from diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI in Gleason score (GS) 3+4 and 4+3 prostate cancers.

Materials and Methods

3T multiparametric-MRI was performed on 23 prostate cancer patients prior to prostatectomy. Textural features [angular second moment (ASM), contrast, correlation, entropy], apparent diffusion coefficient (ADC), and DCE pharmacokinetic parameters (Ktrans and Ve) were calculated from index tumours delineated on the T2W, DW, and DCE images, respectively. The association between the textural features and prostatectomy GS and the MRI-derived parameters, and the utility of the parameters in differentiating between GS 3+4 and 4+3 prostate cancers were assessed statistically.


ASM and entropy correlated significantly (p < 0.05) with both GS and median ADC. Contrast correlated moderately with median ADC. The textural features correlated insignificantly with Ktrans and Ve. GS 4+3 cancers had significantly lower ASM and higher entropy than 3+4 cancers, but insignificant differences in median ADC, Ktrans, and Ve. The combined texture-MRI parameters yielded higher classification accuracy (91%) than the individual parameter sets.


T2W MRI-derived textural features could serve as potential diagnostic markers, sensitive to the pathological differences in prostate cancers.

Key Points

T2W MRI-derived textural features correlate significantly with Gleason score and ADC.

T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers.

T2W image textural features could augment tumour characterization.


Magnetic resonance imaging Apparent diffusion coefficient DCE pharmacokinetic parameters Texture analysis Gleason grading 



The research on which this study was based is funded by The Norwegian Cancer Society.

Gleason grading of the histopathology specimen was performed by Trond Viset, a senior pathologist at St. Olavs University Hospital, Trondheim. The scientific guarantor of this publication is Tone F. Bathen ( 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. No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained from St. Olavs University Hospital, Trondheim, and the Regional Committee for Medical and Health Research Ethics, Central Norway. Written informed consent was obtained from all subjects (patients) in this study. Approval from the institutional animal care committee was not required because this study is on humans. The study subjects or cohorts have not been previously reported. Methodology: Retrospective, diagnostic or prognostic study, performed at one institution.


  1. 1.
    Scheenen TW, Rosenkrantz AB, Haider MA, Futterer JJ (2015) Multiparametric Magnetic Resonance Imaging in Prostate Cancer Management: Current Status and Future Perspectives. Invest Radiol 50:594–600CrossRefPubMedGoogle Scholar
  2. 2.
    Vos EK, Kobus T, Litjens GJ et al (2015) Multiparametric Magnetic Resonance Imaging for Discriminating Low-Grade From High-Grade Prostate Cancer. Invest Radiol 50:490–497CrossRefPubMedGoogle Scholar
  3. 3.
    Kitajima K, Kaji Y, Fukabori Y, Yoshida K, Suganuma N, Sugimura K (2010) Prostate cancer detection with 3 T MRI: comparison of diffusion-weighted imaging and dynamic contrast-enhanced MRI in combination with T2-weighted imaging. J Magn Reson Imaging 31:625–631CrossRefPubMedGoogle Scholar
  4. 4.
    Delongchamps NB, Rouanne M, Flam T et al (2011) Multiparametric magnetic resonance imaging for the detection and localization of prostate cancer: combination of T2-weighted, dynamic contrast-enhanced and diffusion-weighted imaging. BJU Int 107:1411–1418CrossRefPubMedGoogle Scholar
  5. 5.
    Koh DM, Collins DJ (2007) Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 188:1622–1635CrossRefPubMedGoogle Scholar
  6. 6.
    Malayeri AA, El Khouli RH, Zaheer A et al (2011) Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up. Radiographics 31:1773–1791CrossRefPubMedGoogle Scholar
  7. 7.
    Brix G, Griebel J, Kiessling F, Wenz F (2010) Tracer kinetic modelling of tumour angiogenesis based on dynamic contrast-enhanced CT and MRI measurements. Eur J Nucl Med Mol Imaging 37 Suppl 1:S30–S51CrossRefPubMedGoogle Scholar
  8. 8.
    Parker GJM, Buckley DL (2005) Tracer Kinetic Modelling for T1-Weighted DCE-MRI. In: Jackson A, Buckley DL, Parker GJM (eds) Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Oncology. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 81–92CrossRefGoogle Scholar
  9. 9.
    Tofts PS, Brix G, Buckley DL et al (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10:223–232CrossRefPubMedGoogle Scholar
  10. 10.
    Vos EK, Litjens GJ, Kobus T et al (2013) Assessment of prostate cancer aggressiveness using dynamic contrast-enhanced magnetic resonance imaging at 3 T. Eur Urol 64:448–455CrossRefPubMedGoogle Scholar
  11. 11.
    Gleason DF, Mellinger GT (1974) Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging. J Urol 111:58–64PubMedGoogle Scholar
  12. 12.
    Epstein JI, Allsbrook WC Jr, Amin MB, Egevad LL (2005) The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. Am J Surg Pathol 29:1228–1242CrossRefPubMedGoogle Scholar
  13. 13.
    Stav K, Judith S, Merald H, Leibovici D, Lindner A, Zisman A (2007) Does prostate biopsy Gleason score accurately express the biologic features of prostate cancer? Urol Oncol 25:383–386CrossRefPubMedGoogle Scholar
  14. 14.
    Divrik RT, Eroglu A, Sahin A, Zorlu F, Ozen H (2007) Increasing the number of biopsies increases the concordance of Gleason scores of needle biopsies and prostatectomy specimens. Urol Oncol 25:376–382CrossRefPubMedGoogle Scholar
  15. 15.
    Rosenkrantz AB, Triolo MJ, Melamed J, Rusinek H, Taneja SS, Deng FM (2015) Whole-lesion apparent diffusion coefficient metrics as a marker of percentage Gleason 4 component within Gleason 7 prostate cancer at radical prostatectomy. J Magn Reson Imaging 41:708–714CrossRefPubMedGoogle Scholar
  16. 16.
    Verma S, Rajesh A, Morales H et al (2011) Assessment of Aggressiveness of Prostate Cancer: Correlation of Apparent Diffusion Coefficient With Histologic Grade After Radical Prostatectomy. Am J Roentgenol 196:374–381CrossRefGoogle Scholar
  17. 17.
    Chan TY, Partin AW, Walsh PC, Epstein JI (2000) Prognostic significance of Gleason score 3+4 versus Gleason score 4+3 tumor at radical prostatectomy. Urology 56:823–827CrossRefPubMedGoogle Scholar
  18. 18.
    Khoddami SM, Shariat SF, Lotan Y et al (2004) Predictive value of primary Gleason pattern 4 in patients with Gleason score 7 tumours treated with radical prostatectomy. BJU Int 94:42–46CrossRefPubMedGoogle Scholar
  19. 19.
    Stark JR, Perner S, Stampfer MJ et al (2009) Gleason score and lethal prostate cancer: does 3 + 4 = 4 + 3? J Clin Oncol 27:3459–3464CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Epstein JI, Zelefsky MJ, Sjoberg DD et al (2016) A Contemporary Prostate Cancer Grading System: A Validated Alternative to the Gleason Score. Eur Urol 69:428–435CrossRefPubMedGoogle Scholar
  21. 21.
    Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069CrossRefPubMedGoogle Scholar
  22. 22.
    Freeborough PA, Fox NC (1998) MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease. IEEE Trans Med Imaging 17:475–479CrossRefPubMedGoogle Scholar
  23. 23.
    Ganeshan B, Burnand K, Young R, Chatwin C, Miles K (2011) Dynamic contrast-enhanced texture analysis of the liver: initial assessment in colorectal cancer. Invest Radiol 46:160–168CrossRefPubMedGoogle Scholar
  24. 24.
    Teruel JR, Heldahl MG, Goa PE et al (2014) Dynamic contrast-enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. NMR Biomed 27:887–896CrossRefPubMedGoogle Scholar
  25. 25.
    Viswanath SE, Bloch NB, Chappelow JC et al (2012) Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 Tesla endorectal, in vivo T2-weighted MR imagery. J Magn Reson Imaging 36:213–224CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Wibmer A, Hricak H, Gondo T et al (2015) Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol 25:2840–2850CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Nketiah G, Savio S, Dastidar P, Nikander R, Eskola H, Sievanen H (2015) Detection of exercise load-associated differences in hip muscles by texture analysis. Scand J Med Sci Sports 25:428–434CrossRefPubMedGoogle Scholar
  28. 28.
    Boesen L, Chabanova E, Logager V, Balslev I, Thomsen HS (2015) Apparent diffusion coefficient ratio correlates significantly with prostate cancer gleason score at final pathology. J Magn Reson Imaging 42:446–453CrossRefPubMedGoogle Scholar
  29. 29.
    Oto A, Yang C, Kayhan A et al (2011) Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis. AJR Am J Roentgenol 197:1382–1390CrossRefPubMedGoogle Scholar
  30. 30.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural Features for Image Classification. IEEE Trans Syst Man Cybernet SMC-3:610–621CrossRefGoogle Scholar
  31. 31.
    Klein S, Staring M, Murphy K, Viergever MA, Pluim JP (2010) elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29:196–205CrossRefPubMedGoogle Scholar
  32. 32.
    Cohen MS, DuBois RM, Zeineh MM (2000) Rapid and effective correction of RF inhomogeneity for high field magnetic resonance imaging. Hum Brain Mapp 10:204–211CrossRefPubMedGoogle Scholar
  33. 33.
    Fehr D, Veeraraghavan H, Wibmer A et al (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci U S A 112:E6265–E6273CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Fennessy FM, Fedorov A, Gupta SN, Schmidt EJ, Tempany CM, Mulkern RV (2012) Practical considerations in T1 mapping of prostate for dynamic contrast enhancement pharmacokinetic analyses. Magn Reson Imaging 30:1224–1233CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Parker GJ, Roberts C, Macdonald A et al (2006) Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med 56:993–1000CrossRefPubMedGoogle Scholar
  36. 36.
    Nyul LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. IEEE Trans Med Imaging 19:143–150CrossRefPubMedGoogle Scholar
  37. 37.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMedGoogle Scholar
  38. 38.
    Benjamini Y, Hochberg Y (2000) On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics. J Educ Behav Stat 25:60–83CrossRefGoogle Scholar
  39. 39.
    Langenberger H, Shimizu Y, Windischberger C et al (2003) Bone homogeneity factor: an advanced tool for the assessment of osteoporotic bone structure in high-resolution magnetic resonance images. Invest Radiol 38:467–472PubMedGoogle Scholar
  40. 40.
    Weinreb JC, Barentsz JO, Choyke PL et al (2016) PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. Eur Urol 69:16–40CrossRefPubMedGoogle Scholar
  41. 41.
    Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67:786–804CrossRefGoogle Scholar
  42. 42.
    Almuntashri A, Agaian S, Thompson I, Rabah D, Al-Abdin OZ, Nicolas M (2011) Gleason grade-based automatic classification of prostate cancer pathological imagesSystems, Man, and Cybernetics (SMC), 2011 I.E. International Conference on, pp 2696-2701Google Scholar
  43. 43.
    Jafari-Khouzani K, Soltanian-Zadeh H (2003) Multiwavelet grading of pathological images of prostate. IEEE Trans Biomed Eng 50:697–704CrossRefPubMedGoogle Scholar
  44. 44.
    Diamond J, Anderson NH, Bartels PH, Montironi R, Hamilton PW (2004) The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia. Hum Pathol 35:1121–1131CrossRefPubMedGoogle Scholar
  45. 45.
    Vignati A, Mazzetti S, Giannini V et al (2015) Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness. Phys Med Biol 60:2685–2701CrossRefPubMedGoogle Scholar
  46. 46.
    Hegde JV, Mulkern RV, Panych LP et al (2013) Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer. J Magn Reson Imaging 37:1035–1054CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Buckley DL, Roberts C, Parker GJ, Logue JP, Hutchinson CE (2004) Prostate cancer: evaluation of vascular characteristics with dynamic contrast-enhanced T1-weighted MR imaging--initial experience. Radiology 233:709–715CrossRefPubMedGoogle Scholar
  48. 48.
    Tamada T, Sone T, Jo Y, Yamamoto A, Ito K (2014) Diffusion-weighted MRI and its role in prostate cancer. NMR Biomed 27:25–38CrossRefPubMedGoogle Scholar
  49. 49.
    Nagarajan R, Margolis D, Raman S et al (2012) Correlation of Gleason scores with diffusion-weighted imaging findings of prostate cancer. Adv Urol 2012:374805CrossRefPubMedGoogle Scholar
  50. 50.
    Georgiadis P, Cavouras D, Kalatzis I et al (2009) Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods. Magn Reson Imaging 27:120–130CrossRefPubMedGoogle Scholar
  51. 51.
    Mahmoud-Ghoneim D, Toussaint G, Constans JM, de Certaines JD (2003) Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn Reson Imaging 21:983–987CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Gabriel Nketiah
    • 1
  • Mattijs Elschot
    • 1
  • Eugene Kim
    • 1
  • Jose R. Teruel
    • 1
  • Tom W. Scheenen
    • 2
  • Tone F. Bathen
    • 1
    • 3
  • Kirsten M. Selnæs
    • 1
    • 3
  1. 1.Department of Circulation and Medical Imaging, Faculty of MedicineNTNU, Norwegian University of Science and TechnologyTrondheimNorway
  2. 2.Department of Radiology and Nuclear MedicineRadboud University Medical CenterNijmegenThe Netherlands
  3. 3.St. Olavs Hospital, Trondheim University HospitalTrondheimNorway

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