European Radiology

, Volume 27, Issue 5, pp 2146–2152 | Cite as

Non-invasive quantification of tumour heterogeneity in water diffusivity to differentiate malignant from benign tissues of urinary bladder: a phase I study

  • Huyen T. Nguyen
  • Zarine K. Shah
  • Amir Mortazavi
  • Kamal S. Pohar
  • Lai Wei
  • Guang Jia
  • Debra L. Zynger
  • Michael V. Knopp
Magnetic Resonance



To quantify the heterogeneity of the tumour apparent diffusion coefficient (ADC) using voxel-based analysis to differentiate malignancy from benign wall thickening of the urinary bladder.


Nineteen patients with histopathological findings of their cystectomy specimen were included. A data set of voxel-based ADC values was acquired for each patient’s lesion. Histogram analysis was performed on each data set to calculate uniformity (U) and entropy (E). The k-means clustering of the voxel-wised ADC data set was implemented to measure mean intra-cluster distance (MICD) and largest inter-cluster distance (LICD). Subsequently, U, E, MICD, and LICD for malignant tumours were compared with those for benign lesions using a two-sample t-test.


Eleven patients had pathological confirmation of malignancy and eight with benign wall thickening. Histogram analysis showed that malignant tumours had a significantly higher degree of ADC heterogeneity with lower U (P = 0.016) and higher E (P = 0.005) than benign lesions. In agreement with these findings, k-means clustering of voxel-wise ADC indicated that bladder malignancy presented with significantly higher MICD (P < 0.001) and higher LICD (P = 0.002) than benign wall thickening.


The quantitative assessment of tumour diffusion heterogeneity using voxel-based ADC analysis has the potential to become a non-invasive tool to distinguish malignant from benign tissues of urinary bladder cancer.

Key Points

Heterogeneity is an intrinsic characteristic of tumoral tissue.

Non-invasive quantification of tumour heterogeneity can provide adjunctive information to improve cancer diagnosis accuracy.

Histogram analysis and k-means clustering can quantify tumour diffusion heterogeneity.

The quantification helps differentiate malignant from benign urinary bladder tissue.


Bladder malignancy Tumour heterogeneity Apparent Diffusion Coefficient Histogram analysis K-means clustering 



The scientific guarantor of this publication is Dr. Michael V. Knopp at The Ohio State University. 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. The authors state that this work has not received any funding. One of the authors has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. The DCE-MRI data of the patient cohort were reported in Investigative Radiology and the Journal of Magnetic Resonance Imaging. Methodology: prospective, diagnostic or prognostic study, performed at one institution.


  1. 1.
    Bhatt J, Cowan N, Protheroe A, Crew J (2012) Recent advances in urinary bladder cancer detection. Expert Rev Anticancer Ther 12:929–939CrossRefPubMedGoogle Scholar
  2. 2.
    Sadow CA, Silverman SG, O'Leary MP, Signorovitch JE (2008) Bladder cancer detection with CT urography in an Academic Medical Center. Radiology 249:195–202CrossRefPubMedGoogle Scholar
  3. 3.
    Setty BN, Holalkere NS, Sahani DV, Uppot RN, Harisinghani M, Blake MA (2007) State-of-the-art cross-sectional imaging in bladder cancer. Curr Probl Diagn Radiol 36:83–96CrossRefPubMedGoogle Scholar
  4. 4.
    Shariat SF, Karam JA, Lotan Y, Karakiewizc PI (2008) Critical evaluation of urinary markers for bladder cancer detection and monitoring. Rev Urol 10:120–135PubMedPubMedCentralGoogle Scholar
  5. 5.
    Tatsugami K, Kuroiwa K, Kamoto T, Nishiyama H, Watanabe J, Ishikawa S et al (2010) Evaluation of narrow-band imaging as a complementary method for the detection of bladder cancer. J Endourol / Endourol Soc 24:1807–1811CrossRefGoogle Scholar
  6. 6.
    Nishimura K, Fujiyama C, Nakashima K, Satoh Y, Tokuda Y, Uozumi J (2009) The effects of neoadjuvant chemotherapy and chemo-radiation therapy on MRI staging in invasive bladder cancer: comparative study based on the pathological examination of whole layer bladder wall. Int Urol Nephrol 41:869–875CrossRefPubMedGoogle Scholar
  7. 7.
    Lambregts DM, Lahaye MJ, Heijnen LA, Martens MH, Maas M, Beets GL, Beets-Tan RG (2016) MRI and diffusion-weighted MRI to diagnose a local tumour regrowth during long-term follow-up of rectal cancer patients treated with organ preservation after chemoradiotherapy. Eur Radiol 26(7):2118–2125Google Scholar
  8. 8.
    Wagner M, Ronot M, Doblas S, Giraudeau C, Van Beers B, Belghiti J et al (2016) Assessment of the residual tumour of colorectal liver metastases after chemotherapy: diffusion-weighted MR magnetic resonance imaging in the peripheral and entire tumour. Eur Radiol 26:206–215CrossRefPubMedGoogle Scholar
  9. 9.
    Avcu S, Koseoglu MN, Ceylan K, Bulut MD, Unal O (2011) The value of diffusion-weighted MRI in the diagnosis of malignant and benign urinary bladder lesions. Br J Radiol 84:875–882CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Just N (2014) Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 111:2205–2213CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Alic L, Niessen WJ, Veenland JF (2014) Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review. Plos One 9:e110300CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Jerome NP, Miyazaki K, Collins DJ, Orton MR, d’Arcy JA, Wallace T et al (2016) Repeatability of derived parameters from histograms following non-Gaussian diffusion modelling of diffusion-weighted imaging in a paediatric oncological cohort. Eur Radiol. doi: 10.1007/s00330-016-4318-2
  14. 14.
    Andersen EK, Kristensen GB, Lyng H, Malinen E (2011) Pharmacokinetic analysis and k-means clustering of DCEMR images for radiotherapy outcome prediction of advanced cervical cancers. Acta Oncol 50:859–865CrossRefPubMedGoogle Scholar
  15. 15.
    Docquier PL, Paul L, Menten R, Cartiaux O, Francq B, Banse X (2009) Measurement of bone cyst fluid volume using k-means clustering. Magn Reson Imaging 27:1430–1439CrossRefPubMedGoogle Scholar
  16. 16.
    Gray C, MacGillivray TJ, Eeley C, Stephens NA, Beggs I, Fearon KC et al (2011) Magnetic resonance imaging with k-means clustering objectively measures whole muscle volume compartments in sarcopenia/cancer cachexia. Clin Nutr 30:106–111CrossRefPubMedGoogle Scholar
  17. 17.
    Nguyen HT, Jia G, Shah ZK, Pohar K, Mortazavi A, Zynger DL, Wei L, Yang X, Clark D, Knopp MV (2015) Prediction of chemotherapeutic response in bladder cancer using K-means clustering of dynamic contrast-enhanced (DCE)-MRI pharmacokinetic parameters. J Magn Reson Imaging 41:1374–1382Google Scholar
  18. 18.
    Srinivasan A, Galban CJ, Johnson TD, Chenevert TL, Ross BD, Mukherji SK (2010) Utility of the k-means clustering algorithm in differentiating apparent diffusion coefficient values of benign and malignant neck pathologies. AJNR Am J Neuroradiol 31:736–740CrossRefPubMedGoogle Scholar
  19. 19.
    Abou-El-Ghar ME, El-Assmy A, Refaie HF, El-Diasty T (2009) Bladder cancer: diagnosis with diffusion-weighted MR imaging in patients with gross hematuria. Radiology 251:415–421CrossRefPubMedGoogle Scholar
  20. 20.
    Nguyen HT, Pohar KS, Jia G, Shah ZK, Mortazavi A, Zynger DL et al (2014) Improving bladder cancer imaging using 3-T functional dynamic contrast-enhanced magnetic resonance imaging. Investig Radiol 49:390–395CrossRefGoogle Scholar
  21. 21.
    Daggulli M, Onur MR, Firdolas F, Onur R, Kocakoc E, Orhan I (2011) Role of diffusion MRI and apparent diffusion coefficient measurement in the diagnosis, staging and pathological classification of bladder tumors. Urol Int 87:346–352CrossRefPubMedGoogle Scholar
  22. 22.
    El-Assmy A, Abou-El-Ghar ME, Mosbah A, El-Nahas AR, Refaie HF, Hekal IA et al (2009) Bladder tumour staging: comparison of diffusion- and T2-weighted MR imaging. Eur Radiol 19:1575–1581CrossRefPubMedGoogle Scholar
  23. 23.
    Kilickesmez O, Cimilli T, Inci E, Kayhan A, Bayramoglu S, Tasdelen N et al (2009) Diffusion-weighted MRI of urinary bladder and prostate cancers. Diagn Interv Radiol 15:104–110PubMedGoogle Scholar
  24. 24.
    Kobayashi S, Koga F, Kajino K, Yoshita S, Ishii C, Tanaka H et al (2014) Apparent diffusion coefficient value reflects invasive and proliferative potential of bladder cancer. J Magn Reson Imaging: JMRI 39:172–178CrossRefPubMedGoogle Scholar
  25. 25.
    Kobayashi S, Koga F, Yoshida S, Masuda H, Ishii C, Tanaka H et al (2011) Diagnostic performance of diffusion-weighted magnetic resonance imaging in bladder cancer: potential utility of apparent diffusion coefficient values as a biomarker to predict clinical aggressiveness. Eur Radiol 21:2178–2186CrossRefPubMedGoogle Scholar
  26. 26.
    Matsuki M, Inada Y, Tatsugami F, Tanikake M, Narabayashi I, Katsuoka Y (2007) Diffusion-weighted MR imaging for urinary bladder carcinoma: initial results. Eur Radiol 17:201–204CrossRefPubMedGoogle Scholar
  27. 27.
    Takeuchi M, Sasaki S, Ito M, Okada S, Takahashi S, Kawai T et al (2009) Urinary bladder cancer: diffusion-weighted MR imaging--accuracy for diagnosing T stage and estimating histologic grade. Radiology 251:112–121CrossRefPubMedGoogle Scholar
  28. 28.
    Suo ST, Chen XX, Fan Y, Wu LM, Yao QY, Cao MQ et al (2014) Histogram analysis of apparent diffusion coefficient at 3.0 T in urinary bladder lesions: correlation with pathologic findings. Acad Radiol 21:1027–1034CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Huyen T. Nguyen
    • 1
  • Zarine K. Shah
    • 1
  • Amir Mortazavi
    • 2
  • Kamal S. Pohar
    • 3
  • Lai Wei
    • 4
  • Guang Jia
    • 5
    • 6
  • Debra L. Zynger
    • 7
  • Michael V. Knopp
    • 1
  1. 1.Wright Center of Innovation in Biomedical Imaging, Department of RadiologyThe Ohio State UniversityColumbusUSA
  2. 2.Department of Internal MedicineThe Ohio State UniversityColumbusUSA
  3. 3.Department of UrologyThe Ohio State UniversityColumbusUSA
  4. 4.Center for BiostatisticsThe Ohio State UniversityColumbusUSA
  5. 5.Department of Physics and AstronomyLouisiana State UniversityBaton RougeUSA
  6. 6.Pennington Biomedical Research CenterBaton RougeUSA
  7. 7.Department of PathologyThe Ohio State UniversityColumbusUSA

Personalised recommendations