Skip to main content

Advertisement

SpringerLink
Log in

A review of the principles of texture analysis and its role in imaging of genitourinary neoplasms

  • Special section: Radiogenomics
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Advances in the management of genitourinary neoplasms have resulted in a trend towards providing patients with personalized care. Texture analysis of medical images, is one of the tools that is being explored to provide information such as detection and characterization of tumors, determining their aggressiveness including grade and metastatic potential and for prediction of survival rates and risk of recurrence. In this article we review the basic principles of texture analysis and then detail its current role in imaging of individual neoplasms of the genitourinary system.

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. AJCC Cancer Staging Manual| Mahul B. Amin | Springer. http://www.springer.com/us/book/9783319406176 Accessed 10 Sep 2017

  2. Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin. 68(1):7–30

    Article  PubMed  Google Scholar 

  3. Stühler V, Kruck S, Hegemann M, et al. (2017) TKI 2.0—Wandel in der medikamentösen. Therapie des Nierenzellkarzinoms Urol. 6:1–7

    Google Scholar 

  4. Aragon-Ching JB (2014) The Evolution of Prostate Cancer Therapy: Targeting the Androgen Receptor. Front Oncol 4:295

    Article  PubMed  PubMed Central  Google Scholar 

  5. Martyn-Hemphill C, Mak D, Khan MS, Challacombe BJ, Bishop CV (2013) Recent advances in diagnosis and treatment of transitional cell carcinoma of the bladder. Int J Surg. 11(9):749–752

    Article  PubMed  Google Scholar 

  6. Einhorn LH (2002) Curing metastatic testicular cancer. Proc Natl Acad Sci USA. 99(7):4592–4595

    Article  CAS  PubMed  Google Scholar 

  7. Mazurowski MA (2015) Radiogenomics: What It Is and Why It Is Important. J Am Coll Radiol. 12(8):862–866

    Article  PubMed  Google Scholar 

  8. Giardino A, Gupta S, Olson E, et al. (2017) Role of Imaging in the Era of Precision Medicine. Acad Radiol. 24(5):639–649

    Article  PubMed  Google Scholar 

  9. Hellbach K, Sterzik A, Sommer W, et al. (2017) Dual energy CT allows for improved characterization of response to antiangiogenic treatment in patients with metastatic renal cell cancer. Eur Radiol. 27(6):2532–2537

    Article  CAS  PubMed  Google Scholar 

  10. 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(2):374–381

    Article  Google Scholar 

  11. Ledley RS, Huang HK, Rotolo LS (1975) A texture analysis method in classification of coal workers’ pneumoconiosis. Comput Biol Med. 5(1):53–67

    Article  CAS  PubMed  Google Scholar 

  12. Kotter E, Langer M (2011) Computer aided detection and diagnosis in radiology. Eur Radiol. 21(3):590–592

    Article  CAS  PubMed  Google Scholar 

  13. Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol. 59(12):1061–1069

    Article  CAS  PubMed  Google Scholar 

  14. Chen CH, Pau LF, Wang PSP (1993) Handbk of Pattern Recognition. Singapore: World Scientific Publishing Company, p 996

    Google Scholar 

  15. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. RadioGraphics. 37(5):1483–1503

    Article  PubMed  Google Scholar 

  16. Haralick RM, Shanmugam K, Dinstein I (1973) Textural Features for Image Classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621

    Article  Google Scholar 

  17. Mayerhoefer ME, Szomolanyi P, Jirak D, Materka A, Trattnig S (2009) Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: An application-oriented study. Med Phys. 36(4):1236–1243

    Article  PubMed  Google Scholar 

  18. Bashir U, Siddique MM, Mclean E, Goh V, Cook GJ (2016) Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges. Am J Roentgenol. 207(3):534–543

    Article  Google Scholar 

  19. Kassner A, Thornhill RE (2010) Texture Analysis: A Review of Neurologic MR Imaging Applications. Am J Neuroradiol. 31(5):809–816

    Article  CAS  PubMed  Google Scholar 

  20. Kim JY, Kim JK, Kim N, Cho K-S (2008) CT Histogram Analysis: Differentiation of Angiomyolipoma without Visible Fat from Renal Cell Carcinoma at CT Imaging. Radiology. 246(2):472–479

    Article  PubMed  Google Scholar 

  21. Kim JK, Kim SH, Jang YJ, et al. (2006) Renal Angiomyolipoma with Minimal Fat: Differentiation from Other Neoplasms at Double-Echo Chemical Shift FLASH MR Imaging. Radiology. 239(1):174–180

    Article  PubMed  Google Scholar 

  22. Hodgdon T, McInnes MDF, Schieda N, et al. (2015) Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology. 276(3):787–796

    Article  PubMed  Google Scholar 

  23. Lee HS, Hong H, Jung DC, Park S, Kim J (2017) Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification. Med Phys. 44(7):3604–3614

    Article  CAS  PubMed  Google Scholar 

  24. Lubner MG, Stabo N, Abel EJ, Del Rio AM, Pickhardt PJ (2016) CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes. AJR Am J Roentgenol. 207(1):96–105

    Article  PubMed  Google Scholar 

  25. Haider MA, Vosough A, Khalvati F, et al. (2017) CT texture analysis: a potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib. Cancer Imaging. 17:4

    Article  PubMed  PubMed Central  Google Scholar 

  26. Schieda N, Thornhill RE, Al-Subhi M, et al. (2015) Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis. AJR Am J Roentgenol. 204(5):1013–1023

    Article  PubMed  Google Scholar 

  27. Kierans AS, Rusinek H, Lee A, et al. (2014) Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma. AJR Am J Roentgenol. 203(6):W637–W644

    Article  PubMed  Google Scholar 

  28. Pignot G, Elie C, Conquy S, et al. (2007) Survival Analysis of 130 Patients with Papillary Renal Cell Carcinoma: Prognostic Utility of Type 1 and Type 2 Subclassification. Urology. 69(2):230–235

    Article  PubMed  Google Scholar 

  29. Doshi AM, Ream JM, Kierans AS, et al. (2016) Use of MRI in Differentiation of Papillary Renal Cell Carcinoma Subtypes: Qualitative and Quantitative Analysis. AJR Am J Roentgenol. 206(3):566–572

    Article  PubMed  Google Scholar 

  30. Antunes J, Viswanath S, Rusu M, et al. (2016) Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study. Transl Oncol. 9(2):155–162

    Article  PubMed  PubMed Central  Google Scholar 

  31. Goh V, Ganeshan B, Nathan P, et al. (2011) Assessment of Response to Tyrosine Kinase Inhibitors in Metastatic Renal Cell Cancer: CT Texture as a Predictive Biomarker. Radiology. 261(1):165–171

    Article  PubMed  Google Scholar 

  32. Han SM, Lee HJ, Choi JY (2008) Computer-aided Prostate Cancer Detection using Texture Features and Clinical Features in Ultrasound Image. J Digit Imaging. 21(Suppl 1):121–133

    Article  PubMed Central  Google Scholar 

  33. Mohamed SS, Li J, Salama MMA, Freeman G (2009) Prostate Tissue Texture Feature Extraction for Suspicious Regions Identification on TRUS Images. J Digit Imaging. 22(5):503–518

    Article  CAS  PubMed  Google Scholar 

  34. Kwak JT, Xu S, Wood BJ, et al. (2015) Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging. Med Phys. 42(5):2368–2378

    Article  PubMed  PubMed Central  Google Scholar 

  35. Khalvati F, Wong A, Haider MA (2015) Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models. BMC Med Imaging. 15:27

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lv D, Guo X, Wang X, Zhang J, Fang J (2009) Computerized characterization of prostate cancer by fractal analysis in MR images. J Magn Reson Imaging. 30(1):161–168

    Article  PubMed  Google Scholar 

  37. Sidhu HS, Benigno S, Ganeshan B, et al. (2017) Textural analysis of multiparametric MRI detects transition zone prostate cancer. Eur Radiol. 27(6):2348–2358

    Article  PubMed  Google Scholar 

  38. Gordetsky J, Epstein J (2016) Grading of prostatic adenocarcinoma: current state and prognostic implications. Diagn Pathol. 11:25

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Nketiah G, Elschot M, Kim E, et al. (2017) T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol. 27(7):3050–3059

    Article  PubMed  Google Scholar 

  40. 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(7):2685

    Article  CAS  PubMed  Google Scholar 

  41. Rozenberg R, Thornhill RE, Flood TA, et al. (2016) Whole-Tumor Quantitative Apparent Diffusion Coefficient Histogram and Texture Analysis to Predict Gleason Score Upgrading in Intermediate-Risk 3 + 4 = 7 Prostate Cancer. Am J Roentgenol. 206(4):775–782

    Article  Google Scholar 

  42. Gnep K, Fargeas A, Gutiérrez-Carvajal RE, et al. (2017) Haralick textural features on T2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging. 45(1):103–117

    Article  PubMed  Google Scholar 

  43. Reischauer C, Patzwahl R, Koh D-M, Froehlich JM, Gutzeit A (2018) Texture analysis of apparent diffusion coefficient maps for treatment response assessment in prostate cancer bone metastases—A pilot study. Eur J Radiol. 1(101):184–190

    Article  Google Scholar 

  44. Tekes A, Kamel I, Imam K, et al. (2005) Dynamic MRI of Bladder Cancer: Evaluation of Staging Accuracy. Am J Roentgenol. 184(1):121–127

    Article  Google Scholar 

  45. Xu X, Zhang X, Tian Q, et al. (2017) Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI. Int J Comput Assist Radiol Surg. 12(4):645–656

    Article  PubMed  Google Scholar 

  46. Garapati SS, Hadjiiski L, Cha KH, et al. (2017) Urinary bladder cancer staging in CT urography using machine learning. Med Phys. 44(11):5814–5823

    Article  PubMed  PubMed Central  Google Scholar 

  47. Woldu SL, Bagrodia A, Lotan Y (2017) Guideline of guidelines: non-muscle-invasive bladder cancer. BJU Int. 119(3):371–380

    Article  PubMed  PubMed Central  Google Scholar 

  48. Zhang X, Xu X, Tian Q, et al. (2017) Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging. 46(5):1281–1288

    Article  PubMed  PubMed Central  Google Scholar 

  49. Zhang G-M-Y, Sun H, Shi B, Jin Z-Y, Xue H-D (2017) Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. Abdom Radiol. 1, 42(2):561–568

    Article  Google Scholar 

  50. Shi Z, Yang Z, Zhang G, et al. (2013) Characterization of Texture Features of Bladder Carcinoma and the Bladder Wall on MRI. Acad Radiol. 20(8):930–938

    Article  PubMed  PubMed Central  Google Scholar 

  51. Wu S, Zheng J, Li Y, et al. (2017) A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer. Clin Cancer Res. 23(22):6904–6911

    Article  CAS  PubMed  Google Scholar 

  52. Cha KH, Hadjiiski L, Chan H-P, et al. (2017) Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning. Sci Rep. 7:8738

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Lenert JT, Barnett CC, Kudelka AP, et al. (2001) Evaluation and surgical resection of adrenal masses in patients with a history of extra-adrenal malignancy. Surgery. 130(6):1060–1067

    Article  CAS  PubMed  Google Scholar 

  54. Schieda N, Krishna S, McInnes MDF, et al. (2017) Utility of MRI to Differentiate Clear Cell Renal Cell Carcinoma Adrenal Metastases From Adrenal Adenomas. Am J Roentgenol. 209(3):W152–W159

    Article  Google Scholar 

  55. Chong S, Lee KS, Kim HY, et al. (2006) Integrated PET-CT for the Characterization of Adrenal Gland Lesions in Cancer Patients: Diagnostic Efficacy and Interpretation Pitfalls. RadioGraphics. 26(6):1811–1824

    Article  PubMed  Google Scholar 

  56. Nakajo M, Jinguji M, Nakajo M, et al. (2017) Texture analysis of FDG PET/CT for differentiating between FDG-avid benign and metastatic adrenal tumors: efficacy of combining SUV and texture parameters. Abdom Radiol. 13:1–8

    Google Scholar 

  57. Chalkidou A, O’Doherty MJ, Marsden PK (2015) False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review. PLoS ONE. 10(5):e0124165

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Mackin D, Fave X, Zhang L, et al. (2015) Measuring CT scanner variability of radiomics features. Invest Radiol. 50(11):757–765

    Article  PubMed  PubMed Central  Google Scholar 

  59. Lu L, Ehmke RC, Schwartz LH, Zhao B (2016) Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings. PLoS ONE. 11(12):e0166550

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Shafiq-ul-Hassan M, Zhang GG, Latifi K, et al. (2017) Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 44(3):1050–1062

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Solomon J, Mileto A, Nelson RC, Roy Choudhury K, Samei E (2015) Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi-Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm. Radiology. 279(1):185–194

    Article  PubMed  Google Scholar 

  62. Brynolfsson P, Nilsson D, Torheim T, et al. (2017) Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters. Sci Rep. 7:4041

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Fave X, Mackin D, Yang J, et al. (2015) Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med Phys. 42(12):6784–6797

    Article  PubMed  PubMed Central  Google Scholar 

  64. Summers RM (2017) Texture analysis in radiology: Does the emperor have no clothes? Abdom Radiol. 42(2):342–345

    Article  Google Scholar 

  65. Recht M, Bryan RN (2017) Artificial Intelligence: Threat or Boon to Radiologists? J Am Coll Radiol. 14:1476–1480

    Article  PubMed  Google Scholar 

  66. Chockley K, Emanuel E (2016) The End of Radiology? Three Threats to the Future Practice of Radiology. J Am Coll Radiol. 13:1415–1420

    Article  PubMed  Google Scholar 

  67. Wang G, Kalra M, Orton CG (2017) Machine learning will transform radiology significantly within the next 5 years. Med Phys. 44(6):2041–2044

    Article  PubMed  Google Scholar 

  68. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL (2015) Machine Learning methods for Quantitative Radiomic Biomarkers. Sci Rep. 5:13087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, 02115, USA

    Richard Thomas, Francesco Alessandrino, Sonia P. Sahu, Katherine M. Krajewski & Atul Shinagare

  2. Department of Imaging, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA

    Richard Thomas, Lei Qin, Francesco Alessandrino, Pamela J. Guerra, Katherine M. Krajewski & Atul Shinagare

  3. Harvard Medical School, Boston, MA, USA

    Richard Thomas, Lei Qin, Francesco Alessandrino, Sonia P. Sahu, Pamela J. Guerra, Katherine M. Krajewski & Atul Shinagare

Authors
  1. Richard Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesco Alessandrino
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sonia P. Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pamela J. Guerra
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Katherine M. Krajewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Atul Shinagare
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Richard Thomas.

Ethics declarations

Funding

This review article did not receive any funding.

Conflict of interest

Dr. Shinagare has the following financial disclosures: 1. Consultant, Arog Pharmaceuticals, 2. Research funding, GTx Inc.Thomas, Qin, Alessandrino, Sahu, Guerra and Krajewski do not have any disclosures.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

There was no need for informed consent as this is a review article.

IRB approval

No IRB approval was necessary for this review article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thomas, R., Qin, L., Alessandrino, F. et al. A review of the principles of texture analysis and its role in imaging of genitourinary neoplasms. Abdom Radiol 44, 2501–2510 (2019). https://doi.org/10.1007/s00261-018-1832-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-018-1832-5

Keywords

Search

Navigation