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Role of quantitative computed tomography texture analysis in the prediction of adherent perinephric fat

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Abstract

Objective

To assess the performance of computed tomography (CT) texture analysis to predict the presence of adherent perinephric fat (APF).

Materials and methods

Seventy patients with small renal tumors treated with robot-assisted partial nephrectomy were included. Patients were divided into two groups according to the presence of APF. We extracted 15 image features from unenhanced CT and contrast-enhanced CT corresponding to first-order and second-order Haralick textural features. Predictors of APF were evaluated by univariable and multivariable analysis. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) to predict APF was calculated for the independent predictors.

Results

APF was observed in 26 patients (37%). We identified entropy (p = 0.01), sum entropy (p = 0.02) and difference entropy (p = 0.05) as significant independent predictors of APF. In the portal phase, we identified correlation (p = 0.03), inverse difference moment (p = 0.01), sum entropy (p = 0.02), entropy (p = 0.01), difference variance (p = 0.04) and difference entropy (p = 0.02) as significant independent predictors of APF. Combining these parameters yielded to an ROC-AUC of 0.82 (95% CI 0.65–0.86).

Conclusion

Results from this preliminary study suggest that CT texture analysis might be a promising quantitative imaging tool that helps urologist to identify APF.

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References

  1. Patard J-J, Rodriguez A, Rioux-Leclercq N, Guillé F, Lobel B (2002) Prognostic significance of the mode of detection in renal tumours. BJU Int 90(4):358–363

    Article  Google Scholar 

  2. Ljungberg B, Bensalah K, Canfield S, Dabestani S, Hofmann F, Hora M et al (2015) EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol 67(5):913–924

    Article  Google Scholar 

  3. Simhan J, Smaldone MC, Tsai KJ, Canter DJ, Li T, Kutikov A et al (2011) Objective measures of renal mass anatomic complexity predict rates of major complications following partial nephrectomy. Eur Urol 60(4):724–730

    Article  Google Scholar 

  4. Mari A, Antonelli A, Bertolo R, Bianchi G, Borghesi M, Ficarra V et al (2017) Predictive factors of overall and major postoperative complications after partial nephrectomy: results from a multicenter prospective study (The RECORd 1 project). Eur J Surg Oncol EJSO 43(4):823–830

    Article  CAS  Google Scholar 

  5. Kocher NJ, Kunchala S, Reynolds C, Lehman E, Nie S, Raman JD (2016) Adherent perinephric fat at minimally invasive partial nephrectomy is associated with adverse peri-operative outcomes and malignant renal histology. BJU Int 117(4):636–641

    Article  Google Scholar 

  6. Khene Z-E, Peyronnet B, Mathieu R, Fardoun T, Verhoest G, Bensalah K (2015) Analysis of the impact of adherent perirenal fat on peri-operative outcomes of robotic partial nephrectomy. World J Urol 33(11):1801–1806

    Article  Google Scholar 

  7. Zheng Y, Espiritu P, Hakky T, Jutras K, Spiess PE (2014) Predicting ease of perinephric fat dissection at time of open partial nephrectomy using preoperative fat density characteristics: preoperative fat characteristics for predicting ease of dissection at OPN. BJU Int 114(6):872–880

    Article  Google Scholar 

  8. Davidiuk AJ, Parker AS, Thomas CS, Leibovich BC, Castle EP, Heckman MG et al (2014) Mayo adhesive probability score: an accurate image-based scoring system to predict adherent perinephric fat in partial nephrectomy. Eur Urol 66(6):1165–1171

    Article  Google Scholar 

  9. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577

    Article  Google Scholar 

  10. Materka A, Strzelecki M et al (1998) Texture analysis methods—a review. Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels, pp 9–11

  11. Simpson AL, Adams LB, Allen PJ, D’Angelica MI, DeMatteo RP, Fong Y et al (2015) Texture analysis of preoperative CT images for prediction of postoperative hepatic insufficiency: a preliminary study. J Am Coll Surg 220(3):339–346

    Article  Google Scholar 

  12. Ryu YJ, Choi SH, Park SJ, Yun TJ, Kim J-H, Glioma Sohn C-H (2014) Application of Whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity. PLoS ONE. 9(9):e108335

    Article  Google Scholar 

  13. Canellas R, Mehrkhani F, Patino M, Kambadakone A, Sahani D (2016) Characterization of portal vein thrombosis (neoplastic versus bland) on CT images using software-based texture analysis and thrombus density (hounsfield units). Am J Roentgenol 207(5):W81–W87

    Article  Google Scholar 

  14. Gnep K, Fargeas A, Gutiérrez-Carvajal RE, Commandeur F, Mathieu R, Ospina JD et al (2017) Haralick textural features on T 2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer: impact of MRI in prostate cancer. J Magn Reson Imaging 45(1):103–117

    Article  Google Scholar 

  15. Bylund JR, Qiong H, Crispen PL, Venkatesh R, Strup SE (2013) Association of clinical and radiographic features with perinephric ‘sticky’ fat. J Endourol 27(3):370–373

    Article  Google Scholar 

  16. Davidiuk AJ, Parker AS, Thomas CS, Heckman MG, Custer K, Thiel DD (2015) Prospective evaluation of the association of adherent perinephric fat with perioperative outcomes of robotic-assisted partial nephrectomy. Urology 85(4):836–842

    Article  Google Scholar 

  17. Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 22(1):81–91

    Article  CAS  Google Scholar 

  18. Szczypiński PM, Strzelecki M, Materka A, Klepaczko A (2009) MaZda—a software package for image texture analysis. Comput Methods Programs Biomed 94(1):66–76

    Article  Google Scholar 

  19. Gibbs P, Turnbull LW (2003) Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 50(1):92–98

    Article  Google Scholar 

  20. Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804

    Article  Google Scholar 

  21. Raman SP, Chen Y, Schroeder JL, Huang P, Fishman EK (2014) CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. Acad Radiol 21(12):1587–1596

    Article  Google Scholar 

  22. Sasaguri K, Takahashi N, Gomez-Cardona D, Leng S, Schmit GD, Carter RE et al (2015) Small (< 4 cm) renal mass: differentiation of oncocytoma from renal cell carcinoma on biphasic contrast-enhanced CT. AJR Am J Roentgenol 205(5):999–1007

    Article  Google Scholar 

  23. Goh V, Ganeshan B, Nathan P, Juttla JK, Vinayan A, Miles KA (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  Google Scholar 

  24. Macleod LC, Hsi RS, Gore JL, Wright JL, Harper JD (2014) Perinephric fat thickness is an independent predictor of operative complexity during robot-assisted partial nephrectomy. J Endourol 28(5):587–591

    Article  Google Scholar 

  25. Dariane C, Le Guilchet T, Hurel S, Audenet F, Beaugerie A, Badoual C et al (2017) Prospective assessment and histological analysis of adherent perinephric fat in partial nephrectomies. Urol Oncol Semin Orig Investig 35(2):39.e9–39.e17

    Article  Google Scholar 

  26. Lubner MG, Stabo N, Lubner SJ, del Rio AM, Song C, Halberg RB et al (2015) CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging 40(7):2331–2337

    Article  Google Scholar 

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Acknowledgements

Z.-E. Khene would like to thank the French Young Urological Association for his funding.

Author information

Authors and Affiliations

  1. Department of Urology, Rennes University Hospital, Rennes, France

    Zine‐Eddine Khene, Karim Bensalah, Gregory Verhoest, Benoit Peyronnet & Romain Mathieu

  2. LTSI, Inserm U1099, Université de Rennes 1, Rennes, France

    Zine‐Eddine Khene, Axel Largent, Oscar Acosta, Renaud DeCrevoisier & Romain Mathieu

  3. Department of Urology, Medical University Vienna, General Hospital, Vienna, Austria

    Shahrokh Shariat

  4. Department of Urology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA

    Shahrokh Shariat

  5. Department of Urology, Weill Cornell Medical College, New York, NY, USA

    Shahrokh Shariat

  6. Karl Landsteiner Institute, Vienna, Austria

    Shahrokh Shariat

  7. Centre Eugene Marquis, Rennes, France

    Renaud DeCrevoisier

Authors
  1. Zine‐Eddine Khene
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  2. Karim Bensalah
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  3. Axel Largent
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  4. Shahrokh Shariat
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  5. Gregory Verhoest
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  6. Benoit Peyronnet
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  7. Oscar Acosta
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  8. Renaud DeCrevoisier
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  9. Romain Mathieu
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Contributions

Z-EK, KB, AL, SS, GV, BP, OA, RD, RM: Project development. Z-EK, AL, RM, GV, BP: Data collection. Z-EK, KB, RM, SS, OA, RD: Data analysis. Z-EK, KB, AL, SS, GV, BP, OA, RD, RM: Manuscript editing.

Corresponding author

Correspondence to Zine‐Eddine Khene.

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Conflict of interest

Karim Bensalah and Gregory Verhoest are consultants for Intuitive Surgical.

Ethical standard

Local ethics committee approval.

Informed consent

Informed consent was obtained from all individual participants included in the study. This study and all the related procedures have been performed in accordance with the Declaration of Helsinki.

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Khene, Z., Bensalah, K., Largent, A. et al. Role of quantitative computed tomography texture analysis in the prediction of adherent perinephric fat. World J Urol 36, 1635–1642 (2018). https://doi.org/10.1007/s00345-018-2292-9

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  • DOI: https://doi.org/10.1007/s00345-018-2292-9

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