Skip to main content

Advertisement

SpringerLink
Log in

Using texture analysis as a predictive factor of subtype, grade and stage of renal cell carcinoma

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objective

The aim of this study was to evaluate the correlation between the tissue texture analysis and the histological subtypes, grade and stage of the disease in patients with renal cell carcinoma (RCC).

Materials and methods

Seventy-seven patients who underwent computed tomography due to renal mass and diagnosed with RCC as a result of pathological examination were retrospectively analyzed. In these analyses, the demographic characteristics, pathological and radiological findings of the patients were evaluated. The masses were introduced to the Radiomics extension of the software and the first- and second-order texture analysis parameters were obtained. The correlation of these parameters with histological subtype, Fuhrman grade and TNM stage was investigated.

Results

In the comparison of the Radiomics values by stages, “minimum”, “Long Run Low Gray-level Emphasis” values were higher in the stage 1–2 group, while “Energy”, “Total energy”, “Range”, “Joint Average”, “Sum Average”, “Gray-Level Non-Uniformity”, “Short-Run High Gray-level Emphasis “, “Run Length Non-Uniformity “and “High Gray-Level Run Emphasis “values were higher in the stage 3–4 group. Of these parameters, only “Gray-Level Non-Uniformity” and “Run Length Non-Uniformity’’ values were significantly lower in tumors with low Fuhrman grade (1–2) and low TNM stage (1–2). There was no statistically significant correlation between the parameters found to be significant in histological subtype differentiation and Fuhrman grade and TNM stage.

Conclusion

This study demonstrates that “Gray-Level Non-Uniformity” and “Run Length Non-Uniformity “parameters in the texture analysis method can be used to evaluate the prognosis in patients with RCC.

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

Similar content being viewed by others

Abbreviations

RCC:

Renal cell carcinoma

TNM:

Tumor, node, metastasis

CT:

Computed tomography

PACS:

Picture archiving and communication system

DICOM:

Digital imaging and communications in medicine

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run length matrix

ROC:

Receiver operating characteristic curve

AUC:

Area under the ROC curve

GLN:

Gray-level non-uniformity

RLN:

Run length non-uniformity

References

  1. Göğüs C, Baltacı S, Filiz E, Elhan A, Beduk Y. (2004) Significance of thrombocytosis for determining prognosis in patients with lokalized renal cell carsinoma. Urology 63: 447-450.

    Article  Google Scholar 

  2. Landis SH, Murray T, Bolden S et al. (1999) Cancer statistics. CA Cancer J. Clin. 49: 8.

    Article  CAS  Google Scholar 

  3. Johnsen JA, Hellsten S. (1997) Lymphatogenous spread of renal cell carcinoma: an autopsy study. J Urol. 157:450-3.

    Article  CAS  Google Scholar 

  4. Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, Ma ZL, Liu ZY (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer, J. Clin. Oncol. 34 (18) 2157–2164.

    Article  Google Scholar 

  5. Birkhahn M, Mitra AP, Cote RJ (2007) Molecular markers for bladder cancer: the road to a multimarker approach, Expert Rev. Anticancer Ther. 7 (12) 1717–1727.

    Article  CAS  Google Scholar 

  6. Croner RS, Fortsch T, Bruckl WM, Rodel F, Rodel C, Papadopoulos T, Brabletz T, Kirchner T, Sachs M, Behrens J, L. Klein-Hitpass, M. Sturzl,W. Hohenberger,B.Lausen, (2008) Molecular signature for lymphatic metastasis in colorectal carcinomas, Ann. Surg. 247 (5) :803–810.

    Article  Google Scholar 

  7. Ficarra V, Galfano A, Novara G, et al. (2008) Risk stratification and prognostication of renal cell carsinoma. World J Urol. 26: 115-125.

    Article  Google Scholar 

  8. Sheir KZ, El-Azab M, Mosbah A, et al. (2005) Differentiation of renal cell carcinoma subtypes by multislice computerized tomography. J Urol. 174:451–455.

    Article  Google Scholar 

  9. Kim JH, Bae JH, Lee KW, et al. (2012) Predicting the histology of small renal masses using preoperative dynamic contrast-enhanced magnetic resonance imaging. Urology. 80:872–876.

    Article  Google Scholar 

  10. Jung SC, Cho JY, Kim SH. (2012) Subtype differentiation of small renal cell carcinomas on three-phase MDCT: usefulness of the measurement of degree and heterogeneity of enhancement. Acta Radiol. 53:112–118. [PubMed: 22114020]

    Article  Google Scholar 

  11. Zhang J, Lefkowitz R, Ishill N, et al. (2007) Solid renal cortical tumors: differentiation with CT. Radiology. 244:494–504

    Article  Google Scholar 

  12. Choi SK, Jeon SH, Chang SG. (2012) Characterization of small renal masses less than 4 cm with quadriphasic multidetector helical computed tomography: differentiation of benign and malignant lesions. Korean J Urol. 53:159–164.

    Article  Google Scholar 

  13. Jinzaki M, Tanimoto A, Mukai M, et al. (2000) Double-phase helical CT of small renal parenchymal neoplasms: correlation with pathologic findings and tumor angiogenesis. J Comput Assist Tomogr. 24:835–842.

    Article  CAS  Google Scholar 

  14. Young JR, Margolis D, Sauk S, et al. (2013) Clear cell renal cell carcinoma: discrimination from other renal cell carcinom subtypes and oncocytoma at multiphasic multidetector CT. Radiology. 267:444–453.

    Article  Google Scholar 

  15. Herts B, Coll D, Novick A, et al. (2002) Enhancement characteristics of papillary renal neoplasms revealed on triphasic helical CT of the kidneys. AJR Am J Roentgenol. 178:367–37

    Article  Google Scholar 

  16. Bird VG, Kanagarajah P, Morillo G, et al. (2011) Differentiation of oncocytoma and renal cell carcinoma in small renal masses (<4 cm): the role of 4-phase computerized tomography. World J Urol. 29:787–792.

    Article  CAS  Google Scholar 

  17. Raman SP, Johnson PT, Allaf ME, et al. (2013) Chromophobe renal cell carcinoma: multiphase MDCT enhancement patterns and morphologic features. AJR Am J Roentgenol. 201:1268–1276.

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Lambin P, Leijenaar RTH, Deist T et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762.

    Article  Google Scholar 

  20. Lubner MG, et al. (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  Google Scholar 

  21. Kumar, Gu V, Basu Y, Berglund S, Eschrich A, Schabath SA (2012). Radiomics: the process and the challenges. Magnetic Resonance Imaging, 30(9), 1234–1248.

    Article  Google Scholar 

  22. Ba-Ssalamah A, Muin D, Schernthaner R, et al. (2013) Texture-based classification of different gastric tumors at contrast-enhanced CT. Eur J Radiol. 82:e537–e543.

    Article  Google Scholar 

  23. Burke HB, Henson DE (1993) Criteria for prognostic factorsand for an enhanced prognostic system. Cancer, 72: 3131-3135,

    Article  CAS  Google Scholar 

  24. López JI, Angulo JC (2018). Pathological Bases and Clinical Impact of Intratumor Heterogeneity in Clear Cell Renal Cell Carcinoma. Current Urology Reports, 19(1).

  25. Scrima AT, Lubner MG, Abel EJ, Havighurst TC, Shapiro DD, Huang W, Pickhardt PJ (2018) Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers

  26. Novara G, Martignoni G, Artibani W, Ficarra V (2007) Grading systems in renal cell carcinoma, J. Urol. 177 (2) (430–436.

  27. Guethmundsson E, Hellborg H, Lundstam S, Erikson S, Ljungberg B, Swedish G (2011) Kidney Cancer Quality Register, Metastatic potential in renal cell carcinomas </=7 cm: Swedish Kidney Cancer Quality Register data, Eur. Urol. 60 (5): 975 982.

  28. Ding J, Xing Z, Jiang Z, Chen J, Pan L, Qiu J, Xing W (2018). CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. European Journal of Radiology, 103, 51–56.

    Article  Google Scholar 

  29. Oh S, Sung DJ, Yang KS, Sim KC, Han NY, Park BJ, Kim MJ, Cho SB (2017) Correlation of CT imaging features and tumor size with Fuhrman grade of clear cell renal cell carcinoma, Acta Radiol. 58 (3): 376–384.

    Article  Google Scholar 

  30. Hotker AM, Karlo CA, Zheng J, Moskowitz CS, Russo P, Hricak H, Akin O (2016) Clear cell renal cell carcinoma: associations between CT features and patient survival, AJR Am. J. Roentgenol. 206 (5): 1023–1030.

    Article  Google Scholar 

  31. Novara G, Ficarra V, Antonelli A, Artibani W, Bertini R, Carini M, et al. (2010) Validation of the 2009 TNM version in a large multi-institutional cohort of patients treated for renal cell carcinoma: are further improvements needed? Eur Urol;58(4):588-95.

    Article  Google Scholar 

Download references

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Radiology, Kocaeli Derince Training and Research Hospital, University of Health Sciences, İbni Sina M. Sopalı Mevki Lojman S. Derince, Kocaeli, Turkey

    Servan Yaşar, Nuray Voyvoda & Tülay Özer

  2. Department of Urology, Kocaeli Derince Training and Research Hospital, University of Health Sciences, Kocaeli, Turkey

    Bekir Voyvoda

Authors
  1. Servan Yaşar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nuray Voyvoda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bekir Voyvoda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tülay Özer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Servan Yaşar: Concept/ design, Data analysis/interpretation, Drafting article, Critical revision of article, Approval of article, Statistics, Data collection; Nuray Voyvoda: Concept/ design,Data analysis/interpretation, Critical revision of article, Approval of article; Bekir Voyvoda: Data collection; Tülay Özer: Concept/ design, Statistics, Critical revision of article.

Corresponding author

Correspondence to Nuray Voyvoda.

Ethics declarations

Conflict of interest

Authors and authors' institutions declare that they have no conflict of interest.

Ethical approval

The study protocol was approved by the Ethics Committee (25/01/2019, Number: 46418926, Decision: 19/07)..

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yaşar, S., Voyvoda, N., Voyvoda, B. et al. Using texture analysis as a predictive factor of subtype, grade and stage of renal cell carcinoma. Abdom Radiol 45, 3821–3830 (2020). https://doi.org/10.1007/s00261-020-02495-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-020-02495-6

Keywords

Search

Navigation