Advertisement

European Radiology

, Volume 29, Issue 11, pp 6182–6190 | Cite as

Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors

  • Huanjun Wang
  • Daokun Hu
  • Haohua Yao
  • Maodong Chen
  • Shurong Li
  • Haolin Chen
  • Junhang Luo
  • Yanqiu FengEmail author
  • Yan GuoEmail author
Imaging Informatics and Artificial Intelligence

Abstract

Objectives

To develop and validate an MRI-based radiomics strategy for the preoperative estimation of pathological grade in bladder cancer (BCa) tumors.

Methods

A primary cohort of 70 patients (31 high-grade BCa and 39 low-grade BCa) with BCa were retrospectively enrolled. Three sets of radiomics features were separately extracted from tumor volumes on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Two sets of multimodal features were separately generated by the maxout and concatenation of the above mentioned single-modality features. Each feature set was subjected to a two-sample t test and the least absolute shrinkage and selection operator (LASSO) algorithm for feature selection. Multivariable logistic regression (LR) analysis was used to obtain five corresponding radiomics models. The diagnostic abilities of the radiomics models were evaluated using receiver operating characteristic (ROC) curve analysis and compared using the DeLong test. Validation was performed on a time-independent cohort containing 30 consecutive patients.

Results

The areas under the ROC curves (AUCs) of single-modality T2WI, DWI, and ADC models in the training cohort were 0.7933 (95% confidence interval [CI] 0.7471–0.8396), 0.8083 (95% CI 0.7565–0.8601), and 0.8350 (95% CI 0.7924–0.8776), respectively. Both multimodality models achieved higher AUCs (maxout 0.9233, 95% CI 0.9001–0.9466; concatenation 0.9233, 95% CI 0.9001–0.9466) than single-modality models. The AUCs of the maxout and concatenation models in the validation cohort were 0.9186 and 0.9276, respectively.

Conclusions

The MRI-based multiparametric radiomics approach has the potential to be used as a noninvasive imaging tool for preoperative grading of BCa tumors. Multicenter validation is needed to acquire high-level evidence for its clinical application.

Key Points

• Multiparametric MRI may help in the preoperative grading of BCa tumors.

• The Joint_Model established from T2WI, DWI, and ADC feature subsets demonstrated a high diagnostic accuracy for preoperative prediction of pathological grade in BCa tumors.

• The radiomics approach has the potential to preoperatively assess tumor grades in BCa and avoid subjectivity.

Keywords

Magnetic resonance imaging Urinary bladder ROC curve Regression analysis 

Abbreviations

ADC

Apparent diffusion coefficient

AUC

Area under the curve

BCa

Bladder cancer

CI

Confidence interval

DTI

Diffusion tensor imaging

DWI

Diffusion-weighted imaging

LASSO

Least absolute shrinkage and selection operator

MIBCa

Muscle-invasive bladder cancer

NMIBCa

Non-muscle-invasive bladder cancer

PWI

Perfusion-weighted imaging

ROC

Receiver operating characteristic

TIS

Tumor in situ

Notes

Acknowledgements

The abstract of this study has been accepted as a scientific oral presentation at the European Congress of Radiology 2019 annual meeting.

Funding

This study has received funding by National Natural Science Foundation of China (81701747) and Natural Science Foundation of Guangdong Province (2017A030313902).

Compliance with ethical standards

Conflict of interest

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.

Guarantor

The scientific guarantors of this publication are Yanqiu Feng and Yan Guo.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board of the First Affiliated Hospital of Sun Yat-sen University.

Ethical approval

Ethical approval was obtained from the Institutional Review Board of The First Affiliated Hospital of Sun Yat-sen University.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

References

  1. 1.
    Siegel R, Naishadham D, Jemal A (2013) Cancer statistics, 2013. CA Cancer J Clin 63:11–30Google Scholar
  2. 2.
    Svatek RS, Hollenbeck BK, Holmang S et al (2014) The economics of bladder cancer: costs and considerations of caring for this disease. Eur Urol 66:253–262Google Scholar
  3. 3.
    Antoni S, Ferlay J, Soerjomataram I, Znaor A, Jemal A, Bray F (2017) Bladder cancer incidence and mortality: a global overview and recent trends. Eur Urol 71:96–108Google Scholar
  4. 4.
    Eble JN, Sauter G, Epstein JI, Sesterhenn IA (eds) (2004) World Health Organization classification of tumors: pathology and genetics of tumors of the urinary system and male genital organs. IARC Press, Lyon. Available via https://www.iarc.fr/wpcontent/uploads/2018/07/BB7.pdf. Accessed 2 Apr 2019
  5. 5.
    Linton KD, Rosario DJ, Thomas F et al (2013) Disease specific mortality in patients with low risk bladder cancer and the impact of cystoscopic surveillance. J Urol 189:828–833Google Scholar
  6. 6.
    Sylvester RJ, van der Meijden AP, Oosterlinck W et al (2006) Predicting recurrence and progression in individual patients with stage Ta T1 bladder cancer using EORTC risk tables: a combined analysis of 2596 patients from seven EORTC trials. Eur Urol 49:466–465 discussion 475–477Google Scholar
  7. 7.
    Gofrit ON, Pode D, Lazar A, Katz R, Shapiro A (2006) Watchful waiting policy in recurrent Ta G1 bladder tumors. Eur Urol 49:303–306 discussion 306–307Google Scholar
  8. 8.
    Mariappan P, Smith G (2005) A surveillance schedule for G1Ta bladder cancer allowing efficient use of check cystoscopy and safe discharge at 5 years based on a 25-year prospective database. J Urol 173:1108–1111Google Scholar
  9. 9.
    Babjuk M, Bohle A, Burger M et al (2017) EAU guidelines on non-muscleinvasive urothelial carcinoma of the bladder: update 2016. Eur Urol 71:447–461Google Scholar
  10. 10.
    Bellmunt J, Orsola A, Leow JJ et al (2014) Bladder cancer: ESMO Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 25(suppl 3):iii40–iii48Google Scholar
  11. 11.
    Barchetti G, Simone G, Ceravolo I et al (2019) Multiparametric MRI of the bladder: inter-observer agreement and accuracy with the Vesical Imaging-Reporting and Data System (VI-RADS) at a single reference center. Eur Radiol.  https://doi.org/10.1007/s00330-019-06117-8
  12. 12.
    Wang HJ, Pui MH, Guo Y et al (2015) Multiparametric 3-T MRI for differentiating low-versus high-grade and category T1 versus T2 bladder urothelial carcinoma. AJR Am J Roentgenol 204:330–334Google Scholar
  13. 13.
    Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446Google Scholar
  14. 14.
    Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248Google Scholar
  15. 15.
    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577Google Scholar
  16. 16.
    Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006Google Scholar
  17. 17.
    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 46:1281–1288Google Scholar
  18. 18.
    Becker AS, Wagner MW, Wurnig MC, Boss A (2017) Diffusion-weighted imaging of the abdomen: impact of b-values on texture analysis features. NMR Biomed 30(1).  https://doi.org/10.1002/nbm.3669
  19. 19.
    an Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107Google Scholar
  20. 20.
    Vasquez MM, Hu C, Roe DJ, Chen Z, Halonen M, Guerra S (2016) Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application. BMC Med Res Methodol 16:154Google Scholar
  21. 21.
    Franklin J (2010) The elements of statistical learning: data mining, inference and prediction. Publ Am Stat Assoc 99(466):567–567Google Scholar
  22. 22.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845Google Scholar
  23. 23.
    Takeuchi M, Sasaki S, Ito M et al (2009) Urinary bladder cancer: diffusion-weighted MR imaging—accuracy for diagnosing t stage and estimating histologic grade. Radiology 251:112–121Google Scholar
  24. 24.
    Gaston KE, Pruthi RS (2004) Value of urinary cytology in the diagnosis and management of urinary tract malignancies. Urology 63:1009–1016Google Scholar
  25. 25.
    Panebianco V, De Berardinis E, Barchetti G et al (2017) An evaluation of morphological and functional multi-parametric MRI sequences in classifying non-muscle and muscle invasive bladder cancer. Eur Radiol 27:3759–3766Google Scholar
  26. 26.
    Wang F, Chen HG, Zhang RY et al (2019) Diffusion kurtosis imaging to assess correlations with clinicopathologic factors for bladder cancer: a comparison between the multi-b value method and the tensor method. Eur Radiol.  https://doi.org/10.1007/s00330-018-5977-y

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of RadiologyThe First Affiliated Hospital of Sun Yat-Sen UniversityGuangzhouPeople’s Republic of China
  2. 2.School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image ProcessingSouthern Medical UniversityGuangzhouPeople’s Republic of China
  3. 3.Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical EngineeringSouthern Medical UniversityGuangzhouPeople’s Republic of China
  4. 4.Department of UrologyThe First Affiliated Hospital of Sun Yat-Sen UniversityGuangzhouPeople’s Republic of China

Personalised recommendations