Skip to main content

Advertisement

SpringerLink
Log in

Current applications and challenges of radiomics in urothelial cancer

  • Review
  • Published:
Chinese Journal of Academic Radiology Aims and scope Submit manuscript

Abstract

New discoveries and technologies have begun to change paradigms of urothelial cancer therapy in recent years. One of the novel techniques which emerged in the imaging community is radiomics, which refers to the high-throughput extraction of quantitative image features from medical images. Radiomics, being noninvasive and easy to perform, has shown great potential in oncology by providing valuable information about tumor type, aggressiveness, progression, response to treatment and prognosis and enabling us to gain insights into the true utility of personalized medicine in the management of cancer in the near future. With rapid development in this area, radiomics has already been applied in urothelial cancer to predict pathological grade, clinical stage, lymph node metastasis and treatment response demonstrating promising results. In this review, we highlight advances in clinical applications of radiomics in urothelial cancer, discuss about the challenges and implications of radiomics for radiologists and suggest the future directions that we could move toward in order to fully realize the potentials of radiomics to improve personalized management of patients with urothelial cancer.

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

References

  1. Miyazaki J, Nishiyama H. Epidemiology of urothelial carcinoma. Int J Urol. 2017;24(10):730–4. https://doi.org/10.1111/iju.13376.

    Article  PubMed  Google Scholar 

  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018;68(1):7–30. https://doi.org/10.3322/caac.21442.

    Article  PubMed  Google Scholar 

  3. Mahdavifar N, Ghoncheh M, Pakzad R, Momenimovahed Z, Salehiniya H. Epidemiology, incidence and mortality of bladder cancer and their relationship with the development index in the world. Asian Pac J Cancer Prev. 2016;17(1):381–6.

    Article  PubMed  Google Scholar 

  4. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32. https://doi.org/10.3322/caac.21338.

    Article  PubMed  Google Scholar 

  5. Advanced Bladder Cancer Meta-analysis C. Neoadjuvant chemotherapy in invasive bladder cancer: update of a systematic review and meta-analysis of individual patient data advanced bladder cancer (ABC) meta-analysis collaboration. Eur Urol. 2005;48(2):202–5. https://doi.org/10.1016/j.eururo.2005.04.006.

    Article  Google Scholar 

  6. Koshkin VS, Grivas P. Emerging role of immunotherapy in advanced urothelial carcinoma. Curr Oncol Rep. 2018;20(6):48. https://doi.org/10.1007/s11912-018-0693-y.

    Article  CAS  PubMed  Google Scholar 

  7. Felsenstein KM, Theodorescu D. Precision medicine for urothelial bladder cancer: update on tumour genomics and immunotherapy. Nat Rev Urol. 2018;15(2):92–111. https://doi.org/10.1038/nrurol.2017.179.

    Article  CAS  PubMed  Google Scholar 

  8. Theodora Katsila ML, Patrinos GP, Aristotelis B, Dimitrios K. The new age of -omics in urothelial cancer—re-wording its diagnosis and treatment. EBioMedicine. 2018;28:43–50. https://doi.org/10.1016/j.ebiom.2018.01.044.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. https://doi.org/10.1038/ncomms5006.

    Article  CAS  PubMed  Google Scholar 

  10. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6. https://doi.org/10.1016/j.ejca.2011.11.036.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62. https://doi.org/10.1038/nrclinonc.2017.141.

    Article  PubMed  Google Scholar 

  12. Neri E, Del Re M, Paiar F, Erba P, Cocuzza P, Regge D, et al. Radiomics and liquid biopsy in oncology: the holons of systems medicine. Insights Imaging. 2018;9(6):915–24. https://doi.org/10.1007/s13244-018-0657-7.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Spiess PE, Agarwal N, Bangs R, Boorjian SA, Buyyounouski MK, Clark PE, et al. Bladder cancer, version 5.2017, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Cancer Netw. 2017;15(10):1240–67. https://doi.org/10.6004/jnccn.2017.0156.

    Article  Google Scholar 

  14. Zhang G-M-Y, Sun H, Shi B, Jin Z-Y, Xue H-D. Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. Abdom Radiol. 2016;42(2):561–8. https://doi.org/10.1007/s00261-016-0897-2.

    Article  Google Scholar 

  15. Mammen S, Krishna S, Quon M, Shabana WM, Hakim SW, Flood TA, et al. Diagnostic accuracy of qualitative and quantitative computed tomography analysis for diagnosis of pathological grade and stage in upper tract urothelial cell carcinoma. J Comput Assist Tomogr. 2018;42(2):204–10. https://doi.org/10.1097/RCT.0000000000000664.

    Article  PubMed  Google Scholar 

  16. Zhang X, Xu X, Tian Q, Li B, Wu Y, Yang Z, et al. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging. 2017;46(5):1281–8. https://doi.org/10.1002/jmri.25669.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wang H, Hu D, Yao H, Chen M, Li S, Chen H, et al. Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors. Eur Radiol. 2019. https://doi.org/10.1007/s00330-019-06222-8.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Nishiyama H. Asia consensus statement on NCCN clinical practice guideline for bladder cancer. Jpn J Clin Oncol. 2018;48(1):3–6. https://doi.org/10.1093/jjco/hyx130.

    Article  PubMed  Google Scholar 

  19. Garapati SS, Hadjiiski L, Cha KH, Chan H-P, Caoili EM, Cohan RH, et al. Urinary bladder cancer staging in CT urography using machine learning. Med Phys. 2017;44(11):5814–23. https://doi.org/10.1002/mp.12510.

    Article  PubMed  Google Scholar 

  20. Xu X, Zhang X, Tian Q, Wang H, Cui LB, Li S, et al. Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis. J Magn Reson Imaging. 2019;49(5):1489–98. https://doi.org/10.1002/jmri.26327.

    Article  PubMed  Google Scholar 

  21. Tong Y, Udupa JK, Wang C, Chen J, Venigalla S, Guzzo TJ, et al. Radiomics-guided therapy for bladder cancer: using an optimal biomarker approach to determine extent of bladder cancer invasion from t2-weighted magnetic resonance images. Adv Radiat Oncol. 2018;3(3):331–8. https://doi.org/10.1016/j.adro.2018.04.011.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Lim CS, Tirumani S, van der Pol CB, Alessandrino F, Sonpavde GP, Silverman SG, et al. Use of quantitative T2-weighted and apparent diffusion coefficient texture features of bladder cancer and extravesical fat for local tumor staging after transurethral resection. Am J Roentgenol. 2019;212(5):1060–9. https://doi.org/10.2214/ajr.18.20718.

    Article  Google Scholar 

  23. Abol-Enein H, Tilki D, Mosbah A, El-Baz M, Shokeir A, Nabeeh A, et al. Does the extent of lymphadenectomy in radical cystectomy for bladder cancer influence disease-free survival? A prospective single-center study. Eur Urol. 2011;60(3):572–7. https://doi.org/10.1016/j.eururo.2011.05.062.

    Article  PubMed  Google Scholar 

  24. Zehnder P, Studer UE, Skinner EC, Dorin RP, Cai J, Roth B, et al. Super extended versus extended pelvic lymph node dissection in patients undergoing radical cystectomy for bladder cancer: a comparative study. J Urol. 2011;186(4):1261–8. https://doi.org/10.1016/j.juro.2011.06.004.

    Article  PubMed  Google Scholar 

  25. Wu S, Zheng J, Li Y, Wu Z, Shi S, Huang M, et al. Development and validation of an MRI-based radiomics signature for the preoperative prediction of lymph node metastasis in bladder cancer. EBioMedicine. 2018;34:76–84. https://doi.org/10.1016/j.ebiom.2018.07.029.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wu S, Zheng J, Li Y, Yu H, Shi S, Xie W, et al. A Radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res. 2017;23(22):6904–11. https://doi.org/10.1158/1078-0432.Ccr-17-1510.

    Article  CAS  PubMed  Google Scholar 

  27. Soukup V, Capoun O, Cohen D, Hernandez V, Burger M, Comperat E, et al. Risk stratification tools and prognostic models in non-muscle-invasive bladder cancer: a Critical Assessment from the European Association of Urology Non-muscle-invasive Bladder Cancer Guidelines Panel. Eur Urol Focus. 2018. https://doi.org/10.1016/j.euf.2018.11.005.

    Article  PubMed  Google Scholar 

  28. Xu X, Wang H, Du P, Zhang F, Li S, Zhang Z, et al. A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors. J Magn Reson Imaging. 2019. https://doi.org/10.1002/jmri.26749.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Meeks JJ, Bellmunt J, Bochner BH, Clarke NW, Daneshmand S, Galsky MD, et al. A systematic review of neoadjuvant and adjuvant chemotherapy for muscle-invasive bladder cancer. Eur Urol. 2012;62(3):523–33. https://doi.org/10.1016/j.eururo.2012.05.048.

    Article  CAS  PubMed  Google Scholar 

  30. Cha KH, Hadjiiski L, Chan H-P, Weizer AZ, Alva A, Cohan RH, et al. Bladder cancer treatment response assessment in CT using radiomics with deep-learning. Sci Rep. 2017. https://doi.org/10.1038/s41598-017-09315-w.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Vickers AJ. Prediction models: revolutionary in principle, but do they do more good than harm? J Clin Oncol. 2011;29(22):2951–2. https://doi.org/10.1200/JCO.2011.36.1329.

    Article  PubMed  Google Scholar 

  32. Rutman AM, Kuo MD. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol. 2009;70(2):232–41. https://doi.org/10.1016/j.ejrad.2009.01.050.

    Article  PubMed  Google Scholar 

  33. Blaveri E, Brewer JL, Roydasgupta R, Fridlyand J, DeVries S, Koppie T, et al. Bladder cancer stage and outcome by array-based comparative genomic hybridization. Clin Cancer Res. 2005;11(19 Pt 1):7012–22. https://doi.org/10.1158/1078-0432.CCR-05-0177.

    Article  CAS  PubMed  Google Scholar 

  34. Blaveri E, Simko JP, Korkola JE, Brewer JL, Baehner F, Mehta K, et al. Bladder cancer outcome and subtype classification by gene expression. Clin Cancer Res. 2005;11(11):4044–55. https://doi.org/10.1158/1078-0432.CCR-04-2409.

    Article  CAS  PubMed  Google Scholar 

  35. Birkhahn M, Mitra AP, Williams AJ, Lam G, Ye W, Datar RH, et al. Predicting recurrence and progression of noninvasive papillary bladder cancer at initial presentation based on quantitative gene expression profiles. Eur Urol. 2010;57(1):12–20. https://doi.org/10.1016/j.eururo.2009.09.013.

    Article  CAS  PubMed  Google Scholar 

  36. Catto JWF, Abbod MF, Wild PJ, Linkens DA, Pilarsky C, Rehman I, et al. The application of artificial intelligence to microarray data: identification of a novel gene signature to identify bladder cancer progression. Eur Urol. 2010;57(3):398–406. https://doi.org/10.1016/j.eururo.2009.10.029.

    Article  CAS  PubMed  Google Scholar 

  37. Smith SC, Baras AS, Dancik G, Ru YB, Ding KF, Moskaluk CA, et al. A 20-gene model for molecular nodal staging of bladder cancer: development and prospective assessment. Lancet Oncol. 2011;12(2):137–43. https://doi.org/10.1016/S1470-2045(10)70296-5.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Powles T, Smith K, Stenzl A, Bedke J. Immune checkpoint inhibition in metastatic urothelial cancer. Eur Urol. 2017;72(4):477–81. https://doi.org/10.1016/j.eururo.2017.03.047.

    Article  PubMed  Google Scholar 

  39. Rijnders M, de Wit R, Boormans JL, Lolkema MPJ, van der Veldt AAM. Systematic review of immune checkpoint inhibition in urological cancers. Eur Urol. 2017;72(3):411–23. https://doi.org/10.1016/j.eururo.2017.06.012.

    Article  CAS  PubMed  Google Scholar 

  40. Buder-Bakhaya K, Hassel JC. Biomarkers for Clinical Benefit of Immune Checkpoint Inhibitor Treatment-A Review From the Melanoma Perspective and Beyond. Front Immunol. 2018;9:1474. https://doi.org/10.3389/fimmu.2018.01474.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol. 2018;19(9):1180–91. https://doi.org/10.1016/s1470-2045(18)30413-3.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was funded by the Fundamental Research Funds for the Central Universities (Grant No. 3332018022); Beijing Municipal Natural Science Foundation (Grant No. 7192176); Basic Scientific Research Program of Chinese Academy of Medical Sciences (Grant Nos. 2019PT320008 and 2018PT32003); and National Natural Science Foundation of China (Grant No. 81901742).

Author information

Authors and Affiliations

  1. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, People’s Republic of China

    Gumuyang Zhang, Lili Xu, Hao Sun & Zhengyu Jin

Authors
  1. Gumuyang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lili Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhengyu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hao Sun or Zhengyu Jin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Zhang, G., Xu, L., Sun, H. et al. Current applications and challenges of radiomics in urothelial cancer. Chin J Acad Radiol 2, 56–62 (2020). https://doi.org/10.1007/s42058-019-00021-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42058-019-00021-2

Keywords

Search

Navigation