Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer

World Journal of Urology volume 38, pages2329–2347(2020)Cite this article

Abstract

Purpose

The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date.

Methods

A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa).

Results

In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods.

Conclusions

The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.

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References

  1. 1.

    Nuffield Council on Bioethics (2018) Bioethics briefing notes: artificial intelligence (AI) in healthcare and research. https://nuffieldbioethics.org/wp-content/uploads/Artificial-Intelligence-AI-in-healthcare-and-research.pdf. Accessed 21 Dec 2018

  2. 2.

    Frankish K, Ramsey WM (eds) (2014) Introduction. The Cambridge handbook of artificial intelligence. Cambridge University Press, Cambridge, pp 1–14

    Google Scholar 

  3. 3.

    Stuart R, Norvig P (eds) (2010) Artificial intelligence—a modern approach, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  4. 4.

    Tran BX et al (2019) Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J Clin Med 8(3):360

    PubMed Central  Google Scholar 

  5. 5.

    Goldenberg SL, Nir G, Salcudean SE (2019) A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol 16(7):391–403

    PubMed  Google Scholar 

  6. 6.

    Yu KH, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2(10):719–731

    PubMed  Google Scholar 

  7. 7.

    Curran Associates Inc. (2014) Advances in neural information processing systems 26: 27th annual conference on neural information processing systems 2014, December 8–13. Curran Associates Inc., vol 1

  8. 8.

    Abbod MF et al (2007) Application of artificial intelligence to the management of urological cancer. J Urol 178(4 Pt 1):1150–1156

    PubMed  Google Scholar 

  9. 9.

    Kadlec AO et al (2014) Nonlinear logistic regression model for outcomes after endourologic procedures: a novel predictor. Urolithiasis 42(4):323–327

    PubMed  Google Scholar 

  10. 10.

    Aminsharifi A et al (2017) Artificial neural network system to predict the postoperative outcome of percutaneous nephrolithotomy. J Endourol 31(5):461–467

    PubMed  Google Scholar 

  11. 11.

    Choo MS et al (2018) A prediction model using machine learning algorithm for assessing stone-free status after single session shock wave lithotripsy to treat ureteral stones. J Urol 200(6):1371–1377

    PubMed  Google Scholar 

  12. 12.

    Mannil M et al (2018) Prediction of successful shock wave lithotripsy with CT: a phantom study using texture analysis. Abdom Radiol (NY) 43(6):1432–1438

    PubMed  Google Scholar 

  13. 13.

    Mannil M et al (2018) Three-dimensional texture analysis with machine learning provides incremental predictive information for successful shock wave lithotripsy in patients with kidney stones. J Urol 200(4):829–836

    PubMed  Google Scholar 

  14. 14.

    Seckiner I et al (2017) A neural network-based algorithm for predicting stone-free status after ESWL therapy. Int Braz J Urol 43(6):1110–1114

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Langkvist M et al (2018) Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks. Comput Biol Med 97:153–160

    PubMed  Google Scholar 

  16. 16.

    Kazemi Y, Mirroshandel SA (2018) A novel method for predicting kidney stone type using ensemble learning. Artif Intell Med 84:117–126

    PubMed  Google Scholar 

  17. 17.

    Richard PO et al (2015) Renal tumor biopsy for small renal masses: a single-center 13-year experience. Eur Urol 68(6):1007–1013

    PubMed  Google Scholar 

  18. 18.

    Mir MC et al (2018) Role of active surveillance for localized small renal masses. Eur Urol Oncol 1(3):177–187

    PubMed  Google Scholar 

  19. 19.

    Bektas CT et al (2019) Clear cell renal cell carcinoma: machine learning-based quantitative computed tomography texture analysis for prediction of Fuhrman nuclear grade. Eur Radiol 29(3):1153–1163

    PubMed  Google Scholar 

  20. 20.

    Kocak B et al (2018) Textural differences between renal cell carcinoma subtypes: machine learning-based quantitative computed tomography texture analysis with independent external validation. Eur J Radiol 107:149–157

    PubMed  Google Scholar 

  21. 21.

    Kanapuli G et al (2018) A decision-support tool for renal mass classification. J Digit Imaging 31(6):929–939

    Google Scholar 

  22. 22.

    Yu H et al (2017) Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol (NY) 42(10):2470–2478

    Google Scholar 

  23. 23.

    Yan L et al (2015) Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images. Acad Radiol 22(9):1115–1121

    PubMed  Google Scholar 

  24. 24.

    Feng Z et al (2018) Machine learning-based quantitative texture analysis of CT images of small renal masses: differentiation of angiomyolipoma without visible fat from renal cell carcinoma. Eur Radiol 28(4):1625–1633

    PubMed  Google Scholar 

  25. 25.

    Cui EM et al (2019) Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features. Acta Radiol 60(11):1543–1552

    PubMed  Google Scholar 

  26. 26.

    Coy H et al (2019) Deep learning and radiomics: the utility of Google TensorFlow Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT. Abdom Radiol 44(6):2009–2020

    Google Scholar 

  27. 27.

    Minardi D et al (2005) Prognostic role of Fuhrman grade and vascular endothelial growth factor in pT1a clear cell carcinoma in partial nephrectomy specimens. J Urol 174(4 Pt 1):1208–1212

    CAS  PubMed  Google Scholar 

  28. 28.

    Holdbrook DA et al (2018) Automated renal cancer grading using nuclear pleomorphic patterns. JCO Clin Cancer Inform 2:1–12

    PubMed  Google Scholar 

  29. 29.

    Ding J et al (2018) CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur J Radiol 103:51–56

    PubMed  Google Scholar 

  30. 30.

    Kocak B et al (2019) Unenhanced CT texture analysis of clear cell renal cell carcinomas: a machine learning-based study for predicting histopathologic nuclear grade. AJR Am J Roentgenol 212:W1–W8

    Google Scholar 

  31. 31.

    Lin F et al (2019) CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma. Abdom Radiol 44(7):2528–2534

    Google Scholar 

  32. 32.

    Sun X et al (2019) Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images. Medicine (Baltimore) 98(14):e15022

    Google Scholar 

  33. 33.

    Li P et al (2018) Fifteen-gene expression based model predicts the survival of clear cell renal cell carcinoma. Medicine (Baltimore) 97(33):e11839

    CAS  Google Scholar 

  34. 34.

    Kocak B et al (2019) Radiogenomics in clear cell renal cell carcinoma: machine learning-based high-dimensional quantitative CT texture analysis in predicting PBRM1 mutation status. AJR Am J Roentgenol 212(3):W55–W63

    PubMed  Google Scholar 

  35. 35.

    Xu X 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 CARS 12(4):645–656

    Google Scholar 

  36. 36.

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

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Eminaga O et al (2018) Diagnostic classification of cystoscopic images using deep convolutional neural networks. JCO Clin Cancer Inform 2:1–8

    PubMed  Google Scholar 

  38. 38.

    Sokolov I et al (2018) Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: detection of bladder cancer. Proc Natl Acad Sci USA 115(51):12920–12925

    CAS  PubMed  Google Scholar 

  39. 39.

    Brieu N et al (2019) Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis. Sci Rep 9(1):5174

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Hasnain Z et al (2019) Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients. PLoS ONE 14(2):e0210976

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Bartsch G Jr et al (2016) Use of artificial intelligence and machine learning algorithms with gene expression profiling to predict recurrent nonmuscle invasive urothelial carcinoma of the bladder. J Urol 195(2):493–498

    PubMed  Google Scholar 

  42. 42.

    Wu E et al (2019) Deep learning approach for assessment of bladder cancer treatment response. Tomography 5(1):201–208

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Cha KH et al (2018) Diagnostic accuracy of CT for prediction of bladder cancer treatment response with and without computerized decision support. Acad Radiol 26:1137–1145

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Takeuchi T et al (2019) Prediction of prostate cancer by deep learning with multilayer artificial neural network. Can Urol Assoc J 13(5):E145–E150

    PubMed  Google Scholar 

  45. 45.

    Zhang YD et al (2016) An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification. Oncotarget 7(47):78140–78151

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Ishioka J et al (2018) Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm. BJU Int 122(3):411–417

    PubMed  Google Scholar 

  47. 47.

    Bonekamp D et al (2018) Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values. Radiology 289(1):128–137

    PubMed  Google Scholar 

  48. 48.

    Arvaniti E et al (2018) Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep 8(1):12054

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Donovan MJ et al (2018) Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test. Prostate Cancer Prostatic Dis 21(4):594–603

    CAS  PubMed  Google Scholar 

  50. 50.

    Auffenberg GB et al (2019) askMUSIC: leveraging a clinical registry to develop a new machine learning model to inform patients of prostate cancer treatments chosen by similar men. Eur Urol 75(6):901–907

    PubMed  Google Scholar 

  51. 51.

    Abdollahi H et al (2019) Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer. Radiol Med 124(6):555–567

    PubMed  Google Scholar 

  52. 52.

    Hung AJ et al (2018) Utilizing machine learning and automated performance metrics to evaluate robot-assisted radical prostatectomy performance and predict outcomes. J Endourol 32(5):438–444

    PubMed  Google Scholar 

  53. 53.

    Hung AJ et al (2019) A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy. BJU Int 124(3):487–495

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Wong NC et al (2019) Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy. BJU Int 123(1):51–57

    PubMed  Google Scholar 

  55. 55.

    Chen J et al (2019) Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU Int [Epub ahead of print]

  56. 56.

    Goldenberg SL, Nir G, Salcudean SE (2019) A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol 16(7):391–403

    PubMed  Google Scholar 

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Funding

This research received no financial or other support.

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Affiliations

  1. Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany

    Rodrigo Suarez-Ibarrola, Simon Hein, Christian Gratzke & Arkadiusz Miernik

  2. Department Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Germany

    Gerd Reis

Authors
  1. Rodrigo Suarez-Ibarrola
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  2. Simon Hein
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  4. Christian Gratzke
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  5. Arkadiusz Miernik
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Contributions

Project development: RS and AM. Literature review and data extraction: RS. Manuscript drafting: RS, GR, and AM. Manuscript editing: SH, CG, and AM.

Corresponding author

Correspondence to Rodrigo Suarez-Ibarrola.

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As this is a review of the literature, no ethical approval was necessary.

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Suarez-Ibarrola, R., Hein, S., Reis, G. et al. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol 38, 2329–2347 (2020). https://doi.org/10.1007/s00345-019-03000-5

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Keywords

  • Artificial intelligence
  • Machine learning
  • Deep learning
  • Artificial neural network
  • Convolutional neural network
  • Prostate cancer
  • Bladder cancer
  • Renal cell carcinoma
  • Urolithiasis