Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Bioinformatics in urology — molecular characterization of pathophysiology and response to treatment

Nature Reviews Urology volume 21, pages 214–242 (2024)Cite this article

Subjects

Abstract

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes — which enables risk stratification — informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

Key points

  • Retrospective classification of tumours using novel bioinformatics approaches has provided unprecedented insights into the molecular basis of urological cancers.

  • Molecular classifiers provide a useful adjunct to standard-of-care for the management of urological cancers, but prospective prediction of treatment response using molecular classifiers is not yet applied routinely.

  • Machine learning (ML) and artificial intelligence (AI) algorithms might circumvent the challenge of inter-observer variability in histopathology and could be incorporated into routine clinical practice.

  • Benign urology and functional urological disorders require improved patient phenotyping to fully realize the power of bioinformatics, as observed in oncology.

  • Incorporation of ML and AI approaches into routine clinical practice will require adherence to best practices including transparency in reporting results, and external validation in independent samples or patient cohorts before implementation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Understanding AI methods.
Fig. 2: Overview of AI methods.
Fig. 3: Bioinformatics tools related to single-cell technologies.

Similar content being viewed by others

References

  1. Goldenberg, S. L., Nir, G. & Salcudean, S. E. A new era: artificial intelligence and machine learning in prostate cancer. Nat. Rev. Urol. 16, 391–403 (2019).

    Article  PubMed  Google Scholar 

  2. Bentellis, I., Guerin, S., Khene, Z. E., Khavari, R. & Peyronnet, B. Artificial intelligence in functional urology: how it may shape the future. Curr. Opin. Urol. 31, 385–390 (2021).

    Article  PubMed  Google Scholar 

  3. Brodie, A. et al. Artificial intelligence in urological oncology: an update and future applications. Urol. Oncol. 39, 379–399 (2021).

    Article  PubMed  Google Scholar 

  4. Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat. Methods 15, 233–234 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sidey-Gibbons, J. A. M. & Sidey-Gibbons, C. J. Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19, 64 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lo Vercio, L. et al. Supervised machine learning tools: a tutorial for clinicians. J. Neural Eng. 17, 062001 (2020).

    Article  Google Scholar 

  8. Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Jolliffe, I. T. & Cadima, J. Principal component analysis: a review and recent developments. Philos. Trans. A Math. Phys. Eng. Sci. 374, 20150202 (2016).

    PubMed  PubMed Central  Google Scholar 

  10. Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P. & Green, R. Artificial intelligence and machine learning in pathology: the present landscape of supervised methods. Acad. Pathol. 6, 2374289519873088 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Quinn, T. P., Nguyen, T., Lee, S. C. & Venkatesh, S. Cancer as a tissue anomaly: classifying tumor transcriptomes based only on healthy data. Front. Genet. 10, 599 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Yakimovich, A., Beaugnon, A., Huang, Y. & Ozkirimli, E. Labels in a haystack: approaches beyond supervised learning in biomedical applications. Patterns 2, 100383 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Eckardt, J. N., Bornhauser, M., Wendt, K. & Middeke, J. M. Semi-supervised learning in cancer diagnostics. Front. Oncol. 12, 960984 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Bulten, W. et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 21, 233–241 (2020).

    Article  PubMed  Google Scholar 

  15. Marini, N., Otalora, S., Muller, H. & Atzori, M. Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: an experiment on prostate histopathology image classification. Med. Image Anal. 73, 102165 (2021).

    Article  PubMed  Google Scholar 

  16. Doan, S., Conway, M., Phuong, T. M. & Ohno-Machado, L. Natural language processing in biomedicine: a unified system architecture overview. Methods Mol. Biol. 1168, 275–294 (2014).

    Article  PubMed  Google Scholar 

  17. Finne, P. et al. Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network. Urology 56, 418–422 (2000).

    Article  CAS  PubMed  Google Scholar 

  18. Remzi, M. et al. An artificial neural network to predict the outcome of repeat prostate biopsies. Urology 62, 456–460 (2003).

    Article  PubMed  Google Scholar 

  19. Greener, J. G., Kandathil, S. M., Moffat, L. & Jones, D. T. A guide to machine learning for biologists. Nat. Rev. Mol. Cell Biol. 23, 40–55 (2022).

    Article  CAS  PubMed  Google Scholar 

  20. Chen, A. B. et al. Artificial intelligence applications in urology: reporting standards to achieve fluency for urologists. Urol. Clin. North. Am. 49, 65–117 (2022).

    Article  PubMed  Google Scholar 

  21. Thykjaer, T. et al. Identification of gene expression patterns in superficial and invasive human bladder cancer. Cancer Res. 61, 2492–2499 (2001).

    CAS  PubMed  Google Scholar 

  22. Dhanasekaran, S. M. et al. Delineation of prognostic biomarkers in prostate cancer. Nature 412, 822–826 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Luo, J. et al. Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. Cancer Res. 61, 4683–4688 (2001).

    CAS  PubMed  Google Scholar 

  24. Singh, D. et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203–209 (2002).

    Article  CAS  PubMed  Google Scholar 

  25. Dyrskjot, L. et al. Identifying distinct classes of bladder carcinoma using microarrays. Nat. Genet. 33, 90–96 (2003).

    Article  CAS  PubMed  Google Scholar 

  26. Blaveri, E. et al. Bladder cancer outcome and subtype classification by gene expression. Clin. Cancer Res. 11, 4044–4055 (2005).

    Article  CAS  PubMed  Google Scholar 

  27. Rhodes, D. R. et al. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6, 1–6 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. The Cancer Genome Atlas Research Network Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507, 315–322 (2014).

    Article  Google Scholar 

  29. The Cancer Genome Atlas Research Network The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).

    Article  PubMed Central  Google Scholar 

  30. Shen, H. et al. Integrated molecular characterization of testicular germ cell tumors. Cell Rep. 23, 3392–3406 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).

    Article  PubMed Central  Google Scholar 

  32. The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of papillary renal-cell carcinoma. N. Engl. J. Med. 374, 135–145 (2016).

    Article  Google Scholar 

  33. Veldman-Jones, M. H. et al. Evaluating robustness and sensitivity of the NanoString Technologies nCounter platform to enable multiplexed gene expression analysis of clinical samples. Cancer Res. 75, 2587–2593 (2015).

    Article  CAS  PubMed  Google Scholar 

  34. Zheng, H. et al. Comprehensive review of web servers and bioinformatics tools for cancer prognosis analysis. Front. Oncol. 10, 68 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  PubMed  Google Scholar 

  36. Chandrashekar, D. S. et al. UALCAN: an update to the integrated cancer data analysis platform. Neoplasia 25, 18–27 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Chen, M. M. et al. TCPA v3.0: an integrative platform to explore the pan-cancer analysis of functional proteomic data. Mol. Cell Proteom. 18, S15–S25 (2019).

    Article  CAS  Google Scholar 

  38. Navin, N. E. The first five years of single-cell cancer genomics and beyond. Genome Res. 25, 1499–1507 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308.e36 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Su, F. et al. Multimodal single-cell analyses outline the immune microenvironment and therapeutic effectors of interstitial cystitis/bladder pain syndrome. Adv. Sci. 9, e2106063 (2022).

    Article  Google Scholar 

  43. Peng, L. et al. Integrating single-cell RNA sequencing with spatial transcriptomics reveals immune landscape for interstitial cystitis. Signal Transduct. Target. Ther. 7, 161 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Henry, G. H. et al. A cellular anatomy of the normal adult human prostate and prostatic urethra. Cell Rep. 25, 3530–3542.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Karthaus, W. R. et al. Regenerative potential of prostate luminal cells revealed by single-cell analysis. Science 368, 497–505 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Zurauskiene, J. & Yau, C. pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinforma. 17, 140 (2016).

    Article  Google Scholar 

  48. Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Xu, C. & Su, Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31, 1974–1980 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Clarke, Z. A. et al. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat. Protoc. 16, 2749–2764 (2021).

    Article  CAS  PubMed  Google Scholar 

  51. Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zhang, Y. et al. Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response. Proc. Natl Acad. Sci. USA 118, e2103240118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Dong, B. et al. Single-cell analysis supports a luminal-neuroendocrine transdifferentiation in human prostate cancer. Commun. Biol. 3, 778 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Song, H. et al. Single-cell analysis of human primary prostate cancer reveals the heterogeneity of tumor-associated epithelial cell states. Nat. Commun. 13, 141 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Haghverdi, L., Buttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    Article  CAS  PubMed  Google Scholar 

  57. Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943.e2 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Chen, S. et al. Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression. Nat. Cell Biol. 23, 87–98 (2021).

    Article  CAS  PubMed  Google Scholar 

  60. Chen, Z. et al. Single-cell RNA sequencing highlights the role of inflammatory cancer-associated fibroblasts in bladder urothelial carcinoma. Nat. Commun. 11, 5077 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Wu, T., Wu, X., Wang, H. Y. & Chen, L. Immune contexture defined by single cell technology for prognosis prediction and immunotherapy guidance in cancer. Cancer Commun. 39, 21 (2019).

    Article  Google Scholar 

  62. Tuong, Z. K. et al. Resolving the immune landscape of human prostate at a single-cell level in health and cancer. Cell Rep. 37, 110132 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Chen, W. J. et al. Heterogeneity of tumor microenvironment is associated with clinical prognosis of non-clear cell renal cell carcinoma: a single-cell genomics study. Cell Death Dis. 13, 50 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Bi, K. et al. Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma. Cancer Cell 39, 649–661.e5 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Wang, L. et al. Myeloid cell-associated resistance to PD-1/PD-L1 blockade in urothelial cancer revealed through bulk and single-cell RNA sequencing. Clin. Cancer Res. 27, 4287–4300 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).

    Article  CAS  PubMed  Google Scholar 

  67. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Williams, C. G., Lee, H. J., Asatsuma, T., Vento-Tormo, R. & Haque, A. An introduction to spatial transcriptomics for biomedical research. Genome Med. 14, 68 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Longo, S. K., Guo, M. G., Ji, A. L. & Khavari, P. A. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat. Rev. Genet. 22, 627–644 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Rao, A., Barkley, D., Franca, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Dries, R. et al. Advances in spatial transcriptomic data analysis. Genome Res. 31, 1706–1718 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Tan, X., Su, A., Tran, M. & Nguyen, Q. SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells. Bioinformatics 36, 2293–2294 (2020).

    Article  CAS  PubMed  Google Scholar 

  73. Hu, J. et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–1351 (2021).

    Article  PubMed  Google Scholar 

  74. Edsgard, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Bergenstrahle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. 40, 476–479 (2022).

    Article  PubMed  Google Scholar 

  81. Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Berglund, E. et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat. Commun. 9, 2419 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Wang, Y., Ma, S. & Ruzzo, W. L. Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities. Sci. Rep. 10, 3490 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21, 130 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Lu, K. et al. Identification of novel biomarkers in Hunner’s interstitial cystitis using the CIBERSORT, an algorithm based on machine learning. BMC Urol. 21, 109 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Gamper, M. et al. Gene expression profile of bladder tissue of patients with ulcerative interstitial cystitis. BMC Genom. 10, 199 (2009).

    Article  Google Scholar 

  90. Colaco, M. et al. Correlation of gene expression with bladder capacity in interstitial cystitis/bladder pain syndrome. J. Urol. 192, 1123–1129 (2014).

    Article  CAS  PubMed  Google Scholar 

  91. Lindskrog, S. V. et al. An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer. Nat. Commun. 12, 2301 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Yu, L. et al. Prognostic significance of lineage diversity in bladder cancer revealed by single-cell sequencing. Front. Genet. 13, 862634 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Lopez, A. & Liao, J. C. Emerging endoscopic imaging technologies for bladder cancer detection. Curr. Urol. Rep. 15, 406 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Shkolyar, E. et al. Augmented bladder tumor detection using deep learning. Eur. Urol. 76, 714–718 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Ali, N. et al. Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors. Sci. Rep. 11, 11629 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Wu, S. et al. An artificial intelligence system for the detection of bladder cancer via cystoscopy: a multicenter diagnostic study. J. Natl Cancer Inst. 114, 220–227 (2022).

    Article  PubMed  Google Scholar 

  97. Negassi, M., Suarez-Ibarrola, R., Hein, S., Miernik, A. & Reiterer, A. Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects. World J. Urol. 38, 2349–2358 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Chan, E. O., Pradere, B. & Teoh, J. Y., European Association of Urology - Young Academic Urologists Urothelial Carcinoma Working Group The use of artificial intelligence for the diagnosis of bladder cancer: a review and perspectives. Curr. Opin. Urol. 31, 397–403 (2021).

    Article  PubMed  Google Scholar 

  99. Lenis, A. T. & Litwin, M. S. Does artificial intelligence meaningfully enhance cystoscopy. J. Natl Cancer Inst. 114, 174–175 (2022).

    Article  PubMed  Google Scholar 

  100. Sanghvi, A. B., Allen, E. Z., Callenberg, K. M. & Pantanowitz, L. Performance of an artificial intelligence algorithm for reporting urine cytopathology. Cancer Cytopathol. 127, 658–666 (2019).

    Article  PubMed  Google Scholar 

  101. Nojima, S. et al. A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens. Cancer Cytopathol. 129, 984–995 (2021).

    Article  CAS  PubMed  Google Scholar 

  102. Lebret, T. et al. Artificial intelligence to improve cytology performances in bladder carcinoma detection: results of the VisioCyt test. BJU Int. 129, 356–363 (2022).

    Article  PubMed  Google Scholar 

  103. Sanchez-Carbayo, M., Socci, N. D., Lozano, J., Saint, F. & Cordon-Cardo, C. Defining molecular profiles of poor outcome in patients with invasive bladder cancer using oligonucleotide microarrays. J. Clin. Oncol. 24, 778–789 (2006).

    Article  CAS  PubMed  Google Scholar 

  104. Kim, W. J. et al. Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer. Mol. Cancer 9, 3 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Lindgren, D. et al. Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome. Cancer Res. 70, 3463–3472 (2010).

    Article  CAS  PubMed  Google Scholar 

  106. Sjodahl, G. et al. A molecular taxonomy for urothelial carcinoma. Clin. Cancer Res. 18, 3377–3386 (2012).

    Article  PubMed  Google Scholar 

  107. Damrauer, J. S. et al. Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology. Proc. Natl Acad. Sci. USA 111, 3110–3115 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Choi, W. et al. Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell 25, 152–165 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Kardos, J. et al. Development and validation of a NanoString BASE47 bladder cancer gene classifier. PLoS ONE 15, e0243935 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Robertson, A. G. et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell 171, 540–556.e25 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Sjodahl, G., Eriksson, P., Liedberg, F. & Hoglund, M. Molecular classification of urothelial carcinoma: global mRNA classification versus tumour-cell phenotype classification. J. Pathol. 242, 113–125 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Batista da Costa, J. et al. Molecular characterization of neuroendocrine-like bladder cancer. Clin. Cancer Res. 25, 3908–3920 (2019).

    Article  CAS  PubMed  Google Scholar 

  113. de Jong, J. J. et al. Long non-coding RNAs identify a subset of luminal muscle-invasive bladder cancer patients with favorable prognosis. Genome Med. 11, 60 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Grivas, P. et al. Validation of a neuroendocrine-like classifier confirms poor outcomes in patients with bladder cancer treated with cisplatin-based neoadjuvant chemotherapy. Urol. Oncol. 38, 262–268 (2020).

    Article  CAS  PubMed  Google Scholar 

  115. Hedegaard, J. et al. Comprehensive transcriptional analysis of early-stage urothelial carcinoma. Cancer Cell 30, 27–42 (2016).

    Article  CAS  PubMed  Google Scholar 

  116. Hurst, C. D. et al. Genomic subtypes of non-invasive bladder cancer with distinct metabolic profile and female gender bias in KDM6A mutation frequency. Cancer Cell 32, 701–715.e7 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Kates, M. et al. Adaptive immune resistance to intravesical BCG in non-muscle invasive bladder cancer: implications for prospective BCG-unresponsive trials. Clin. Cancer Res. 26, 882–891 (2020).

    Article  CAS  PubMed  Google Scholar 

  118. Strandgaard, T. et al. Elevated T-cell exhaustion and urinary tumor DNA levels are associated with bacillus Calmette-Guérin failure in patients with non-muscle-invasive bladder cancer. Eur. Urol. 82, 646–656 (2022).

    Article  CAS  PubMed  Google Scholar 

  119. Kamoun, A. et al. A consensus molecular classification of muscle-invasive bladder cancer. Eur. Urol. 77, 420–433 (2020).

    Article  PubMed  Google Scholar 

  120. de Jong, J. J. et al. Gene expression profiling of muscle-invasive bladder cancer with secondary variant histology. Am. J. Clin. Pathol. 156, 895–905 (2021).

    Article  PubMed  Google Scholar 

  121. Lotan, Y. et al. Patients with muscle-invasive bladder cancer with nonluminal subtype derive greatest benefit from platinum based neoadjuvant chemotherapy. J. Urol. 207, 541–550 (2022).

    Article  PubMed  Google Scholar 

  122. Morera, D. S. et al. Clinical parameters outperform molecular subtypes for predicting outcome in bladder cancer: results from multiple cohorts, including TCGA. J. Urol. 203, 62–72 (2020).

    Article  PubMed  Google Scholar 

  123. Woerl, A. C. et al. Deep learning predicts molecular subtype of muscle-invasive bladder cancer from conventional histopathological slides. Eur. Urol. 78, 256–264 (2020).

    Article  CAS  PubMed  Google Scholar 

  124. Roubal, K., Myint, Z. W. & Kolesar, J. M. Erdafitinib: a novel therapy for FGFR-mutated urothelial cancer. Am. J. Health Syst. Pharm. 77, 346–351 (2020).

    Article  PubMed  Google Scholar 

  125. Loeffler, C. M. L. et al. Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: a possible preselection for molecular testing? Eur. Urol. Focus. 8, 472–479 (2021).

    Article  PubMed  Google Scholar 

  126. McConkey, D. J. et al. Therapeutic opportunities in the intrinsic subtypes of muscle-invasive bladder cancer. Hematol. Oncol. Clin. North. Am. 29, 377–394 (2015).

    Article  PubMed  Google Scholar 

  127. Motterle, G., Andrews, J. R., Morlacco, A. & Karnes, R. J. Predicting response to neoadjuvant chemotherapy in bladder cancer. Eur. Urol. Focus. 6, 642–649 (2020).

    Article  PubMed  Google Scholar 

  128. Takata, R. et al. Predicting response to methotrexate, vinblastine, doxorubicin, and cisplatin neoadjuvant chemotherapy for bladder cancers through genome-wide gene expression profiling. Clin. Cancer Res. 11, 2625–2636 (2005).

    Article  CAS  PubMed  Google Scholar 

  129. Kato, Y. et al. Predicting response of bladder cancers to gemcitabine and carboplatin neoadjuvant chemotherapy through genome-wide gene expression profiling. Exp. Ther. Med. 2, 47–56 (2011).

    Article  CAS  PubMed  Google Scholar 

  130. Als, A. B. et al. Emmprin and survivin predict response and survival following cisplatin-containing chemotherapy in patients with advanced bladder cancer. Clin. Cancer Res. 13, 4407–4414 (2007).

    Article  CAS  PubMed  Google Scholar 

  131. Kato, Y. et al. A prospective study to examine the accuracies and efficacies of prediction systems for response to neoadjuvant chemotherapy for muscle invasive bladder cancer. Oncol. Lett. 16, 5775–5784 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. McConkey, D. J. et al. A prognostic gene expression signature in the molecular classification of chemotherapy-naive urothelial cancer is predictive of clinical outcomes from neoadjuvant chemotherapy: a phase 2 trial of dose-dense methotrexate, vinblastine, doxorubicin, and cisplatin with bevacizumab in urothelial cancer. Eur. Urol. 69, 855–862 (2016).

    Article  CAS  PubMed  Google Scholar 

  133. Seiler, R. et al. Impact of molecular subtypes in muscle-invasive bladder cancer on predicting response and survival after neoadjuvant chemotherapy. Eur. Urol. 72, 544–554 (2017).

    Article  CAS  PubMed  Google Scholar 

  134. Moschini, M. et al. Characteristics and clinical significance of histological variants of bladder cancer. Nat. Rev. Urol. 14, 651–668 (2017).

    Article  PubMed  Google Scholar 

  135. Warrick, J. I. et al. Intratumoral heterogeneity of bladder cancer by molecular subtypes and histologic variants. Eur. Urol. 75, 18–22 (2019).

    Article  CAS  PubMed  Google Scholar 

  136. Sjodahl, G. et al. Molecular subtypes as a basis for stratified use of neoadjuvant chemotherapy for muscle-invasive bladder cancer – a narrative review. Cancers 14, 1692 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Bellmunt, J., Powles, T. & Vogelzang, N. J. A review on the evolution of PD-1/PD-L1 immunotherapy for bladder cancer: the future is now. Cancer Treat. Rev. 54, 58–67 (2017).

    Article  CAS  PubMed  Google Scholar 

  138. Rosenberg, J. E. et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387, 1909–1920 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Kim, J. et al. The Cancer Genome Atlas expression subtypes stratify response to checkpoint inhibition in advanced urothelial cancer and identify a subset of patients with high survival probability. Eur. Urol. 75, 961–964 (2019).

    Article  CAS  PubMed  Google Scholar 

  140. Korpal, M. et al. Evasion of immunosurveillance by genomic alterations of PPARγ/RXRα in bladder cancer. Nat. Commun. 8, 103 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  141. Necchi, A. et al. Impact of molecular subtyping and immune infiltration on pathological response and outcome following neoadjuvant pembrolizumab in muscle-invasive bladder cancer. Eur. Urol. 77, 701–710 (2020).

    Article  CAS  PubMed  Google Scholar 

  142. Havaleshko, D. M. et al. Prediction of drug combination chemosensitivity in human bladder cancer. Mol. Cancer Ther. 6, 578–586 (2007).

    Article  CAS  PubMed  Google Scholar 

  143. Lee, J. K. et al. A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery. Proc. Natl Acad. Sci. USA 104, 13086–13091 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Smith, S. C., Baras, A. S., Lee, J. K. & Theodorescu, D. The COXEN principle: translating signatures of in vitro chemosensitivity into tools for clinical outcome prediction and drug discovery in cancer. Cancer Res. 70, 1753–1758 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Flaig, T. W. et al. A randomized phase II study of coexpression extrapolation (COXEN) with neoadjuvant chemotherapy for bladder cancer (SWOG S1314; NCT02177695). Clin. Cancer Res. 27, 2435–2441 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Kong, J. et al. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nat. Commun. 11, 5485 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Barabasi, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Menche, J. et al. Disease networks. Uncovering disease–disease relationships through the incomplete interactome. Science 347, 1257601 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  149. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    Article  CAS  PubMed  Google Scholar 

  150. Cha, K. H. et al. Bladder cancer treatment response assessment in CT using radiomics with deep-learning. Sci. Rep. 7, 8738 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  151. Tomlins, S. A. et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310, 644–648 (2005).

    Article  CAS  PubMed  Google Scholar 

  152. Pettersson, A. et al. The TMPRSS2:ERG rearrangement, ERG expression, and prostate cancer outcomes: a cohort study and meta-analysis. Cancer Epidemiol. Biomark. Prev. 21, 1497–1509 (2012).

    Article  Google Scholar 

  153. Adamo, P. & Ladomery, M. R. The oncogene ERG: a key factor in prostate cancer. Oncogene 35, 403–414 (2016).

    Article  CAS  PubMed  Google Scholar 

  154. Rosen, P. et al. Clinical potential of the ERG oncoprotein in prostate cancer. Nat. Rev. Urol. 9, 131–137 (2012).

    Article  CAS  PubMed  Google Scholar 

  155. Taylor, B. S. et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 11–22 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. You, S. et al. Integrated classification of prostate cancer reveals a novel luminal subtype with poor outcome. Cancer Res. 76, 4948–4958 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Zhao, S. G. et al. Associations of luminal and basal subtyping of prostate cancer with prognosis and response to androgen deprivation therapy. JAMA Oncol. 3, 1663–1672 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  158. Signoretti, S. et al. p63 is a prostate basal cell marker and is required for prostate development. Am. J. Pathol. 157, 1769–1775 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Stoyanova, T. et al. Prostate cancer originating in basal cells progresses to adenocarcinoma propagated by luminal-like cells. Proc. Natl Acad. Sci. USA 110, 20111–20116 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Yoon, J. et al. A comparative study of PCS and PAM50 prostate cancer classification schemes. Prostate Cancer Prostatic Dis. 24, 733–742 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Mosley, J. D. & Keri, R. A. Cell cycle correlated genes dictate the prognostic power of breast cancer gene lists. BMC Med. Genom. 1, 11 (2008).

    Article  Google Scholar 

  162. Cuzick, J. et al. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol. 12, 245–255 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Whitfield, M. L. et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13, 1977–2000 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Cuzick, J. et al. Prognostic value of a cell cycle progression signature for prostate cancer death in a conservatively managed needle biopsy cohort. Br. J. Cancer 106, 1095–1099 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Erho, N. et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS ONE 8, e66855 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Karnes, R. J. et al. Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population. J. Urol. 190, 2047–2053 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Klein, E. A. et al. Decipher genomic classifier measured on prostate biopsy predicts metastasis risk. Urology 90, 148–152 (2016).

    Article  PubMed  Google Scholar 

  168. Klein, E. A. et al. A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur. Urol. 66, 550–560 (2014).

    Article  PubMed  Google Scholar 

  169. Van Den Eeden, S. K. et al. A biopsy-based 17-gene genomic prostate score as a predictor of metastases and prostate cancer death in surgically treated men with clinically localized disease. Eur. Urol. 73, 129–138 (2018).

    Article  PubMed  Google Scholar 

  170. Brooks, M. A. et al. GPS assay association with long-term cancer outcomes: twenty-year risk of distant metastasis and prostate cancer-specific mortality. JCO Precis. Oncol. 5, 325 (2021).

    Google Scholar 

  171. Hu, J. C. et al. Clinical utility of gene expression classifiers in men with newly diagnosed prostate cancer. JCO Precis. Oncol. 2, 163 (2018).

    Google Scholar 

  172. Fine, N. D., LaPolla, F., Epstein, M., Loeb, S. & Dani, H. Genomic classifiers for treatment selection in newly diagnosed prostate cancer. BJU Int. 124, 578–586 (2019).

    Article  PubMed  Google Scholar 

  173. Karnes, R. J. et al. Development and validation of a prostate cancer genomic signature that predicts early ADT treatment response following radical prostatectomy. Clin. Cancer Res. 24, 3908–3916 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Feng, F. Y. et al. Association of molecular subtypes with differential outcome to apalutamide treatment in nonmetastatic castration-resistant prostate cancer. JAMA Oncol. 7, 1005–1014 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  175. Smith, M. R. et al. Apalutamide treatment and metastasis-free survival in prostate cancer. N. Engl. J. Med. 378, 1408–1418 (2018).

    Article  CAS  PubMed  Google Scholar 

  176. Elmarakeby, H. A. et al. Biologically informed deep neural network for prostate cancer discovery. Nature 598, 348–352 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).

    Article  CAS  PubMed  Google Scholar 

  178. Snow, P. B., Smith, D. S. & Catalona, W. J. Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study. J. Urol. 152, 1923–1926 (1994).

    Article  CAS  PubMed  Google Scholar 

  179. Mosquera-Lopez, C., Agaian, S., Velez-Hoyos, A. & Thompson, I. Computer-aided prostate cancer diagnosis from digitized histopathology: a review on texture-based systems. IEEE Rev. Biomed. Eng. 8, 98–113 (2015).

    Article  PubMed  Google Scholar 

  180. Turkbey, B. & Haider, M. A. Deep learning-based artificial intelligence applications in prostate MRI: brief summary. Br. J. Radiol. 95, 20210563 (2022).

    Article  PubMed  Google Scholar 

  181. Suarez-Ibarrola, R. et al. Artificial intelligence in magnetic resonance imaging-based prostate cancer diagnosis: where do we stand in 2021? Eur. Urol. Focus. 8, 409–417 (2022).

    Article  PubMed  Google Scholar 

  182. Ferro, M. et al. Radiomics in prostate cancer: an up-to-date review. Ther. Adv. Urol. 14, 17562872221109020 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  183. Baydoun, A. et al. Artificial intelligence applications in prostate cancer. Prostate Cancer Prostatic Dis. https://doi.org/10.1038/s41391-023-00684-0 (2023).

  184. Doyle, S., Feldman, M., Tomaszewski, J. & Madabhushi, A. A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans. Biomed. Eng. 59, 1205–1218 (2012).

    Article  PubMed  Google Scholar 

  185. Berney, D. M. et al. The reasons behind variation in Gleason grading of prostatic biopsies: areas of agreement and misconception among 266 European pathologists. Histopathology 64, 405–411 (2014).

    Article  PubMed  Google Scholar 

  186. Nir, G. et al. Automatic grading of prostate cancer in digitized histopathology images: learning from multiple experts. Med. Image Anal. 50, 167–180 (2018).

    Article  PubMed  Google Scholar 

  187. Strom, P. et al. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 21, 222–232 (2020).

    Article  PubMed  Google Scholar 

  188. Pantanowitz, L. et al. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digit. Health 2, e407–e416 (2020).

    Article  PubMed  Google Scholar 

  189. Nagpal, K. et al. Development and validation of a deep learning algorithm for Gleason grading of prostate cancer from biopsy specimens. JAMA Oncol. 6, 1372–1380 (2020).

    Article  PubMed  Google Scholar 

  190. Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  191. Huang, W. et al. Development and validation of an artificial intelligence-powered platform for prostate cancer grading and quantification. JAMA Netw. Open. 4, e2132554 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  192. da Silva, L. M. et al. Independent real-world application of a clinical-grade automated prostate cancer detection system. J. Pathol. 254, 147–158 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  193. Pound, C. R. et al. Natural history of progression after PSA elevation following radical prostatectomy. JAMA 281, 1591–1597 (1999).

    Article  CAS  PubMed  Google Scholar 

  194. Wong, N. C., Lam, C., Patterson, L. & Shayegan, B. Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy. BJU Int. 123, 51–57 (2019).

    Article  PubMed  Google Scholar 

  195. Eksi, M. et al. Machine learning algorithms can more efficiently predict biochemical recurrence after robot-assisted radical prostatectomy. Prostate 81, 913–920 (2021).

    Article  PubMed  Google Scholar 

  196. Tan, Y. G. et al. Incorporating artificial intelligence in urology: supervised machine learning algorithms demonstrate comparative advantage over nomograms in predicting biochemical recurrence after prostatectomy. Prostate 82, 298–305 (2022).

    Article  CAS  PubMed  Google Scholar 

  197. Cheng, L. et al. Risk of prostate carcinoma death in patients with lymph node metastasis. Cancer 91, 66–73 (2001).

    Article  CAS  PubMed  Google Scholar 

  198. Gandaglia, G. et al. A novel nomogram to identify candidates for extended pelvic lymph node dissection among patients with clinically localized prostate cancer diagnosed with magnetic resonance imaging-targeted and systematic biopsies. Eur. Urol. 75, 506–514 (2019).

    Article  PubMed  Google Scholar 

  199. Luzzago, S. et al. A novel nomogram to identify candidates for active surveillance amongst patients with International Society of Urological Pathology (ISUP) grade group (GG) 1 or ISUP GG2 prostate cancer, according to multiparametric magnetic resonance imaging findings. BJU Int. 126, 104–113 (2020).

    Article  CAS  PubMed  Google Scholar 

  200. Wessels, F. et al. Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer. BJU Int. 128, 352–360 (2021).

    Article  CAS  PubMed  Google Scholar 

  201. Davis, C. F. et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell 26, 319–330 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  202. Ricketts, C. J. et al. The Cancer Genome Atlas comprehensive molecular characterization of renal cell carcinoma. Cell Rep. 23, 313–326.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  203. Linehan, W. M. & Ricketts, C. J. The Cancer Genome Atlas of renal cell carcinoma: findings and clinical implications. Nat. Rev. Urol. 16, 539–552 (2019).

    Article  CAS  PubMed  Google Scholar 

  204. Beuselinck, B. et al. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin. Cancer Res. 21, 1329–1339 (2015).

    Article  CAS  PubMed  Google Scholar 

  205. Rini, B. et al. A 16-gene assay to predict recurrence after surgery in localised renal cell carcinoma: development and validation studies. Lancet Oncol. 16, 676–685 (2015).

    Article  CAS  PubMed  Google Scholar 

  206. Motzer, R. J. et al. Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade. Cancer Cell 38, 803–817.e4 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. Buttner, F. A. et al. A novel molecular signature identifies mixed subtypes in renal cell carcinoma with poor prognosis and independent response to immunotherapy. Genome Med. 14, 105 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  208. Motzer, R. J. et al. Molecular characterization of renal cell carcinoma tumors from a phase III anti-angiogenic adjuvant therapy trial. Nat. Commun. 13, 5959 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  209. Rini, B. I. et al. Validation of the 16-gene recurrence score in patients with locoregional, high-risk renal cell carcinoma from a phase III trial of adjuvant sunitinib. Clin. Cancer Res. 24, 4407–4415 (2018).

    Article  CAS  PubMed  Google Scholar 

  210. Rini, B. I. et al. Atezolizumab plus bevacizumab versus sunitinib in patients with previously untreated metastatic renal cell carcinoma (IMmotion151): a multicentre, open-label, phase 3, randomised controlled trial. Lancet 393, 2404–2415 (2019).

    Article  PubMed  Google Scholar 

  211. McDaniel, A. S. et al. Genomic profiling of penile squamous cell carcinoma reveals new opportunities for targeted therapy. Cancer Res. 75, 5219–5227 (2015).

    Article  CAS  PubMed  Google Scholar 

  212. Necchi, A. et al. Gene expression profiling of advanced penile squamous cell carcinoma receiving cisplatin-based chemotherapy improves prognostication and identifies potential therapeutic targets. Eur. Urol. Focus. 4, 733–736 (2018).

    Article  PubMed  Google Scholar 

  213. Macedo, J. et al. Genomic profiling reveals the pivotal role of hrHPV driving copy number and gene expression alterations, including mRNA downregulation of TP53 and RB1 in penile cancer. Mol. Carcinog. 59, 604–617 (2020).

    Article  CAS  PubMed  Google Scholar 

  214. Chahoud, J. et al. Whole-exome sequencing in penile squamous cell carcinoma uncovers novel prognostic categorization and drug targets similar to head and neck squamous cell carcinoma. Clin. Cancer Res. 27, 2560–2570 (2021).

    Article  CAS  PubMed  Google Scholar 

  215. Jacob, J. et al. Comprehensive genomic profiling of histologic subtypes of urethral carcinomas. Urol. Oncol. 39, 731.e1–731.e15 (2021).

    Article  CAS  PubMed  Google Scholar 

  216. Hovelson, D. H. et al. Development and validation of a scalable next-generation sequencing system for assessing relevant somatic variants in solid tumors. Neoplasia 17, 385–399 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  217. Sambandam, V. et al. PDK1 mediates NOTCH1-mutated head and neck squamous carcinoma vulnerability to therapeutic PI3K/mTOR inhibition. Clin. Cancer Res. 25, 3329–3340 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  218. Hashemi Gheinani, A., Bigger-Allen, A., Wacker, A. & Adam, R. M. Systems analysis of benign bladder disorders: insights from omics analysis. Am. J. Physiol. Ren. Physiol. 318, F901–F910 (2020).

    Article  Google Scholar 

  219. Gheinani, A. H. et al. Integrated mRNA-miRNA transcriptome analysis of bladder biopsies from patients with bladder pain syndrome identifies signaling alterations contributing to the disease pathogenesis. BMC Urol. 21, 172 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  220. Cheng, X. F. et al. Integrated analysis of microarray studies to identify novel diagnostic markers in bladder pain syndrome/interstitial cystitis with Hunner lesion. Int. J. Gen. Med. 15, 3143–3154 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  221. Joseph, D. B. et al. Single-cell analysis of mouse and human prostate reveals novel fibroblasts with specialized distribution and microenvironment interactions. J. Pathol. 255, 141–154 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  222. Middleton, L. W. et al. Genomic analysis of benign prostatic hyperplasia implicates cellular re-landscaping in disease pathogenesis. JCI Insight 5, e129749 (2019).

    Article  PubMed  Google Scholar 

  223. Liu, D. et al. Integrative multiplatform molecular profiling of benign prostatic hyperplasia identifies distinct subtypes. Nat. Commun. 11, 1987 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  224. Yang, B., Veneziano, D. & Somani, B. K. Artificial intelligence in the diagnosis, treatment and prevention of urinary stones. Curr. Opin. Urol. 30, 782–787 (2020).

    Article  PubMed  Google Scholar 

  225. Michaels, E. K. et al. Use of a neural network to predict stone growth after shock wave lithotripsy. Urology 51, 335–338 (1998).

    Article  CAS  PubMed  Google Scholar 

  226. Black, K. M., Law, H., Aldoukhi, A., Deng, J. & Ghani, K. R. Deep learning computer vision algorithm for detecting kidney stone composition. BJU Int. 125, 920–924 (2020).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  228. Ganesan, V. & Pearle, M. S. Artificial intelligence in stone disease. Curr. Opin. Urol. 31, 391–396 (2021).

    Article  PubMed  Google Scholar 

  229. Muller, S. et al. Can a dinosaur think? Implementation of artificial intelligence in extracorporeal shock wave lithotripsy. Eur. Urol. Open. Sci. 27, 33–42 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  230. Aminsharifi, A. et al. Predicting the postoperative outcome of percutaneous nephrolithotomy with machine learning system: software validation and comparative analysis with Guy’s stone score and the CROES nomogram. J. Endourol. 34, 692–699 (2020).

    Article  PubMed  Google Scholar 

  231. Venhola, M., Reunanen, M., Taskinen, S., Lahdes-Vasama, T. & Uhari, M. Interobserver and intra-observer agreement in interpreting urodynamic measurements in children. J. Urol. 169, 2344–2346 (2003).

    Article  PubMed  Google Scholar 

  232. Dudley, A. G. et al. Interrater reliability in pediatric urodynamic tracings: a pilot study. J. Urol. 197, 865–870 (2017).

    Article  PubMed  Google Scholar 

  233. Wang, H. S. et al. Pattern recognition algorithm to identify detrusor overactivity on urodynamics. Neurourol. Urodyn. 40, 428–434 (2021).

    Article  PubMed  Google Scholar 

  234. Hobbs, K. T. et al. Machine learning for urodynamic detection of detrusor overactivity. Urology 159, 247–254 (2022).

    Article  PubMed  Google Scholar 

  235. Doern, C. D. & Richardson, S. E. Diagnosis of urinary tract infections in children. J. Clin. Microbiol. 54, 2233–2242 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  236. Medina, M. & Castillo-Pino, E. An introduction to the epidemiology and burden of urinary tract infections. Ther. Adv. Urol. 11, 1756287219832172 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  237. Nitzan, O., Elias, M., Chazan, B. & Saliba, W. Urinary tract infections in patients with type 2 diabetes mellitus: review of prevalence, diagnosis, and management. Diabetes Metab. Syndr. Obes. 8, 129–136 (2015).

    PubMed  PubMed Central  Google Scholar 

  238. Pannek, J. & Wollner, J. Management of urinary tract infections in patients with neurogenic bladder: challenges and solutions. Res. Rep. Urol. 9, 121–127 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  239. Ripa, F. et al. Association of kidney stones and recurrent UTIs: the chicken and egg situation. A systematic review of literature. Curr. Urol. Rep. 23, 165–174 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  240. Taylor, R. A., Moore, C. L., Cheung, K. H. & Brandt, C. Predicting urinary tract infections in the emergency department with machine learning. PLoS ONE 13, e0194085 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  241. Ozkan, I. A., Koklu, M. & Sert, I. U. Diagnosis of urinary tract infection based on artificial intelligence methods. Comput. Meth Prog. Bio 166, 51–59 (2018).

    Article  Google Scholar 

  242. Price, T. K. et al. The clinical urine culture: enhanced techniques improve detection of clinically relevant microorganisms. J. Clin. Microbiol. 54, 1216–1222 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  243. Szlachta-McGinn, A. et al. Molecular diagnostic methods versus conventional urine culture for diagnosis and treatment of urinary tract infection: a systematic review and meta-analysis. Eur. Urol. Open. Sci. 44, 113–124 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  244. Roux-Dalvai, F. et al. Fast and accurate bacterial species identification in urine specimens using LC-MS/MS mass spectrometry and machine learning. Mol. Cell Proteom. 18, 2492–2505 (2019).

    Article  CAS  Google Scholar 

  245. Advanced Analytics Group of Pediatric Urology and ORC Personalized Medicine Group Targeted workup after initial febrile urinary tract infection: using a novel machine learning model to identify children most likely to benefit from voiding cystourethrogram. J. Urol. 202, 144–152 (2019).

    Article  Google Scholar 

  246. Bagli, D. J. et al. Artificial neural networks in pediatric urology: prediction of sonographic outcome following pyeloplasty. J. Urol. 160, 980–983 (1998).

    Article  CAS  PubMed  Google Scholar 

  247. Seckiner, I., Seckiner, S. U., Bayrak, O. & Erturhan, S. Use of artificial neural networks in the management of antenatally diagnosed ureteropelvic junction obstruction. Can. Urol. Assoc. J. 5, E152–E155 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  248. Drysdale, E. et al. Personalized application of machine learning algorithms to identify pediatric patients at risk for recurrent ureteropelvic junction obstruction after dismembered pyeloplasty. World J. Urol. 40, 593–599 (2022).

    Article  PubMed  Google Scholar 

  249. Rademakers, K. et al. Male bladder outlet obstruction: time to re-evaluate the definition and reconsider our diagnostic pathway? ICI-RS 2015. Neurourol. Urodyn. 36, 894–901 (2017).

    Article  PubMed  Google Scholar 

  250. Sonke, G. S., Heskes, T., Verbeek, A. L., de la Rosette, J. J. & Kiemeney, L. A. Prediction of bladder outlet obstruction in men with lower urinary tract symptoms using artificial neural networks. J. Urol. 163, 300–305 (2000).

    Article  CAS  PubMed  Google Scholar 

  251. Abdovic, S. et al. Predicting posterior urethral obstruction in boys with lower urinary tract symptoms using deep artificial neural network. World J. Urol. 37, 1973–1979 (2019).

    Article  CAS  PubMed  Google Scholar 

  252. Yin, S. et al. Multi-instance deep learning of ultrasound imaging data for pattern classification of congenital abnormalities of the kidney and urinary tract in children. Urology 142, 183–189 (2020).

    Article  PubMed  Google Scholar 

  253. Kwong, J. C. et al. Posterior urethral valves outcomes prediction (PUVOP): a machine learning tool to predict clinically relevant outcomes in boys with posterior urethral valves. Pediatr. Nephrol. 37, 1067–1084 (2021).

    Article  PubMed  Google Scholar 

  254. Thomas, A. A. et al. Extracting data from electronic medical records: validation of a natural language processing program to assess prostate biopsy results. World J. Urol. 32, 99–103 (2014).

    Article  PubMed  Google Scholar 

  255. Odisho, A. Y. et al. Automating the capture of structured pathology data for prostate cancer clinical care and research. JCO Clin. Cancer Inf. 3, 1–8 (2019).

    Google Scholar 

  256. Schroeck, F. R. et al. Development of a natural language processing engine to generate bladder cancer pathology data for health services research. Urology 110, 84–91 (2017).

    Article  PubMed  Google Scholar 

  257. Glaser, A. P. et al. Automated extraction of grade, stage, and quality information from transurethral resection of bladder tumor pathology reports using natural language processing. JCO Clin. Cancer Inf. 2, 1–8 (2018).

    Google Scholar 

  258. Bashashati, A. & Goldenberg, S. L. AI for prostate cancer diagnosis – hype or today’s reality? Nat. Rev. Urol. 19, 261–262 (2022).

    Article  PubMed  Google Scholar 

  259. Yang, C. et al. Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review. J. Am. Med. Inf. Assoc. 29, 983–989 (2022).

    Article  Google Scholar 

  260. Reinke, A., Tizabi, M. D., Eisenmann, M. & Maier-Hein, L. Common pitfalls and recommendations for grand challenges in medical artificial intelligence. Eur. Urol. Focus. 7, 710–712 (2021).

    Article  PubMed  Google Scholar 

  261. Bulten, W. et al. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat. Med. 28, 154–163 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  262. Maier-Hein, L. et al. Why rankings of biomedical image analysis competitions should be interpreted with care. Nat. Commun. 9, 5217 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  263. Zhou, Q., Chen, Z. H., Cao, Y. H. & Peng, S. Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review. NPJ Digit. Med. 4, 154 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  264. Dhiman, P. et al. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med. Res. Methodol. 22, 101 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  265. Collins, G. S. et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 11, e048008 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  266. Calogero, A. E., Burgio, G., Condorelli, R. A., Cannarella, R. & La Vignera, S. Epidemiology and risk factors of lower urinary tract symptoms/benign prostatic hyperplasia and erectile dysfunction. Aging Male 22, 12–19 (2019).

    Article  PubMed  Google Scholar 

  267. Grossman, R. L. et al. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  268. Regev, A. et al. The Human Cell Atlas. Elife 6, e27041 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  269. Goldman, M. J. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 38, 675–678 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  270. Papatheodorou, I. et al. Expression Atlas: gene and protein expression across multiple studies and organisms. Nucleic Acids Res. 46, D246–D251 (2018).

    Article  CAS  PubMed  Google Scholar 

  271. Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  PubMed  Google Scholar 

  272. Hu, X. et al. TumorFusions: an integrative resource for cancer-associated transcript fusions. Nucleic Acids Res. 46, D1144–D1149 (2018).

    Article  CAS  PubMed  Google Scholar 

  273. Omar, M. I. et al. Introducing PIONEER: a project to harness big data in prostate cancer research. Nat. Rev. Urol. 17, 351–362 (2020).

    Article  PubMed  Google Scholar 

  274. Dunning, M. J. et al. Mining human prostate cancer datasets: the “camcAPP” shiny app. EBioMedicine 17, 5–6 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  275. Hu, Z. et al. Genomic characterization of genes encoding histone acetylation modulator proteins identifies therapeutic targets for cancer treatment. Nat. Commun. 10, 733 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  276. Ghoshdastider, U. et al. Pan-cancer analysis of ligand–receptor cross-talk in the tumor microenvironment. Cancer Res. 81, 1802–1812 (2021).

    Article  CAS  PubMed  Google Scholar 

  277. Rohatgi, N., Ghoshdastider, U., Baruah, P., Kulshrestha, T. & Skanderup, A. J. A pan-cancer metabolic atlas of the tumor microenvironment. Cell Rep. 39, 110800 (2022).

    Article  CAS  PubMed  Google Scholar 

  278. Stewart, B. J. et al. Spatiotemporal immune zonation of the human kidney. Science 365, 1461–1466 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  279. McMahon, A. P. et al. GUDMAP: the genitourinary developmental molecular anatomy project. J. Am. Soc. Nephrol. 19, 667–671 (2008).

    Article  PubMed  Google Scholar 

  280. Rigden, D. J. & Fernandez, X. M. The 2018 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res. 46, D1–D7 (2018).

    Article  CAS  PubMed  Google Scholar 

  281. van der Wijst, M. et al. Science Forum: the single-cell eQTLGen consortium. Elife 9, e52155 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  282. Abugessaisa, I. et al. SCPortalen: human and mouse single-cell centric database. Nucleic Acids Res. 46, D781–D787 (2018).

    Article  CAS  PubMed  Google Scholar 

  283. Ner-Gaon, H., Melchior, A., Golan, N., Ben-Haim, Y. & Shay, T. JingleBells: a repository of immune-related single-cell RNA-sequencing datasets. J. Immunol. 198, 3375–3379 (2017).

    Article  CAS  PubMed  Google Scholar 

  284. Cao, Y., Zhu, J., Jia, P. & Zhao, Z. scRNASeqDB: a database for RNA-seq based gene expression profiles in human single cells. Genes 8, 368 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  285. Cao, Z. J., Wei, L., Lu, S., Yang, D. C. & Gao, G. Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST. Nat. Commun. 11, 3458 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  286. Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15, 255–261 (2018).

    Article  CAS  PubMed  Google Scholar 

  287. Svensson, V., da Veiga Beltrame, E. & Pachter, L. A curated database reveals trends in single-cell transcriptomics. Database 2020, baaa073 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  288. Li, M. et al. DISCO: a database of Deeply Integrated human Single-Cell Omics data. Nucleic Acids Res. 50, D596–D602 (2022).

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Author notes

  1. Ali Hashemi Gheinani

    Present address: Department for BioMedical Research, University of Bern, Bern, Switzerland

Authors and Affiliations

  1. Department of Urology, Boston Children’s Hospital, Boston, MA, USA

    Ali Hashemi Gheinani & Rosalyn M. Adam

  2. Department of Surgery, Harvard Medical School, Boston, MA, USA

    Ali Hashemi Gheinani & Rosalyn M. Adam

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Ali Hashemi Gheinani & Rosalyn M. Adam

  4. Department of Urology, Inselspital, Bern, Switzerland

    Ali Hashemi Gheinani

  5. Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jina Kim & Sungyong You

  6. Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jina Kim & Sungyong You

  7. Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jina Kim & Sungyong You

Authors
  1. Ali Hashemi Gheinani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jina Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sungyong You
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rosalyn M. Adam
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors researched data for the article. R.M.A., A.H.G. and S.Y. contributed substantially to discussion of the content. All authors wrote the article. R.M.A., A.H.G. and S.Y. reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Rosalyn M. Adam.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Urology thanks Roland Arnold, Andrew Hung and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Related links

Grand Challenge for medical image analysis: www.grand-challenge.org

PAIP: https://paip2021.grand-challenge.org/

PANDA: https://panda.grand-challenge.org/

PI-CAI: https://pi-cai.grand-challenge.org/

PROSTATEx: https://prostatex.grand-challenge.org/

Shiny: https://www.rstudio.com/products/shiny/

Supplementary information

Glossary

Area under the curve

(AUC). The AUC of a receiver operating characteristic curve is used to measure the accuracy of a model. An AUC of 0.5 represents a model that does not perform any better than random chance.

Artificial neural networks

Computational models inspired by the structure and functioning of biological neural networks, used in machine learning to process complex data and make predictions or classifications.

Biases

Biases are systematic errors or prejudices that can exist in artificial intelligence (AI) systems, data, algorithms or decision-making processes. Biases can arise owing to various factors, such as biased data collection, biased algorithm design or biased human decisions that influence the training process. For example, bias can be introduced if a training dataset used to develop an AI model for bladder cancer classification majorly consists of patient material from a specific demographic group or sex, or if certain genomic markers are prioritized.

Cohen’s κ coefficient

A statistical parameter used to measure reliability between raters that can have values from −1 to +1, in which 0 is the extent of agreement expected by random chance.

Data leakage

Data leakage occurs when information from the test set leaks into the training set, or vice versa. This phenomenon can happen when the data are pre-processed or cleaned before splitting, or when the test set is used to inform feature selection or model tuning. Data leakage can result in overly optimistic performance estimates, and the model might perform poorly on new, unseen data.

Deep learning

A subfield of machine learning that uses artificial neural networks with multiple layers, enabling the artificial intelligence system to learn hierarchical representations and extract intricate patterns from large datasets.

Dropout

A technique used in machine learning to prevent overfitting, in which certain information is temporarily ignored during training to ensure that the model does not overly rely on specific features, improving the ability of the model to generalize and make accurate predictions. By incorporating dropout, the ability of the model to generalize is improved and placing excessive importance on individual genes is avoided, ensuring a robust and reliable analysis.

Early stopping

A strategy used during machine learning training to avoid overfitting, in which the training process is stopped before completion based on a specific measure (such as validation performance) to prevent the model from becoming too specialized to the training data, ensuring good ability to generalize to new, unseen data.

Imbalanced classes

Classes are imbalanced when the proportion of observations in one class is much higher or lower than in the other. This phenomenon can lead to biased performance estimates, as the model might be accurate on the dominant class but perform poorly on the minority class. This problem can be addressed by using techniques such as stratified sampling, oversampling or undersampling.

Learning rubbish (learning garbage)

This refers to the process of training an artificial intelligence system using low-quality or inaccurate data. For example, if in a study, the training dataset contains gene expression profiles from unrelated cancer types or includes samples with unreliable annotations, the AI system might learn from this rubbish data and produce misleading associations.

Microarray

Microarrays are nucleic acid sequences corresponding to defined genes or transcripts arrayed on a solid phase support for hybridization with cDNA prepared from samples under investigation. Microarrays enable measurement of transcript abundance on a genome-wide scale.

Natural language processing

A branch of artificial intelligence focused on the interaction between computers and human language, enabling machines to understand, interpret and generate human language text or speech.

Negative predictive value

The proportion of individuals with a negative test result who do not have the disease.

Non-representative sampling

Sampling is non-representative when the training or test set is not representative of the population from which the test was sampled. This phenomenon can happen when the data are collected from a biased or limited source, or when hidden confounding factors influence the outcome variable. Non-representative samples can lead to poor generalization and low predictive accuracy.

Optimal fitting

Optimal fitting occurs when a model is sufficiently complex to capture underlying patterns in the data and generalizes well to new data. This fitting requires a balance between model complexity and the amount and quality of data available for training the model.

Overfitting

Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on new data. This phenomenon can happen when a model is trained on a limited set of data and learns the noise in the data instead of the underlying patterns. For example, in a situation in which a decision tree model is used to predict the outcome of a therapy based on the expression of a large number of genes in a tumour (features), but many of these genes are irrelevant or noisy, the model might overfit the data.

Positive predictive value

The proportion of individuals with a positive test result who actually have the disease.

Semi-supervised learning

A machine learning technique that uses a combination of labelled and unlabelled data to train an artificial intelligence system, leveraging the available labelled data and the patterns inferred from the unlabelled data.

Small sample size

The sample size is small when too few observations are available in the training or test set to build or evaluate a robust model. In this case, the model either memorizes the training data or fails to capture the underlying patterns, leading to overfitting or underfitting. Small sample size can be addressed by increasing the size of the dataset, using data augmentation techniques or using models with increased robustness.

Supervised learning

A machine learning technique in which the artificial intelligence system is trained using labelled data, where the input and corresponding output pairs are provided to guide the learning process.

Test or testing set

A subset of a dataset used to evaluate the performance of a machine learning model. The purpose of the test dataset is to measure how well the model performs on new, unseen data. The test dataset is used to estimate the accuracy of the model’s predictions on new data.

Training set

A subset of a dataset used to train a machine learning model. The purpose of the training dataset is to build the model by learning the relationships between the input variables (features) and the output variable (target variable). The model uses the training dataset to determine how to make predictions.

Underfitting

Underfitting occurs when a model is too simple and fails to capture the complexity of the data, resulting in poor performance on both the training data and new data. For example, if a linear regression model is used to predict the outcome of a therapy based on the size and number of tumours but the relationship between these variables is more complex, the model might underfit the data.

Unsupervised learning

A machine learning technique in which the artificial intelligence system discovers patterns or structures in data without being explicitly guided or labelled.

Validation

Validation is the process of assessing the accuracy of a model on data that have not yet been seen by the model. Cross-validation is a technique in which a dataset is divided into multiple training and test sets, and the model is trained and tested on each set, to evaluate the performance of the model on the dataset. Common problems with separating the training and test sets include feature selection performed on the entire set, which can lead to overfitting and poor performance on new data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hashemi Gheinani, A., Kim, J., You, S. et al. Bioinformatics in urology — molecular characterization of pathophysiology and response to treatment. Nat Rev Urol 21, 214–242 (2024). https://doi.org/10.1038/s41585-023-00805-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41585-023-00805-3

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research