Neural networks in oncourology

In recent decades, neural networks have been widely applied in many fields of science and medicine. Accurate and early diagnosis of malignancies is a key challenge in oncology. Neural networks can analyse a wide range of medical data and identify relationships between qualitative and quantitative features. This allows for more precise and timely diagnoses. Moreover, they can be used to predict tumour progression, evaluate treatment effectiveness, and optimise treatment plans for each patient

In oncourology, the use of neural networks offers new perspectives for the diagnosis, prognosis, and treatment of various cancer conditions related to the urinary tract and male reproductive system. This review article explores how neural networks are being used in this field and present research into the use of neural networks for diagnosing, predicting the course and treating urological oncological diseases. The advantages and limitations of using neural networks in this field are demonstrated, and possible directions for future research are suggested. The application of neural networks in oncourology opens new horizons for the development of a personalised approach to diagnosing and treating oncological diseases. Artificial intelligence has the potential to become a powerful tool for improving the accuracy of patient outcome predictions and reducing undesirable side effects of therapy. Introducing neural networks into oncourological practice creates new opportunities for enhancing the work of healthcare organisations and improving the quality of care provided to patients. This can lead to better treatment outcomes and improved patient satisfaction.

1. Orudzhev AA, Breusov AV. Dynamics of urological morbidity of the Central Federal District population (Russian Federation) in 2013-2017. Russian Open Medical Journal. 2020;9:e0108. DOI: 10.15275/rusomj.2020.0108

2. Gareev I, Gileva Y, Dzidzaria A, Beylerli O, Pavlov V, Agaverdiev M, Mazorov B, Biganyakov I, Vardikyan A, Jin M, Ahmad A. Long non-coding RNAs in oncourology. Noncoding RNA Res. 2021;6(3):139-145. DOI: 10.1016/j.ncrna.2021.08.001

3. Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS One. 2019;14(2):e0212356. DOI: 10.1371/journal.pone.0212356

4. Kolachalama VB, Garg PS. Machine learning and medical education. NPJ Digit Med. 2018;1:54. DOI: 10.1038/s41746-018-0061-1

5. Chen J, Remulla D, Nguyen JH, Dua A, Liu Y, Dasgupta P, Hung AJ. Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU Int. 2019;124(4):567-577. Erratum in: BJU Int. 2020;126(5):647. DOI: 10.1111/bju.14852

6. Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol. 2020;56(4):498-505. DOI: 10.1002/uog.22122

7. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. DOI: 10.1038/nature14539

8. Miller DD, Brown EW. Artificial Intelligence in Medical Practice: The Question to the Answer? Am J Med. 2018;131(2):129-133. DOI: 10.1016/j.amjmed.2017.10.035

9. Lancashire LJ, Lemetre C, Ball GR. An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies. Brief Bioinform. 2009;10(3):315-329. DOI: 10.1093/bib/bbp012

10. Sajda P. Machine learning for detection and diagnosis of disease. Annu Rev Biomed Eng. 2006;8:537-565. DOI: 10.1146/annurev.bioeng.8.061505.095802

11. Molla M, Waddell M, Page D, Shavlik J. Using Machine Learning to Design and Interpret Gene-Expression Microarrays. AIMag. 2004;25(1):23. DOI: 10.1609/aimag.v25i1.1745

12. Shi TW, Kah WS, Mohamad MS, Moorthy K, Deris S, Sjaugi MF, Omatu S, Corchado JM, Kasim S. A review of gene selection tools in classifying cancer microarray data. Curr Bioinform. 2017;12(3):202-212. DOI: 10.2174/1574893610666151026215104

13. Elkin PL, Schlegel DR, Anderson M, Komm J, Ficheur G, Bisson L. Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support. Appl Clin Inform. 2018;9(2):432-439. DOI: 10.1055/s-0038-1656547

14. Rong G, Mendez A, Assi EB, Zhao B, Sawan M. Artificial Intelligence in Healthcare: Review and Prediction Case Studies. Engineering. 2020,6(3):291-301. DOI: 10.1016/j.eng.2019.08.015

15. Safdar S, Zafar S, Zafar N, Khan NF. Machine learning based decision support systems (DSS) for heart disease diagnosis: a review. Artif Intell Rev. 2018;50:597-623. DOI: 10.1007/s10462-017-9552-8

16. Long D., Magerko B. What Is AI Literacy? Competencies and Design Considerations. CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020:1-16. DOI: 10.1145/3313831.3376727

17. , Briganti G, Le Moine O. Artificial Intelligence in Medicine: Today and Tomorrow. Front Med (Lausanne). 2020;7:27. DOI: 10.3389/fmed.2020.00027

18. Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, Kadoury S, Tang A. Deep Learning: A Primer for Radiologists. Radiographics. 2017;37(7):2113-2131. DOI: 10.1148/rg.2017170077

19. Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E. Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide. Radiology. 2019;290(3):590-606. DOI: 10.1148/radiol.2018180547

20. Cohen MS, Hanley RS, Kurteva T, Ruthazer R, Silverman ML, Sorcini A, Hamawy K, Roth RA, Tuerk I, Libertino JA. Comparing the Gleason prostate biopsy and Gleason prostatectomy grading system: the Lahey Clinic Medical Center experience and an international meta-analysis. Eur Urol. 2008;54(2):371-381. DOI: 10.1016/j.eururo.2008.03.049

21. Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics. 2019;9(5):1303-1322. DOI: 10.7150/thno.30309

22. Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Med. 2017;38:122-139. DOI: 10.1016/j.ejmp.2017.05.071

23. Chaddad A, Kucharczyk MJ, Daniel P, Sabri S, Jean-Claude BJ, Niazi T, Abdulkarim B. Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation. Front Oncol. 2019;9:374. DOI: 10.3389/fonc.2019.00374

24. Song J, Yin Y, Wang H, Chang Z, Liu Z, Cui L. A review of original articles published in the emerging field of radiomics. Eur J Radiol. 2020;127:108991. DOI: 10.1016/j.ejrad.2020.108991

25. Liberini V, Laudicella R, Balma M, Nicolotti DG, Buschiazzo A, Grimaldi S, Lorenzon L, Bianchi A, Peano S, Bartolotta TV, Farsad M, Baldari S, Burger IA, Huellner MW, Papaleo A, Deandreis D. Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics. Eur Radiol Exp. 2022;6(1):27. DOI: 10.1186/s41747-022-00282-0

26. Mata LA, Retamero JA, Gupta RT, García Figueras R, Luna A. Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation. Radiographics. 2021;41(6):1676-1697. DOI: 10.1148/rg.2021210020

27. Cuocolo R, Cipullo MB, Stanzione A, Romeo V, Green R, Cantoni V, Ponsiglione A, Ugga L, Imbriaco M. Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis. Eur Radiol. 2020;30(12):6877-6887. DOI: 10.1007/s00330-020-07027-w

28. Rakovic K, Colling R, Browning L, Dolton M, Horton MR, Protheroe A, Lamb AD, Bryant RJ, Scheffer R, Crofts J, Stanislaus E, Verrill C. The Use of Digital Pathology and Artificial Intelligence in Histopathological Diagnostic Assessment of Prostate Cancer: A Survey of Prostate Cancer UK Supporters. Diagnostics (Basel). 2022;12(5):1225. DOI: 10.3390/diagnostics12051225

29. Raciti P, Sue J, Ceballos R, Godrich R, Kunz JD, Kapur S, Reuter V, Grady L, Kanan C, Klimstra DS, Fuchs TJ. Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies. Mod Pathol. 2020;33(10):2058-2066. DOI: 10.1038/s41379-020-0551-y

30. Chatrian A, Colling RT, Browning L, Alham NK, Sirinukunwattana K, Malacrino S, Haghighat M, Aberdeen A, Monks A, Moxley-Wyles B, Rakha E, Snead DRJ, Rittscher J, Verrill C. Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies. Mod Pathol. 2021;34(9):1780-1794. DOI: 10.1038/s41379-021-00826-6

31. Osoba D. Current applications of health-related quality-of-life assessment in oncology. Support Care Cancer. 1997;5(2):100-104. DOI: 10.1007/BF01262565

32. Tzelves L, Manolitsis I, Varkarakis I, Ivanovic M, Kokkonidis M, Useros CS, Kosmidis T, Muñoz M, Grau I, Athanatos M, Vizitiu A, Lampropoulos K, Koutsouri T, Stefanatou D, Perrakis K, Stratigaki C, Autexier S, Kosmidis P, Valachis A. Artificial intelligence supporting cancer patients across Europe-The ASCAPE project. PLoS One. 2022;17(4):e0265127. DOI: 10.1371/journal.pone.0265127

33. International Agency for Research on Cancer. Estimated number of new cases in 2020, worldwide, both sexes, all ages. Geneva, Switzerland: World Health Organization; 2021.

34. Ahmadi H, Duddalwar V, Daneshmand S. Diagnosis and Staging of Bladder Cancer. Hematol Oncol Clin North Am. 2021;35(3):531-541. DOI: 10.1016/j.hoc.2021.02.004

35. Jia X, Shkolyar E, Laurie MA, Eminaga O, Liao JC, Xing L. Tumor detection under cystoscopy with transformer-augmented deep learning algorithm. Phys Med Biol. 2023;68(16):10.1088/1361-6560/ace499. DOI: 10.1088/1361-6560/ace499

36. Ikeda A, Nosato H, Kochi Y, Kojima T, Kawai K, Sakanashi H, Murakawa M, Nishiyama H. Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence. J Endourol. 2020;34(3):352-358. DOI: 10.1089/end.2019.0509

37. Lorencin I, Anđelić N, Španjol J, Car Z. Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis. Artif Intell Med. 2020;102:101746. DOI: 10.1016/j.artmed.2019.101746

38. Eminaga O, Eminaga N, Semjonow A, Breil B. Diagnostic Classification of Cystoscopic Images Using Deep Convolutional Neural Networks. JCO Clin Cancer Inform. 2018;2:1-8. DOI: 10.1200/CCI.17.00126

39. Chang TC, Shkolyar E, Del Giudice F, Eminaga O, Lee T, Laurie M, Seufert C, Jia X, Mach KE, Xing L, Liao JC. Real-time Detection of Bladder Cancer Using Augmented Cystoscopy with Deep Learning: a Pilot Study. J Endourol. 2023. Epub ahead of print. DOI: 10.1089/end.2023.0056

40. Yoo JW, Koo KC, Chung BH, Baek SY, Lee SJ, Park KH, Lee KS. Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method. Sci Rep. 2022;12:17699. DOI: 10.1038/s41598-022-22797-7

41. Xu X, Zhang X, Tian Q, Zhang G, Liu Y, Cui G, Meng J, Wu Y, Liu T, Yang Z, Lu H. Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI. Int J Comput Assist Radiol Surg. 2017;12(4):645-656. DOI: 10.1007/s11548-017-1522-8

42. Ljungberg B, Bensalah K, Canfield S, Dabestani S, Hofmann F, Hora M, Kuczyk MA, Lam T, Marconi L, Merseburger AS, Mulders P, Powles T, Staehler M, Volpe A, Bex A. EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol. 2015;67(5):913-924. DOI: 10.1016/j.eururo.2015.01.005

43. Pedersen M, Andersen MB, Christiansen H, Azawi NH. Classification of renal tumour using convolutional neural networks to detect oncocytoma. Eur J Radiol. 2020;133:109343. DOI: 10.1016/j.ejrad.2020.109343

44. Zheng H, Ji J, Zhao L, Chen M, Shi A, Pan L, Huang Y, Zhang H, Dong B, Gao H. Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps. Oncotarget. 2016;7(37):59189-59198. DOI: 10.18632/oncotarget.10830

45. Kohonen T. Self-organized formation of topologically correct feature maps. Biol Cybern. 2004;43:59-69. DOI: 10.1007/BF00337288

46. Kocak B, Yardimci AH, Bektas CT, Turkcanoglu MH, Erdim C, Yucetas U, Koca SB, Kilickesmez O. Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation. Eur J Radiol. 2018;107:149-157. DOI: 10.1016/j.ejrad.2018.08.014

47. Feng Z, Rong P, Cao P, Zhou Q, Zhu W, Yan Z, Liu Q, Wang W. 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. 2018;28(4):1625-1633. DOI: 10.1007/s00330-017-5118-z

48. Cheng L, Albers P, Berney DM, Feldman DR, Daugaard G, Gilligan T, Looijenga LHJ. Testicular cancer. Nat Rev Dis Primers. 2018;4(1):29. DOI: 10.1038/s41572-018-0029-0

49. Batool A, Karimi N, Wu XN, Chen SR, Liu YX. Testicular germ cell tumor: a comprehensive review. Cell Mol Life Sci. 2019;76(9):1713-1727. DOI: 10.1007/s00018-019-03022-7

50. Baessler B, Nestler T, Pinto Dos Santos D, Paffenholz P, Zeuch V, Pfister D, Maintz D, Heidenreich A. Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection. Eur Radiol. 2020;30(4):2334-2345. DOI: 10.1007/s00330-019-06495-z

51. Lewin J, Dufort P, Halankar J, O'Malley M, Jewett MAS, Hamilton RJ, Gupta A, Lorenzo A, Traubici J, Nayan M, Leão R, Warde P, Chung P, Anson Cartwright L, Sweet J, Hansen AR, Metser U, Bedard PL. Applying Radiomics to Predict Pathology of Postchemotherapy Retroperitoneal Nodal Masses in Germ Cell Tumors. JCO Clin Cancer Inform. 2018;2:1-12. DOI: 10.1200/CCI.18.00004

52. Lotti F, Frizza F, Balercia G, Barbonetti A, Behre HM, Calogero AE, Cremers JF, Francavilla F, Isidori AM, Kliesch S, La Vignera S, Lenzi A, Marcou M, Pilatz A, Poolamets O, Punab M, Peraza Godoy MF, Rajmil O, Salvio G, Shaeer O, Weidner W, Maseroli E, Cipriani S, Baldi E, Degl'Innocenti S, Danza G, Caldini AL, Terreni A, Boni L, Krausz C, Maggi M. The European Academy of Andrology (EAA) ultrasound study on healthy, fertile men: clinical, seminal and biochemical characteristics. Andrology. 2020;8(5):1005-1020. DOI: 10.1111/andr.12808

53. Fanni SC, Febi M, Colligiani L, Volpi F, Ambrosini I, Tumminello L, Aghakhanyan G, Aringhieri G, Cioni D, Neri E. A first look into radiomics application in testicular imaging: A systematic review. Front Radiol. 2023;3:1141499. DOI: 10.3389/fradi.2023.1141499

54. Soomro NA, Hashimoto DA, Porteous AJ, Ridley CJA, Marsh WJ, Ditto R, Roy S. Systematic review of learning curves in robot-assisted surgery. BJS Open. 2020;4(1):27-44. DOI: 10.1002/bjs5.50235

55. Agha RA, Fowler AJ. The role and validity of surgical simulation. Int Surg. 2015;100(2):350-357. DOI: 10.9738/INTSURG-D-14-00004.1

56. Thomas MP. The role of simulation in the development of technical competence during surgical training: a literature review. Int J Med Educ. 2013;4:48–58. DOI: 10.5116/ijme.513b.2df7

57. Andras I, Mazzone E, van Leeuwen FWB, De Naeyer G, van Oosterom MN, Beato S, Buckle T, O'Sullivan S, van Leeuwen PJ, Beulens A, Crisan N, D'Hondt F, Schatteman P, van Der Poel H, Dell'Oglio P, Mottrie A. Artificial intelligence and robotics: a combination that is changing the operating room. World J Urol. 2020;38(10):2359-2366. DOI: 10.1007/s00345-019-03037-6

58. Hung AJ, Chen J, Gill IS. Automated Performance Metrics and Machine Learning Algorithms to Measure Surgeon Performance and Anticipate Clinical Outcomes in Robotic Surgery. JAMA Surg. 2018;153(8):770-771. DOI: 10.1001/jamasurg.2018.1512

59. Bhandari M, Zeffiro T, Reddiboina M. Artificial intelligence and robotic surgery: current perspective and future directions. Curr Opin Urol. 2020;30(1):48-54. DOI: 10.1097/MOU.0000000000000692

60. Yang GZ, Cambias J, Cleary K, Daimler E, Drake J, Dupont PE, Hata N, Kazanzides P, Martel S, Patel RV, Santos VJ, Taylor RH. Medical robotics-Regulatory, ethical, and legal considerations for increasing levels of autonomy. Sci Robot. 2017;2(4):eaam8638. DOI: 10.1126/scirobotics.aam8638

61. Hashizume M, Konishi K, Tsutsumi N, Yamaguchi S, Shimabukuro R. A new era of robotic surgery assisted by a computer-enhanced surgical system. Surgery. 2002;131(1 Suppl):S330-3. DOI: 10.1067/msy.2002.120119

62. McCartney J. AI Is Poised to “Revolutionize” Surgery. ACS Bulletin. 2023;108.