Use of machine learning to predict bladder cancer survival outcomes: a systematic literature review

References

• Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023. CA Cancer J Clin. 2023 Jan;73(1):17–48.
   PubMed Web of Science ®Google Scholar
• Richters A, Aben KKH, Kiemeney L. The global burden of urinary bladder cancer: an update. World J Urol. 2020 Aug;38(8):1895–1904. doi: 10.1007/s00345-019-02984-4.
   PubMed Web of Science ®Google Scholar
• Ilijazi D, Abufaraj M, Hassler MR, et al. Waiting in the wings: the emerging role of molecular biomarkers in bladder cancer. Expert Rev Mol Diagn. 2018 Apr;18(4):347–356.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Hong YK, Zhuang DW, et al. Bladder cancer survival nomogram: development and validation of a prediction tool, using the SEER and TCGA databases. Medicine (Baltimore). 2019 Nov;98(44):e17725.
   PubMed Web of Science ®Google Scholar
• Wagner MW, Namdar K, Biswas A, et al. Radiomics, machine learning, and artificial intelligence-what the neuroradiologist needs to know. Neuroradiology. 2021 Dec;63:1957–1967.
   PubMed Web of Science ®Google Scholar
• Doupe P, Faghmous J, Basu S. Machine Learning for Health Services Researchers. Value Health. 2019 Jul;22(7):808–815. doi:10.1016/j.jval.2019.02.012.
   PubMed Web of Science ®Google Scholar
• Li J, Zhou Z, Dong J, et al. Predicting breast cancer 5-year survival using machine learning: a systematic review. PLoS ONE. 2021;16(4):e0250370. doi:10.1371/journal.pone.0250370
   PubMed Web of Science ®Google Scholar
• Borhani S, Borhani R, Kajdacsy-Balla A. Artificial intelligence: a promising frontier in bladder cancer diagnosis and outcome prediction. Crit Rev Oncol Hematol. 2022 Mar;171:103601. doi:10.1016/j.critrevonc.2022.103601.
   PubMed Web of Science ®Google Scholar
• Suarez-Ibarrola R, Hein S, Reis G, et al. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol. 2020 Oct;38(10):2329–2347.
   PubMed Web of Science ®Google Scholar
• Gandi C, Vaccarella L, Bientinesi R, et al. Bladder cancer in the time of machine learning: intelligent tools for diagnosis and management. Urologia. 2021 May;88(2):94–102.
   PubMed Web of Science ®Google Scholar
• Ge L, Chen Y, Yan C, et al. Study Progress of Radiomics with Machine Learning for Precision Medicine in Bladder Cancer Management. Front Oncol. 2019 Nov 28;9:1296. doi:10.3389/fonc.2019.01296
   PubMed Web of Science ®Google Scholar
• Ouzzani M, Hammady H, Fedorowicz Z, et al. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016 Dec 5;5:210. doi:10.1186/s13643-016-0384-4
   PubMedGoogle Scholar
• Cabitza F, Campagner A. The need to separate the wheat from the chaff in medical informatics: introducing a comprehensive checklist for the (self)-assessment of medical AI studies. Int J Med Inform. 2021;153:104510. doi: 10.1016/j.ijmedinf.2021.104510
   PubMed Web of Science ®Google Scholar
• Wang M, Francis F, Kunz H, et al. Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: a systematic review. Intelligence-Based Medicine. 2022;6:100072. doi:10.1016/j.ibmed.2022.100072
   Google Scholar
• Zhou Y, Y-T G, X-L S, et al. Machine learning predictive models for acute pancreatitis: a systematic review. Int J Med Inform. 2022;157:104641. doi:10.1016/j.ijmedinf.2021.104641
   PubMed Web of Science ®Google Scholar
• Bassi P, Sacco E, De Marco V, et al. Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis. BJU Int. 2007 May;99(5):1007–1012.
   PubMed Web of Science ®Google Scholar
• Buchner A, May M, Burger M, et al. Prediction of outcome in patients with urothelial carcinoma of the bladder following radical cystectomy using artificial neural networks. Eur J Surg Oncol. 2013 Apr;39(4):372–379.
   PubMed Web of Science ®Google Scholar
• Ji W, Naguib RN, Ghoneim MA. Neural network-based assessment of prognostic markers and outcome prediction in bilharziasis-associated bladder cancer. IEEE Trans Inf Technol Biomed. 2003 Sep;7(3):218–224. doi: 10.1109/TITB.2003.813796.
   PubMedGoogle Scholar
• Klén R, Salminen AP, Mahmoudian M, et al. Prediction of complication related death after radical cystectomy for bladder cancer with machine learning methodology. Scand J Urol. 2019 Oct;53(5):325–331.
   PubMed Web of Science ®Google Scholar
• Kolasa M, Wojtyna R, Długosz R, et al. Application of artificial neural network to predict survival time for patients with bladder cancer. In: Computers in Medical Activity. Springer Berlin Heidelberg; 2009. p. 113–122. doi:10.1007/978-3-642-04462-5_11
   Google Scholar
• Qureshi KN, Naguib RN, Hamdy FC, et al. Neural network analysis of clinicopathological and molecular markers in bladder cancer. J Urol. 2000 Feb;163:630–633.
   PubMed Web of Science ®Google Scholar
• Schuettfort VM, D’Andrea D, Quhal F, et al. A panel of systemic inflammatory response biomarkers for outcome prediction in patients treated with radical cystectomy for urothelial carcinoma. BJU Int. 2022 Feb;129:182–193.
   PubMed Web of Science ®Google Scholar
• Abdul-Muhsin H, De Lucia N, Singh V, et al. Outcome prediction following radical nephroureterectomy for upper tract urothelial carcinoma. Urologic Oncology. 2021;39(133): e9–133.e16. doi:10.1016/j.urolonc.2020.08.021
   Google Scholar
• Bhambhvani HP, Zamora A, Shkolyar E, et al. Development of robust artificial neural networks for prediction of 5-year survival in bladder cancer. Urologic Oncology. 2021;39(3):193.e7–193.e12. doi: 10.1016/j.urolonc.2020.05.009
   Google Scholar
• Hasnain Z, Mason J, Gill K, et al. Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients. PLoS ONE. 2019;14(2):e0210976. doi:10.1371/journal.pone.0210976
   PubMed Web of Science ®Google Scholar
• Song Q, Seigne JD, Schned AR, et al. A machine learning approach for long-term prognosis of bladder cancer based on clinical and molecular features. AMIA Summits Transl Sci Proc. 2020;2020:607.
   PubMedGoogle Scholar
• Wang G, Lam K-M, Deng Z, et al. Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques. Comput Biol Med. 2015;63:124–132. doi:10.1016/j.compbiomed.2015.05.015
   PubMed Web of Science ®Google Scholar
• Wang G, Zhang G, Choi K-S, et al., editors. An output-based knowledge transfer approach and its application in bladder cancer prediction. 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA; 2017: IEEE
   Google Scholar
• Abuhelwa AY, Kichenadasse G, McKinnon RA, et al. Machine Learning for Prediction of Survival Outcomes with Immune-Checkpoint Inhibitors in Urothelial Cancer. Cancers (Basel). 2021 Apr 21;13(9):2001. doi:10.3390/cancers13092001
   PubMed Web of Science ®Google Scholar
• Petrylak DP, de Wit R, Chi KN, et al. Ramucirumab plus docetaxel versus placebo plus docetaxel in patients with locally advanced or metastatic urothelial carcinoma after platinum-based therapy (RANGE): overall survival and updated results of a randomised, double-blind, phase 3 trial. Lancet Oncol. 2020 Jan;21(1):105–120.
   PubMed Web of Science ®Google Scholar
• Kourou K, Exarchos TP, Exarchos KP, et al. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17. doi:10.1016/j.csbj.2014.11.005
   PubMed Web of Science ®Google Scholar
• Razzaghi T, Roderick O, Safro I, et al. Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values. PLoS ONE. 2016;11(5):e0155119. doi:10.1371/journal.pone.0155119
   PubMed Web of Science ®Google Scholar
• Pérez J, Iturbide E, Olivares V, et al. A Data Preparation Methodology in Data Mining Applied to Mortality Population Databases. J Med Syst. 2015 Nov;39(11):152.
   PubMed Web of Science ®Google Scholar
• Richter AN, Khoshgoftaar TM. A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artif Intell Med. 2018 Aug;90:1–14. doi:10.1016/j.artmed.2018.06.002.
   PubMed Web of Science ®Google Scholar
• Shahid N, Rappon T, Berta W, et al. Applications of artificial neural networks in health care organizational decision-making: a scoping review. PLoS ONE. 2019 Feb 19;14(2):e0212356. doi:10.1371/journal.pone.0212356
   PubMed Web of Science ®Google Scholar
• Tickle AB, Andrews R, Golea M, et al. The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Trans Neural Netw. 1998;9(6):1057–1068. doi:10.1109/72.728352
   PubMed Web of Science ®Google Scholar
• Spyridonos P, Cavouras D, Ravazoula P, et al. Neural network-based segmentation and classification system for automated grading of histologic sections of bladder carcinoma. Anal Quant Cytol Histol. 2002;24:317–324.
   PubMedGoogle Scholar
• Robertson AG, Kim J, Al-Ahmadie H, et al. Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. Cell. 2017;171(3):540–556.e25. doi:10.1016/j.cell.2017.09.007
   PubMed Web of Science ®Google Scholar
• Black AJ, Black PC. Variant histology in bladder cancer: diagnostic and clinical implications. Transl Cancer Res TCR. 2020 Oct;9(10):6565–6575. doi: 10.21037/tcr-20-2169.
   PubMed Web of Science ®Google Scholar
• Claps F, van de Kamp MW, Mayr R, et al. Prognostic impact of variant histologies in urothelial bladder cancer treated with radical cystectomy. BJU Int. 2023 Feb 6. doi: 10.1111/bju.15984
   Web of Science ®Google Scholar
• Lopez-Beltran A, Blanca A, Cimadamore A, et al. T1 bladder carcinoma with variant histology: pathological features and clinical significance. Virchows Arch. 2022;480(5):989–998. doi:10.1007/s00428-021-03264-6
   PubMed Web of Science ®Google Scholar
• Evrimler S, Ali Gedik M, Ahmet Serel T, et al. Bladder Urothelial Carcinoma: machine Learning-based Computed Tomography Radiomics for Prediction of Histological Variant. Acad Radiol. 2022;29(11):1682–1689. doi:10.1016/j.acra.2022.02.007
   Web of Science ®Google Scholar