1. Ha Chung B, Horie S, Chiong E. The incidence, mortality, and risk factors of prostate cancer in Asian men. Prostate Int. 2019; 7:1–8.
Article
2. Mohler JL, Antonarakis ES, Armstrong AJ, D’Amico AV, Davis BJ, Dorff T, et al. Prostate cancer, version 2.2019, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2019; 17:479–505.
3. Sanda MG, Cadeddu JA, Kirkby E, Chen RC, Crispino T, Fontanarosa J, et al. Clinically localized prostate cancer: AUA/ASTRO/SUO guideline. Part I: Risk stratification, shared decision making, and care options. J Urol. 2018; 199:683–90.
Article
4. Martinez-Gonzalez NA, Plate A, Senn O, Markun S, Rosemann T, Neuner-Jehle S. Shared decision-making for prostate cancer screening and treatment: a systematic review of randomised controlled trials. Swiss Med Wkly. 2018; 148:w14584.
Article
5. Cuypers M, Lamers RE, Cornel EB, van de Poll-Franse LV, de Vries M, Kil PJ. The impact of prostate cancer diagnosis and treatment decision-making on health-related quality of life before treatment onset. Support Care Cancer. 2018; 26:1297–304.
Article
6. Mottet N, Bellmunt J, Bolla M, Briers E, Cumberbatch MG, De Santis M, et al. EAU-ESTRO-SIOG guidelines on prostate cancer. Part 1: Screening, diagnosis, and local treatment with curative intent. Eur Urol. 2017; 71:618–29.
Article
7. Spelt L, Nilsson J, Andersson R, Andersson B. Artificial neural networks: a method for prediction of survival following liver resection for colorectal cancer metastases. Eur J Surg Oncol. 2013; 39:648–54.
8. Peng JH, Fang YJ, Li CX, Ou QJ, Jiang W, Lu SX, et al. A scoring system based on artificial neural network for predicting 10-year survival in stage II A colon cancer patients after radical surgery. Oncotarget. 2016; 7:22939–47.
Article
9. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015; 13:8–17.
Article
10. Koo KC, Lee KS, Kim S, Min C, Min GR, Lee YH, et al. Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system. World J Urol. 2020; 38:2469–76.
Article
12. van Kleef JJ, van den Boorn HG, Verhoeven RH, Vanschoenbeek K, Abu-Hanna A, Zwinderman AH, et al. External validation of the Dutch SOURCE survival prediction model in Belgian metastatic oesophageal and gastric cancer patients. Cancers (Basel). 2020; 12:834.
Article
13. D’Amico AV, Cote K, Loffredo M, Renshaw AA, Schultz D. Determinants of prostate cancer-specific survival after radiation therapy for patients with clinically localized prostate cancer. J Clin Oncol. 2002; 20:4567–73.
Article
14. Cooperberg MR, Broering JM, Carroll PR. Risk assessment for prostate cancer metastasis and mortality at the time of diagnosis. J Natl Cancer Inst. 2009; 101:878–87.
Article
15. Oh SE, Seo SW, Choi MG, Sohn TS, Bae JM, Kim S. Prediction of overall survival and novel classification of patients with gastric cancer using the survival recurrent network. Ann Surg Oncol. 2018; 25:1153–9.
Article
16. Obrzut B, Kusy M, Semczuk A, Obrzut M, Kluska J. Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods. BMC Cancer. 2017; 17:840.
Article
17. Qiao G, Li J, Huang A, Yan Z, Lau WY, Shen F. Artificial neural networking model for the prediction of post-hepatectomy survival of patients with early hepatocellular carcinoma. J Gastroenterol Hepatol. 2014; 29:2014–20.
18. Snow PB, Rodvold DM, Brandt JM. Artificial neural networks in clinical urology. Urology. 1999; 54:787–90.
Article
19. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010; 21:128–38.
20. Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med. 2016; 35:214–26.
Article