Uneingeschränkter Zugang

Decision Tree Analysis for Prostate Cancer Prediction in Patients with Serum PSA 10 ng/ml or Less


Zitieren

1. Arnold M, Karim-Kos HE, Coebergh JW, Byrnes G, Antilla A, Ferlay J, et al. (2015). Recent trends in incidence of five common cancers in 26 European countries since 1988:Analysis of the European Cancer Observatory.Eur J Cancer.51(9):1164-87.DOI: 10.1016/j.ejca.2013.09.002.10.1016/j.ejca.2013.09.00224120180Search in Google Scholar

2. Bul M, Zhu X, Valdagni R, Pickles T, Kakehi Y, Ran-nikko A, et al. (2013). Active surveillance for low-risk prostate cancer worldwide: the PRIAS study. Eur Urol. 63(4):597-603. DOI: 10.1016/j.eururo.2012.11.005.10.1016/j.eururo.2012.11.00523159452Search in Google Scholar

3. Catalona WJ, Southwick PC, Slawin KM, Partin AW, Brawer MK, Flanigan RC, et al. (2000). Comparison of percent free PSA, PSA density, and age-specific PSA cutoffs for prostate cancer detection and staging. Urology. 1;56(2):255-60.10.1016/S0090-4295(00)00637-3Search in Google Scholar

4. Carter HB, & Pearson JD. (1997). Prostate-specific antigen velocity and repeated measures of prostate-specific antigen. Urol Clin North Am. 24(2):333-8.10.1016/S0094-0143(05)70380-3Search in Google Scholar

5. Benson MC, Whang IS, Pantuck A, Ring K, Kaplan SA, Olsson CA, et al. (1992). Prostate specific antigen density: a means of distinguishing benign prostatic hyper-trophy and prostate cancer. J Urol.147(3 Pt 2):815-6.10.1016/S0022-5347(17)37393-7Search in Google Scholar

6. Djavan B, Remzi M, Zlotta A, Seitz C, Snow P, & Marberger M. (2002). Novel artificial neural network for early detection of prostate cancer. Clin.Onkol. 20(4):921-929.DOI:10.1200/JCO.2002.20.4.921.10.1200/JCO.2002.20.4.92111844812Search in Google Scholar

7. Garzotto M, Hudson RG, Peters L, Hsieh YC, Barrera E, Mori M., et al. (2003). Predictive modeling for the presence of prostate carcinoma using clinical, laboratory, and ultrasound parameters in patients with prostate specific antigen levels < or = 10 ng/ml. Cancer. 1;98(7):1417-22.DOI:10.1002/cncr.11668.10.1002/cncr.1166814508828Search in Google Scholar

8. Filella X, & Giménez N. (2013). Evaluation of [-2] proPSA and Prostate Health Index (phi) for the detection of prostate cancer: a systematic review and meta-analysis. Clin Chem Lab Med. 51(4):729-39. DOI:10.1515/cclm-2012-0410.10.1515/cclm-2012-041023154423Search in Google Scholar

9. Parekh DJ, Punnen S, Sjoberg DD, Asroff SW, Bailen JL, Cochran JS, et al. (2015). A multi-institutional prospective trial in the USA confirms that the 4Kscore accurately identifies men with high-grade prostate cancer. Eur Urol. 68(3):464-70.DOI: 10.1016/j.eururo.2014.10.021.10.1016/j.eururo.2014.10.02125454615Search in Google Scholar

10. Tomlins SA, Day JR, Lonigro RJ, Hovelson DH, Siddiqui J, Kunju LP, et al. (2016). Urine TMPRSS2:ERG Plus PCA3 for Individualized Prostate Cancer Risk Assessment. Eur Urol. 70(1):45-53. DOI: 10.1016/j.eururo.2015.04.039.10.1016/j.eururo.2015.04.039464472425985884Search in Google Scholar

11. Lughezzani G, Lazzeri M, Larcher A, Lista G, Scattoni V, Cestari A, et al. (2012). Development and internal validation of a Prostate Health Index based nomogram for predicting prostate cancer at extended biopsy. J Urol. 188(4):1144-50.DOI: 10.1016/j.juro.2012.06.025.10.1016/j.juro.2012.06.02522901589Search in Google Scholar

12. Chun FK, Graefen M, Briganti A, Gallina A, Hopp J, Kattan MW, et al. (2006). Initial biopsy outcome prediction--head-to-head comparison of a logistic regression-based nomogram versus artificial neural network. EurUrol. 51(5):1236-40.DOI:10.1016/j.eururo.2006.07.021.10.1016/j.eururo.2006.07.02116945477Search in Google Scholar

13. Karakiewicz PI, Benayoun S, Kattan MW, Perrotte P, Valiquette L, Scardino PT, et al. (2005). Development and validation of a nomogram predicting the outcome of prostate biopsy based on patient age, digital rectal examination and serum prostate specific antigen. J Urol. 173(6):1930-4.DOI:10.1097/01. ju.0000158039.94467.5d.10.1097/01Search in Google Scholar

14. Ankerst DP, Hoefler J, Bock S, Goodman PJ, Vickers A, Hernandez J, et al. (2014). Prostate Cancer Prevention Trial risk calculator 2.0 for the prediction of lowvs high-grade prostate cancer.Urology.83(6):1362-7. DOI:10.1016/j.urology.2014.02.035.10.1016/j.urology.2014.02.035Search in Google Scholar

15. Roobol MJ, Schröder FH, Hugosson J, Jones JS, Kattan MW, Klein EA, et al. (2012). Importance of prostate volume in the European Randomised Study of Screening for Prostate Cancer (ERSPC) risk calculators: results from the prostate biopsy collaborative group. World J Urol. 30(2):149–55. DOI: 10.1007/s00345-011-0804-y.10.1007/s00345-011-0804-ySearch in Google Scholar

16. Park JY, Yoon S, Park MS, Choi H, Bae JH, Moon DG, et al. (2017), Development and External Validation of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer: Comparison with Two Western Risk Calculators in an Asian Cohort. PLoS One.12(1):e0168917.DOI:10.1371/journal. pone.0168917.10.1371/journal.pone.0168917Search in Google Scholar

17. Spurgeon SE, Hsieh YC, Rivadinera A, Beer TM, Mori M, & Garzotto M. (2006). Classification and regression tree analysis for the prediction of aggressive prostate cancer on biopsy. J Urol. 175(3 Pt 1):918-22. DOI:10.1016/S0022-5347(05)00353-8.10.1016/S0022-5347(05)00353-8Search in Google Scholar

18. Garzotto M, Beer TM, Hudson RG, Peters L, Hsieh YC, Barrera E, et al. (2005). Improved detection of prostate cancer using classification and regression tree analysis. J Clin Oncol. 23(19):4322-9. DOI: 10.1200/JCO.2005.11.136.10.1200/JCO.2005.11.13615781880Search in Google Scholar

19. Briganti A, Passoni N, Ferrari M, Capitanio U, Suardi N, Gallina A, et al. (2010). When to perform bone scan in patients with newly diagnosed prostate cancer: external validation of the currently available guidelines and proposal of a novel risk stratification tool. Eur Urol. 57(4):551-8.DOI:10.1016/j.eururo.2009.12.023.10.1016/j.eururo.2009.12.02320034730Search in Google Scholar

20. DeLong ER, DeLong DM, & Clarke-Pearson DL. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 44(3):837-45.10.2307/2531595Search in Google Scholar

21. Hwang SH, Pyo T, Oh HB, Park HJ, & Lee KJ. (2013). Combined application of information theory on laboratory results with classification and regression tree analysis: analysis of unnecessary biopsy for prostate cancer. Clin Chim Acta. 415:133-7. DOI: 10.1016/j. cca.2012.10.012.10.1016/j.cca.2012.10.012Search in Google Scholar

22. Milkovic B, Dzamic Z, Pejcic T, Kajmakovic B, Nikolic D, Cirovic D, et al. (2014). Evaluation of free-to-total prostate specific antigen (F/T PSA), prostate specific antigen density (PSAD) and (F/T)/PSAD sensitivity on reduction of unnecessary prostate biopsies for patients with PSA in gray zone. Ann Ital Chir.85(5):448-53.Search in Google Scholar

23. Sfoungaristos S, & Perimenis P. (2012) PSA density is superior than PSA and Gleason score for adverse pathologic features prediction in patients with clinically localized prostate cancer. Can Urol Assoc J. 6(1):46-50. DOI:10.5489/cuaj.11079.10.5489/cuaj.329Search in Google Scholar

24. Nowroozi MR, Momeni SA, Ohadian Moghadam S, Ayati E, Mortazavi A, Arfae S, et al. (2016). Prostate-Specific Antigen Density and Gleason Score Predict Adverse Pathologic Features in Patients with Clinically Localized Prostate Cancer. Nephrourol. Mon. 8(6):e39984. eCollection.DOI:10.5812/numonthly.39984.10.5812/numonthly.39984512023427896239Search in Google Scholar

25. Kotb AF, Tanguay S, Luz MA, Kassouf W, & Aprikian AG. (2011). Relationship between initial PSA density with future PSA kinetics and repeat biopsies in men with prostate cancer on active surveillance. Prostate Cancer Prostatic Dis.14(1):53-7.DOI: 10.1038/pcan.2010.36.10.1038/pcan.2010.36303698120938463Search in Google Scholar

26. Sun L, Caire AA, Robertson CN, George DJ, Polascik TJ, Maloney KE, et al. (2009). Men older than 70 years have higher risk prostate cancer and poorer survival in the early and late prostate specific antigen eras. J Urol.182(5):2242-8.DOI: 10.1016/j.juro.2009.07.034.10.1016/j.juro.2009.07.03419758616Search in Google Scholar

27. Pepe P, & Pennisi M. (2015). Gleason score stratification according to age at diagnosis in 1028 men. Contemp Oncol (Pozn). 19(6):471-3. DOI: 10.5114/wo.2015.56654.10.5114/wo.2015.56654473145426843845Search in Google Scholar

28. Louie KS, Seigneurin A, Cathcart P, & Sasieni P. (2015). Do prostate cancer risk models improve the predictive accuracy of PSA screening?. A metaanalysis. Ann Oncol.26(5):848–64.DOI:10.1093/annonc/mdu525.10.1093/annonc/mdu52525403590Search in Google Scholar

29. Bjurlin MA, & Taneja SS. (2014). Standards for prostate biopsy. Curr Opin Urol. 24(2):155-61. DOI: 10.1097/MOU.0000000000000031.10.1097/MOU.0000000000000031414219624451092Search in Google Scholar

30. Schiavina R, Borghesi M, Brunocilla E, Romagnoli D, Diazzi D,Giunchi F, et al. (2015). The biopsy Gleason score 3+4 in a single core does not necessarily reflect an unfavourable pathological disease after radical prostatectomy in comparison with biopsy Gleason score 3+3: looking for larger selection criteria for active surveillance candidates.Prostate Cancer Prostatic Dis.18(3):270-5. DOI: 10.1038/pcan.2015.21.10.1038/pcan.2015.2126055663Search in Google Scholar

31. Kim SB, Cho IC, & Min SK. (2014). Prostate volume measurement by transrectal ultrasonography: comparison of height obtained by use of transaxial and midsagittal scanning. Korean J Urol. 55(7):470-4. DOI: 10.4111/kju.2014.55.7.470.10.4111/kju.2014.55.7.470410111725045446Search in Google Scholar

eISSN:
2335-075X
ISSN:
1820-8665
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Medizin, Klinische Medizin, andere