Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators

Journal of Biomedical Informatics

Volumn 56, Issue , 2015, Pages 87-93

  Strobl, Andreas N ^a,b   Vickers, Andrew J ^c   Van Calster, Ben ^d   Steyerberg, Ewout ^e   Leach, Robin J ^f   Thompson, Ian M ^f   Ankerst, Donna P ^a,b,f,g  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b INSTITUTE OF EPIDEMIOLOGY   (Germany)

^c MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^d UNIVERSITY OF LEUVEN   (Belgium)

^e ERASMUS MEDICAL CENTER   (Netherlands)

^f Mays Cancer Center at UT Health San Antonio   (United States)

^g UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT SAN ANTONIO   (United States)

Author keywords

Calibration; Discrimination; Logistic regression; Prediction; Prostate cancer; Revision

Indexed keywords

CALIBRATION; DECISION TREES; DISEASES; FORECASTING; MATHEMATICAL INSTRUMENTS; REGRESSION ANALYSIS; SOCIAL NETWORKING (ONLINE); UROLOGY;

BAYESIAN PRIORS; CONCERNING RISK; DISCRIMINATION; LOGISTIC REGRESSIONS; PROSTATE BIOPSY; PROSTATE CANCERS; REVISION; RISK PREDICTIONS;

RISK ASSESSMENT;

ACCURACY; ADULT; AGED; ARTICLE; BAYES THEOREM; CALCULATOR; CALIBRATION; CANCER RISK; CASE STUDY; CONTROLLED STUDY; HUMAN; INTERMETHOD COMPARISON; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; ONLINE SYSTEM; PREDICTION; PRIORITY JOURNAL; PROSTATE CANCER; PROSTATE CANCER PREVENTION TRIAL RISK CALCULATOR; RANDOM FOREST; RISK ASSESSMENT; STATISTICAL ANALYSIS; ALGORITHM; AUSTRIA; BIOPSY; BLOOD; CLINICAL TRIAL; COHORT ANALYSIS; ENGLAND; FACTUAL DATABASE; MIDDLE AGED; MULTICENTER STUDY; PROCEDURES; PROSTATIC NEOPLASMS; REPRODUCIBILITY; STATISTICAL MODEL; UNITED STATES; VERY ELDERLY;

PROSTATE SPECIFIC ANTIGEN;

ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; AUSTRIA; BAYES THEOREM; BIOPSY; CALIBRATION; COHORT STUDIES; DATABASES, FACTUAL; ENGLAND; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; PROSTATE-SPECIFIC ANTIGEN; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; UNITED STATES;

EID: 84938573721     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2015.05.001     Document Type: Article

References (44)

1. Wilson P.W., D'Agostino R.B., Levy D., Belanger A.M., Silbershatz H., Kannel W.B. Prediction of coronary heart disease using risk factor categories. Circulation 1998, 97(18):1837-1847.
2. Gail M.H., Brinton L.A., Byar D.P., Corle D.K., Green S.B., Schairer C., et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J. Natl Cancer Inst. 1989, 81(24):1879-1886.
3. Thompson I.M., Ankerst D.P., Chi C., Goodman P.J., Tangen C.M., Lucia M.S., et al. Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial. J. Natl Cancer Inst. 2006, 98(8):529-534.
4. D.C. Goff Jr., D.M. Lloyd-Jones, G. Bennett et al., 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, J. Am. Coll. Cardiol. 63(25 Pt B) (2014) 2935-2959 (published correction appears in J. Am. Coll. Cardiol. 63(25 Pt B) (2014) 3026).
5. Ridker P.M., Cook N.R. Statins: new American guidelines for prevention of cardiovascular disease. Lancet 2013, 382(9907):1762-1765.
6. Vickers A.J., Fearn P., Kattan M.W., Scardino P.T. Why can't nomograms be more like Netflix?. Urology 2010, 75(3):511-513.
7. Thompson I.M., Goodman P.J., Tangen C.M., et al. The influence of finasteride on the development of prostate cancer. N. Engl. J. Med. 2003, 349(3):215-224.
8. Thompson I.M., Pauler D.K., Goodman P.J., et al. Prevalence of prostate cancer among men with a prostate-specific antigen level
9. Parekh D.J., Ankerst D.P., Higgins B.A., et al. External validation of the Prostate Cancer Prevention Trial risk calculator in a screened population. Urology 2006, 68(6):1152-1155.
10. Eyre S.J., Ankerst D.P., Wei J.T., et al. Validation in a multiple urology practice cohort of the Prostate Cancer Prevention Trial calculator for predicting prostate cancer detection. J. Urol. 2009, 182(6):2653-2658.
11. Hernandez D.J., Han M., Humphreys E.B., et al. Predicting the outcome of prostate biopsy: comparison of a novel logistic regression-based model, the prostate cancer risk calculator, and prostate-specific antigen level alone. BJU Int. 2009, 103(5):609-614.
12. Cavadas V., Osorio L., Sabell F., et al. Prostate cancer prevention trial and European randomized study of screening for prostate cancer risk calculators: a performance comparison in a contemporary screened cohort. Eur. Urol. 2010, 58(4):551-558.
13. Kaplan D.J., Boorjian S.A., Ruth K., et al. Evaluation of the Prostate Cancer Prevention Trial Risk calculator in a high-risk screening population. BJU Int. 2010, 105(3):334-337.
14. Nam R.K., Kattan M.W., Chin J.L., et al. Prospective multi-institutional study evaluating the performance of prostate cancer risk calculators. J. Clin. Oncol. 2011, 29(22):2959-2964.
15. Trottier G., Roobol M.J., Lawrentschuk N., et al. Comparison of risk calculators from the Prostate Cancer Prevention Trial and the European Randomized Study of Screening for Prostate Cancer in a contemporary Canadian cohort. BJU Int. 2011, 108(8b):E237-E244.
16. Oliveira M., Marques V., Carvalho A.P., et al. Head-to-head comparison of two online nomograms for prostate biopsy outcome prediction. BJU Int. 2011, 107(11):1780-1783.
17. Zhu Y., Wang J.Y., Shen Y.J., et al. External validation of the Prostate Cancer Prevention Trial and the European Randomized Study of Screening for Prostate risk calculators in a Chinese cohort. Asian J Androl. 2012, 14(5):738-744.
18. Ankerst D.P., Boeck A., Freedland S.J., et al. Evaluating the PCPT risk calculator in ten international biopsy cohorts: results from the Prostate Biopsy Collaborative Group. World J. Urol. 2012, 30(2):181-187.
19. Lee D.H., Jung H.B., Park J.W., et al. Can Western based online prostate cancer risk calculators be used to predict prostate cancer after prostate biopsy for the Korean population?. Yonsei Med. J. 2013, 54(3):665-671.
20. Ankerst D.P., Boeck A., Freedland S.J., et al. Evaluating the Prostate Cancer Prevention Trial High Grade prostate cancer risk calculator in 10 international biopsy cohorts: results from the prostate biopsy collaborative group. World J. Urol. 2014, 32(1):185-191.
21. Vickers A.J., Cronin A.M., Roobol M.J., et al. The relationship between prostate-specific antigen and prostate cancer risk: the Prostate Biopsy Collaborative Group. Clin. Cancer Res. 2010, 16(17):4374-4381.
22. Ankerst D.P., Till C., Boeck A., et al. The impact of prostate volume, number of biopsy cores and American Urological Association symptom score on the sensitivity of cancer detection using the Prostate Cancer Prevention Trial risk calculator. J. Urol. 2013, 190(1):70-76.
23. Steyerberg E.W. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating 2009, Springer, New York.
24. Janssen K.J., Moons K.G., Kalkman C.J., et al. Updating methods improved the performance of a clinical prediction model in new patients. J. Clin. Epidemiol. 2008, 61(1):76-86.
25. Schuetz P., Koller M., Christ-Crain M., et al. Predicting mortality with pneumonia severity scores: importance of model recalibration to local settings. Epidemiol. Infect. 2008, 136(12):1628-1637.
26. Genders T.S., Steyerberg E.W., Alkadhi H., et al. A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension. Eur. Heart J. 2011, 32(11):1316-1330.
27. Van Hoorde K., Vergouwe Y., Timmerman D., et al. Simple dichotomous updating methods improved the validity of polytomous prediction models. J. Clin. Epidemiol. 2013, 66(10):1158-1165.
28. Kong C.H., Guest G.D., Stupart D.A., et al. Recalibration and validation of a preoperative risk prediction model for mortality in major colorectal surgery. Dis. Colon Rectum 2013, 56(7):844-849.
29. Visser I.H., Hazelzet J.A., Albers M.J., et al. Mortality prediction models for pediatric intensive care: comparison of overall and subgroup specific performance. Intensive Care Med. 2013, 39(5):942-950.
30. Minne L., Eslami S., de Keizer N., et al. Effect of changes over time in the performance of a customized SAPS-II model on the quality of care assessment. Intensive Care Med. 2012, 38(1):40-46.
31. Minne L., Eslami S., de Keizer N., et al. Statistical process control for validating a classification tree model for predicting mortality-a novel approach towards temporal validation. J. Biomed. Inform. 2012, 45(1):37-44.
32. Wiens J., Guttag J., Horvitz E. A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions. J. Am. Med. Inform. Assoc. 2014, 21(4):699-706.
33. Ankerst D.P., Hoefler J., Bock S., et al. Prostate Cancer Prevention Trial risk calculator 2.0 for the prediction of low- versus high-grade prostate cancer. Urology 2014, 83(6):1362-1367.
34. Pauler D.K. The Schwarz criterion and related methods for Normal linear models. Biometrika 1998, 85:13-27.
35. Breiman L. Random forests. Machine Learning 2001, 45(1):5-32.
36. Ware J.H. The limitations of risk factors as prognostic tools. N. Engl. J. Med. 2006, 355(25):2615-2617.
37. Ankerst D.P., Koniarski T., Liang Y., et al. Updating risk prediction tools: a case study in prostate cancer. Biom J. 2012, 54(1):127-142.
38. Hickey G.L., Grant S.W., Murphy G.J., et al. Dynamic trends in cardiac surgery: why the logistic EuroSCORE is no longer suitable for contemporary cardiac surgery and implications for future risk models. Eur. J. Cardiothorac. Surg. 2013, 43(6):1146-1152.
39. Hickey G.L., Grant S.W., Caiado C., et al. Dynamic prediction modeling approaches for cardiac surgery. Circ. Cardiovasc. Qual. Outcomes. 2013, 6(6):649-658.
40. G. Lee, I. Rubinfeld, Z. Syed, Adapting surgical models to individual hospitals using transfer learning, IEEE ICDM 2012 Workshop on Biological Data Mining and its Applications in Healthcare (BioDM), 2012.
41. Steyerberg E.W., Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur. Heart J. 2014, 35(29):1925-1931.
42. Vickers A.J., Elkin E.B. Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Making 2006, 26(6):565-574.
43. Van Hoorde K., Van Huffel S., Timmerman D., et al. A spline-based tool to assess and visualize the calibration of multiclass risk predictions. J. Biomed. Inform. 2015, 54:283-293.
44. Debray T.P., Vergouwe Y., Koffijberg H., et al. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J. Clin. Epidemiol. 2015, 68(3):279-289.