Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2009 Summer;11(3):117-26.

Predictive models for newly diagnosed prostate cancer patients

William T Lowrance  1 , Peter T Scardino
Affiliations

Predictive models for newly diagnosed prostate cancer patients

William T Lowrance et al. Rev Urol. 2009 Summer.

Abstract

Accurate risk assessment is of paramount importance to newly diagnosed prostate cancer patients and their physicians. Risk prediction models help identify those at high (or low) risk of disease progression and guide discussions about prognosis and treatment. Widely used, well-validated prediction tools are based on standard, readily available clinical and pathologic parameters, but do not include biomarkers, some of which may have an important role in predicting prognosis or determining therapeutic options. A new approach, known as systems pathology, may improve the accuracy of traditional prediction methods and provide patients with a more personalized risk assessment of clinically relevant outcomes. The ultimate goal of prediction models is to improve medical decision making.

Keywords: Biological marker; Nomogram; Prognosis; Prostate cancer; Statistical model.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Preoperative nomogram predicting 10-year freedom from biochemical recurrence for use in patients who have chosen radical prostatectomy. Reprinted with permission from Kattan MW et al.
Figure 2
Figure 2
Overview of the systems pathology approach to risk prediction.
Figure 3
Figure 3
Kaplan-Meier estimates of the probability of disease recurrence for patients classified as nonrecurrent and recurrent by model combining gene expression signatures and clinical variables. Reprinted with permission from Stephenson AJ et al.
See this image and copyright information in PMC

References

    1. Jemal A, Siegel R, Ward E, et al. Cancer statistics, 2008. CA Cancer J Clin. 2008;58:71–96. - PubMed
    1. Andriole GL, Crawford ED, Grubb RL, 3rd, et al. Mortality results from a randomized prostate-cancer screening trial. N Engl J Med. 2009;360:1310–1319. - PMC - PubMed
    1. Schröder FH, Hugosson J, Roobol MJ, et al. Screening and prostate-cancer mortality in a randomized European study. N Engl J Med. 2009;360:1320–1328. - PubMed
    1. D’Amico AV, Whittington R, Malkowicz SB, et al. Pretreatment nomogram for prostate-specific antigen recurrence after radical prostatectomy or external-beam radiation therapy for clinically localized prostate cancer. J Clin Oncol. 1999;17:168–172. - PubMed
    1. D’Amico AV, Whittington R, Malkowicz SB, et al. The combination of preoperative prostate specific antigen and postoperative pathological findings to predict prostate specific antigen outcome in clinically localized prostate cancer. J Urol. 1998;160:2096–2101. - PubMed

LinkOut - more resources