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Building Race-Specific Models for Disease Progression and Health-Related Quality of Life for Prostate Cancer Patients

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Technical Report,30 Sep 2015,29 Sep 2019

Corporate Author:

Henry M. Jackson Foundation CPDR Bethesda United States

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One in six American men will be diagnosed with prostate cancer during his life-time. The burden of disease is particularly heavy among African Americans, who have a 1.5-fold incidence 2.3 fold mortality rate, as compared to Caucasians. Moreover, significant racial differences may exist in health-related quality of life HRQoL. Understanding the natural history of disease progression following cancer therapy is critical for informed treatment decision-making and for the provision of optimal cancer care. However, data on the disease course, including HRQoL, among African Americans are extremely limited as a result, hundreds of thousands of African American patients have to make difficult treatment decisions without data pertinent to them. Altogether, these factors highlight the importance of generating race-specific data about the prostate cancer disease course and its associated HRQoL along the continuum of care. One of the most challenging issues in managing prostate cancer is to identify patients who are most likely to benefit from timely intervention among patients with biochemical recurrence to prevent downstream outcomes, such as metastasis and cancer-specific death. Predictive tools that can risk stratify patients are urgently needed to guide treatment decisions. However, very few existing predictive tools are available specifically for African Americans. HypothesisObjectives It is hypothesized that the knowledge gained and predictive tools i.e., nomograms generated from this study, based on comprehensive, high-quality, longitudinal clinical data, will provide precise, individualized risk estimates of prostate cancer progression and identify key predictors of cancer progression among African Americans.

Subject Categories:

  • Medicine and Medical Research
  • Sociology and Law

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