OBJECTIVE: To design a decision-support tool to facilitate evidence-based treatment decisions in clinically localized prostate cancer, as individualized risk assessment and shared decision-making can decrease distress and decisional regret in patients with prostate cancer, but current individual models vary or only predict one outcome of interest. METHODS: We searched Medline for previous reports and identified peer-reviewed articles providing pretreatment predictive models that estimated pathological stage and treatment outcomes in men with biopsy-confirmed, clinical T1-3 prostate cancer. Each model was entered into a spreadsheet to provide calculated estimates of extracapsular extension (ECE), seminal vesicle invasion (SVI), and lymph node involvement (LNI). Estimates of the prostate-specific antigen (PSA) outcome after radical prostatectomy (RP) or radiotherapy (RT), and clinical outcomes after RT, were also entered. The data are available at http://www.capcalculator.org. RESULTS: Entering a patient's 2002 clinical T stage, Gleason score and pretreatment PSA level, and details from core biopsy findings, into the CaP Calculator provides estimates from predictive models of pathological extent of disease, four models for ECE, four for SVI and eight for LNI. The 5-year estimates of PSA relapse-free survival after RT and 10-year estimates after RP were available. A printout can be generated with individualized results for clinicians to review with each patient. CONCLUSIONS: The CaP Calculator is a free, online 'clearing house' of several predictive models for prostate cancer, available in an accessible, user-friendly format. With further development and testing with patients, the CaP Calculator might be a useful decision-support tool to help doctors promote evidence-based shared decision-making in prostate cancer.
OBJECTIVE: To design a decision-support tool to facilitate evidence-based treatment decisions in clinically localized prostate cancer, as individualized risk assessment and shared decision-making can decrease distress and decisional regret in patients with prostate cancer, but current individual models vary or only predict one outcome of interest. METHODS: We searched Medline for previous reports and identified peer-reviewed articles providing pretreatment predictive models that estimated pathological stage and treatment outcomes in men with biopsy-confirmed, clinical T1-3prostate cancer. Each model was entered into a spreadsheet to provide calculated estimates of extracapsular extension (ECE), seminal vesicle invasion (SVI), and lymph node involvement (LNI). Estimates of the prostate-specific antigen (PSA) outcome after radical prostatectomy (RP) or radiotherapy (RT), and clinical outcomes after RT, were also entered. The data are available at http://www.capcalculator.org. RESULTS: Entering a patient's 2002 clinical T stage, Gleason score and pretreatment PSA level, and details from core biopsy findings, into the CaP Calculator provides estimates from predictive models of pathological extent of disease, four models for ECE, four for SVI and eight for LNI. The 5-year estimates of PSA relapse-free survival after RT and 10-year estimates after RP were available. A printout can be generated with individualized results for clinicians to review with each patient. CONCLUSIONS: The CaP Calculator is a free, online 'clearing house' of several predictive models for prostate cancer, available in an accessible, user-friendly format. With further development and testing with patients, the CaP Calculator might be a useful decision-support tool to help doctors promote evidence-based shared decision-making in prostate cancer.
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