Development and Validation of a Lookup Table for the Prediction of Metastatic Prostate Cancer According to Prostatic-specific Antigen Value, Clinical Tumor Stage, and Gleason Grade Groups

  • BACKGROUND: Prostate cancer (PCa) staging is crucial in clinical decision making and treatment assignment.

    OBJECTIVE: To develop a predictive tool that is capable of predicting the probability of metastases at initial PCa diagnosis.

    DESIGN, SETTING, AND PARTICIPANTS: Within the Surveillance, Epidemiology, and End Results database (2010-2014), we identified patients with newly diagnosed PCa and available clinical tumor stage, prostatic-specific antigen value (PSA), and Gleason grade group (GGG), and with or without metastases.

    OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We relied on PSA, clinical tumor stages, and GGG to discriminate between M1 and M0 patients. Patients were randomly divided according to the registry of origin between development (n=102469) and validation (n=98755) cohorts. Logistic regression modeling coefficients were used to devise a lookup table to discriminate between M0 and M1 stages. Receiver operating characteristic-derived area under the curve was tested for model accuracy, within the validation cohort. A total of 2000 bootstrap resamples were applied to 95% confidence intervals (CIs). Decision curve analysis (DCA) and calibration plots were used to test the performance of the lookup table.

    RESULTS AND LIMITATIONS: Of 201224 patients, 3.5% harbored metastatic PCa (mPCa). PSA >40ng/ml, GGG5, and GGG4, in that order, represented the strongest predictors of mPCa. Overall, PSA, clinical tumor stage, and GGG were 94.3% (95% CI: 94.2-94.3%) accurate in predicting the probability of mPCa, in the external validation cohort. Up to 39.4% probability of mPCa, the model demonstrated accurate predictions in the calibration plot. In DCA, a net benefit was recorded up to a threshold probability of approximately 54%.

    CONCLUSIONS: The proposed lookup table for the prediction of the probability of mPCa may represent a useful clinical tool based on its high accuracy, excellent calibration, and robust nature of predictions.

    PATIENT SUMMARY: Our study provides a highly accurate lookup table for the prediction of the probability of metastatic prostate cancer patients. This clinical tool can be useful in staging decisions.

  • info:eu-repo/semantics/restrictedAccess
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