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
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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. / Preisser, Felix; Bandini, Marco; Nazzani, Sebastiano; Mazzone, Elio; Marchioni, Michele; Tian, Zhe; Chun, Felix K H; Saad, Fred; Briganti, Alberto; Haese, Alexander; Montorsi, Francesco; Huland, Hartwig; Graefen, Markus; Tilki, Derya; Karakiewicz, Pierre I.
In: EUR UROL ONCOL, Vol. 3, No. 5, 10.2020, p. 631-639.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - 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
AU - Preisser, Felix
AU - Bandini, Marco
AU - Nazzani, Sebastiano
AU - Mazzone, Elio
AU - Marchioni, Michele
AU - Tian, Zhe
AU - Chun, Felix K H
AU - Saad, Fred
AU - Briganti, Alberto
AU - Haese, Alexander
AU - Montorsi, Francesco
AU - Huland, Hartwig
AU - Graefen, Markus
AU - Tilki, Derya
AU - Karakiewicz, Pierre I
N1 - Copyright © 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
U2 - 10.1016/j.euo.2019.03.003
DO - 10.1016/j.euo.2019.03.003
M3 - SCORING: Journal article
C2 - 31411975
VL - 3
SP - 631
EP - 639
JO - EUR UROL ONCOL
JF - EUR UROL ONCOL
SN - 2588-9311
IS - 5
ER -
