The EMPaCT Classifier: A Validated Tool to Predict Postoperative Prostate Cancer-related Death Using Competing-risk Analysis
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The EMPaCT Classifier: A Validated Tool to Predict Postoperative Prostate Cancer-related Death Using Competing-risk Analysis. / Tosco, Lorenzo; Laenen, Annouschka; Briganti, Alberto; Gontero, Paolo; Karnes, R Jeffrey; Bastian, Patrick J; Chlosta, Piotr; Claessens, Frank; Chun, Felix K; Everaerts, Wouter; Gratzke, Christian; Albersen, Maarten; Graefen, Markus; Kneitz, Burkhard; Marchioro, Giansilvio; Salas, Rafael Sanchez; Tombal, Bertrand; Van den Broeck, Thomas; Van Der Poel, Henk; Walz, Jochen; De Meerleer, Gert; Bossi, Alberto; Haustermans, Karin; Van Poppel, Hendrik; Spahn, Martin; Joniau, Steven; European Multicenter Prostate Cancer Clinical and Translational Research group (EMPaCT).
in: EUR UROL FOCUS, Jahrgang 4, Nr. 3, 04.2018, S. 369-375.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - The EMPaCT Classifier: A Validated Tool to Predict Postoperative Prostate Cancer-related Death Using Competing-risk Analysis
AU - Tosco, Lorenzo
AU - Laenen, Annouschka
AU - Briganti, Alberto
AU - Gontero, Paolo
AU - Karnes, R Jeffrey
AU - Bastian, Patrick J
AU - Chlosta, Piotr
AU - Claessens, Frank
AU - Chun, Felix K
AU - Everaerts, Wouter
AU - Gratzke, Christian
AU - Albersen, Maarten
AU - Graefen, Markus
AU - Kneitz, Burkhard
AU - Marchioro, Giansilvio
AU - Salas, Rafael Sanchez
AU - Tombal, Bertrand
AU - Van den Broeck, Thomas
AU - Van Der Poel, Henk
AU - Walz, Jochen
AU - De Meerleer, Gert
AU - Bossi, Alberto
AU - Haustermans, Karin
AU - Van Poppel, Hendrik
AU - Spahn, Martin
AU - Joniau, Steven
AU - European Multicenter Prostate Cancer Clinical and Translational Research group (EMPaCT)
N1 - Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.
PY - 2018/4
Y1 - 2018/4
N2 - BACKGROUND: Accurate prediction of survival after radical prostatectomy (RP) is important for making decisions regarding multimodal therapies. There is a lack of tools to predict prostate cancer-related death (PCRD) in patients with high-risk features.OBJECTIVE: To develop and validate a prognostic model that predicts PCRD combining pathologic features and using competing-risks analysis.DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective multi-institutional observational cohort study of 5876 patients affected by high-risk prostate cancer. Patients were treated using RP and pelvic lymph node dissection (PLND) in a multimodal setting, with median follow-up of 49 mo.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: For PCRD prediction, a multivariate model with correction for competing risks was constructed to evaluate pathologic high-risk features (pT3b-4, Gleason score ≥8, and pN1) as predictors of mortality. All possible associations of the predictors were combined, and then subgroups with similar risk of PCRD were collapsed to obtain a simplified model encoding subgroups with significantly differing risk. Eightfold cross-validation of the model was performed.RESULTS AND LIMITATIONS: After applying exclusion criteria, 2823 subjects were identified. pT3b-4, Gleason score ≥8, and pN1 were all independent predictors of PCRD. The simplified model included the following prognostic groups: good prognosis, pN0 with 0-1 additional predictors; intermediate prognosis, pN1 with 0-1 additional predictors; poor prognosis, any pN with two additional predictors. The cross-validation yielded excellent median model accuracy of 88%. The retrospective design and the short follow-up could limit our findings.CONCLUSIONS: We developed and validated a novel and easy-to-use prognostic instrument to predict PCRD after RP+PLND. This model may allow clinicians to correctly counsel patients regarding the intensity of follow-up and to tailor adjuvant treatments.PATIENT SUMMARY: Prediction of mortality after primary surgery for prostate cancer is important for subsequent treatment plans. We present an accurate postoperative model to predict cancer mortality after radical prostatectomy for high-risk prostate cancer.
AB - BACKGROUND: Accurate prediction of survival after radical prostatectomy (RP) is important for making decisions regarding multimodal therapies. There is a lack of tools to predict prostate cancer-related death (PCRD) in patients with high-risk features.OBJECTIVE: To develop and validate a prognostic model that predicts PCRD combining pathologic features and using competing-risks analysis.DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective multi-institutional observational cohort study of 5876 patients affected by high-risk prostate cancer. Patients were treated using RP and pelvic lymph node dissection (PLND) in a multimodal setting, with median follow-up of 49 mo.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: For PCRD prediction, a multivariate model with correction for competing risks was constructed to evaluate pathologic high-risk features (pT3b-4, Gleason score ≥8, and pN1) as predictors of mortality. All possible associations of the predictors were combined, and then subgroups with similar risk of PCRD were collapsed to obtain a simplified model encoding subgroups with significantly differing risk. Eightfold cross-validation of the model was performed.RESULTS AND LIMITATIONS: After applying exclusion criteria, 2823 subjects were identified. pT3b-4, Gleason score ≥8, and pN1 were all independent predictors of PCRD. The simplified model included the following prognostic groups: good prognosis, pN0 with 0-1 additional predictors; intermediate prognosis, pN1 with 0-1 additional predictors; poor prognosis, any pN with two additional predictors. The cross-validation yielded excellent median model accuracy of 88%. The retrospective design and the short follow-up could limit our findings.CONCLUSIONS: We developed and validated a novel and easy-to-use prognostic instrument to predict PCRD after RP+PLND. This model may allow clinicians to correctly counsel patients regarding the intensity of follow-up and to tailor adjuvant treatments.PATIENT SUMMARY: Prediction of mortality after primary surgery for prostate cancer is important for subsequent treatment plans. We present an accurate postoperative model to predict cancer mortality after radical prostatectomy for high-risk prostate cancer.
KW - Journal Article
U2 - 10.1016/j.euf.2016.12.008
DO - 10.1016/j.euf.2016.12.008
M3 - SCORING: Journal article
C2 - 28753838
VL - 4
SP - 369
EP - 375
JO - EUR UROL FOCUS
JF - EUR UROL FOCUS
SN - 2405-4569
IS - 3
ER -