The EMPaCT Classifier

A Validated Tool to Predict Postoperative Prostate Cancer-related Death Using Competing-risk Analysis

Lorenzo Tosco, Annouschka Laenen, Alberto Briganti, Paolo Gontero, Robert Jeffrey Karnes, Patrick J. Bastian, Piotr Chlosta, Frank Claessens, Felix K. Chun, Wouter Everaerts, Christian Gratzke, Maarten Albersen, Markus Graefen, Burkhard Kneitz, Giansilvio Marchioro, Rafael Sanchez Salas, Bertrand Tombal, Thomas Van den Broeck, Henk Van Der Poel, Jochen Walz & 6 others Gert De Meerleer, Alberto Bossi, Karin Haustermans, Hendrik Van Poppel, Martin Spahn, Steven Joniau

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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. The EMPaCT classifier can accurately predict the survival of patients with high-risk prostate cancer. The EMPaCT classifier can become a novel standard to support decision-making in the multimodal setting.

Original languageEnglish (US)
JournalEuropean Urology Focus
DOIs
StateAccepted/In press - 2017

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Prostatic Neoplasms
Prostatectomy
Neoplasm Grading
Lymph Node Excision
Mortality
Decision Making
Survival
Observational Studies
Cohort Studies
Therapeutics
Neoplasms

Keywords

  • High-risk disease
  • Prognosis
  • Prostate cancer
  • Surgery

ASJC Scopus subject areas

  • Urology

Cite this

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, Robert 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.

In: European Urology Focus, 2017.

Research output: Contribution to journalArticle

Tosco, L, Laenen, A, Briganti, A, Gontero, P, Karnes, RJ, Bastian, PJ, Chlosta, P, Claessens, F, Chun, FK, Everaerts, W, Gratzke, C, Albersen, M, Graefen, M, Kneitz, B, Marchioro, G, Salas, RS, Tombal, B, Van den Broeck, T, Van Der Poel, H, Walz, J, De Meerleer, G, Bossi, A, Haustermans, K, Van Poppel, H, Spahn, M & Joniau, S 2017, 'The EMPaCT Classifier: A Validated Tool to Predict Postoperative Prostate Cancer-related Death Using Competing-risk Analysis', European Urology Focus. https://doi.org/10.1016/j.euf.2016.12.008
Tosco, Lorenzo ; Laenen, Annouschka ; Briganti, Alberto ; Gontero, Paolo ; Karnes, Robert 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. / The EMPaCT Classifier : A Validated Tool to Predict Postoperative Prostate Cancer-related Death Using Competing-risk Analysis. In: European Urology Focus. 2017.
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title = "The EMPaCT Classifier: A Validated Tool to Predict Postoperative Prostate Cancer-related Death Using Competing-risk Analysis",
abstract = "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. The EMPaCT classifier can accurately predict the survival of patients with high-risk prostate cancer. The EMPaCT classifier can become a novel standard to support decision-making in the multimodal setting.",
keywords = "High-risk disease, Prognosis, Prostate cancer, Surgery",
author = "Lorenzo Tosco and Annouschka Laenen and Alberto Briganti and Paolo Gontero and Karnes, {Robert Jeffrey} and Bastian, {Patrick J.} and Piotr Chlosta and Frank Claessens and Chun, {Felix K.} and Wouter Everaerts and Christian Gratzke and Maarten Albersen and Markus Graefen and Burkhard Kneitz and Giansilvio Marchioro and Salas, {Rafael Sanchez} and Bertrand Tombal and {Van den Broeck}, Thomas and {Van Der Poel}, Henk and Jochen Walz and {De Meerleer}, Gert and Alberto Bossi and Karin Haustermans and {Van Poppel}, Hendrik and Martin Spahn and Steven Joniau",
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language = "English (US)",
journal = "European Urology Focus",
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TY - JOUR

T1 - The EMPaCT Classifier

T2 - 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, Robert 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

PY - 2017

Y1 - 2017

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. The EMPaCT classifier can accurately predict the survival of patients with high-risk prostate cancer. The EMPaCT classifier can become a novel standard to support decision-making in the multimodal setting.

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. The EMPaCT classifier can accurately predict the survival of patients with high-risk prostate cancer. The EMPaCT classifier can become a novel standard to support decision-making in the multimodal setting.

KW - High-risk disease

KW - Prognosis

KW - Prostate cancer

KW - Surgery

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