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: A Validated Tool to Predict Postoperative Prostate Cancer-related Death Using Competing-risk Analysis
Gontero, Paolo;Joniau, Steven
2018-01-01
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.
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