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Nomogram Predicting Downgrading in National Comprehensive Cancer Network High-risk Prostate Cancer Patients Treated with Radical Prostatectomy

M. Wenzel, C. Würnschimmel, F. Chierigo, RS. Flammia, Z. Tian, SF. Shariat, M. Gallucci, C. Terrone, F. Saad, D. Tilki, M. Graefen, A. Becker, LA. Kluth, P. Mandel, FKH. Chun, PI. Karakiewicz

. 2022 ; 8 (5) : 1133-1140. [pub] 20210730

Jazyk angličtina Země Nizozemsko

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc22033333

BACKGROUND: Some high-risk prostate cancer (PCa) patients may show more favorable Gleason pattern at radical prostatectomy (RP) than at biopsy. OBJECTIVE: To test whether downgrading could be predicted accurately. DESIGN, SETTING, AND PARTICIPANTS: Within the Surveillance, Epidemiology and End Results database (2010-2016), 6690 National Comprehensive Cancer Network (NCCN) high-risk PCa patients were identified. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES: We randomly split the overall cohort between development and validation cohorts (both n = 3345, 50%). Multivariable logistic regression models used biopsy Gleason, prostate-specific antigen, number of positive prostate biopsy cores, and cT stage to predict downgrading. Accuracy, calibration, and decision curve analysis (DCA) tested the model in the external validation cohort. RESULTS AND LIMITATIONS: Of 6690 patients, 50.3% were downgraded at RP, and of 2315 patients with any biopsy pattern 5, 44.1% were downgraded to RP Gleason pattern ≤4 + 4. Downgrading rates were highest in biopsy Gleason pattern 5 + 5 (84.1%) and lowest in 3 + 4 (4.0%). In the validation cohort, the logistic regression model-derived nomogram predicted downgrading with 71.0% accuracy, with marginal departures (±3.3%) from ideal predictions in calibration. In DCA, a net benefit throughout all threshold probabilities was recorded, relative to treat-all or treat-none strategies and an algorithm based on an average downgrading rate of 50.3%. All steps were repeated in the subgroup with any biopsy Gleason pattern 5, to predict RP Gleason pattern ≤4 + 4. Here, a second nomogram (n = 2315) yielded 68.0% accuracy, maximal departures from ideal prediction of ±5.7%, and virtually the same DCA pattern as the main nomogram. CONCLUSIONS: Downgrading affects half of all high-risk PCa patients. Its presence may be predicted accurately and may help with better treatment planning. PATIENT SUMMARY: Downgrading occurs in every second high-risk prostate cancer patients. The nomograms developed by us can predict these probabilities accurately.

Citace poskytuje Crossref.org

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$a Wenzel, Mike $u Department of Urology, University Hospital Frankfurt, Frankfurt am Main, Germany; Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada. Electronic address: Mike.Wenzel@kgu.de
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$a BACKGROUND: Some high-risk prostate cancer (PCa) patients may show more favorable Gleason pattern at radical prostatectomy (RP) than at biopsy. OBJECTIVE: To test whether downgrading could be predicted accurately. DESIGN, SETTING, AND PARTICIPANTS: Within the Surveillance, Epidemiology and End Results database (2010-2016), 6690 National Comprehensive Cancer Network (NCCN) high-risk PCa patients were identified. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES: We randomly split the overall cohort between development and validation cohorts (both n = 3345, 50%). Multivariable logistic regression models used biopsy Gleason, prostate-specific antigen, number of positive prostate biopsy cores, and cT stage to predict downgrading. Accuracy, calibration, and decision curve analysis (DCA) tested the model in the external validation cohort. RESULTS AND LIMITATIONS: Of 6690 patients, 50.3% were downgraded at RP, and of 2315 patients with any biopsy pattern 5, 44.1% were downgraded to RP Gleason pattern ≤4 + 4. Downgrading rates were highest in biopsy Gleason pattern 5 + 5 (84.1%) and lowest in 3 + 4 (4.0%). In the validation cohort, the logistic regression model-derived nomogram predicted downgrading with 71.0% accuracy, with marginal departures (±3.3%) from ideal predictions in calibration. In DCA, a net benefit throughout all threshold probabilities was recorded, relative to treat-all or treat-none strategies and an algorithm based on an average downgrading rate of 50.3%. All steps were repeated in the subgroup with any biopsy Gleason pattern 5, to predict RP Gleason pattern ≤4 + 4. Here, a second nomogram (n = 2315) yielded 68.0% accuracy, maximal departures from ideal prediction of ±5.7%, and virtually the same DCA pattern as the main nomogram. CONCLUSIONS: Downgrading affects half of all high-risk PCa patients. Its presence may be predicted accurately and may help with better treatment planning. PATIENT SUMMARY: Downgrading occurs in every second high-risk prostate cancer patients. The nomograms developed by us can predict these probabilities accurately.
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$a Würnschimmel, Christoph $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada; Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany
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$a Chierigo, Francesco $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada; Department of Urology, Policlinico San Martino Hospital, University of Genova, Genova, Italy
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$a Flammia, Rocco Simone $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada; Department of Maternal-Child and Urological Sciences, Sapienza Rome University, Policlinico Umberto I Hospital, Rome, Italy
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$a Tian, Zhe $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
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$a Shariat, Shahrokh F $u Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria; Departments of Urology, Weill Cornell Medical College, New York, NY, USA; Department of Urology, University of Texas Southwestern, Dallas, TX, USA; Department of Urology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Division of Urology, Department of Special Surgery, Jordan University Hospital, The University of Jordan, Amman, Jordan
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$a Gallucci, Michele $u Department of Maternal-Child and Urological Sciences, Sapienza Rome University, Policlinico Umberto I Hospital, Rome, Italy
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$a Terrone, Carlo $u Department of Urology, Policlinico San Martino Hospital, University of Genova, Genova, Italy
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$a Saad, Fred $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
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$a Tilki, Derya $u Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany
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$a Graefen, Markus $u Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany
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$a Becker, Andreas $u Department of Urology, University Hospital Frankfurt, Frankfurt am Main, Germany
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$a Kluth, Luis A $u Department of Urology, University Hospital Frankfurt, Frankfurt am Main, Germany
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$a Mandel, Philipp $u Department of Urology, University Hospital Frankfurt, Frankfurt am Main, Germany
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$a Chun, Felix K H $u Department of Urology, University Hospital Frankfurt, Frankfurt am Main, Germany
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$a Karakiewicz, Pierre I $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
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