Francesco Chierigo1,2,3, Marco Borghesi4, Christoph Würnschimmel5,6, Rocco Simone Flammia5,7, Benedikt Horlemann5, Gabriele Sorce5,8, Benedikt Höh5,9, Zhe Tian5, Fred Saad5, Markus Graefen6, Michele Gallucci7, Alberto Briganti8, Francesco Montorsi8, Felix K H Chun9, Shahrokh F Shariat10,11,12,13,14,15, Guglielmo Mantica4, Nazareno Suardi4, Carlo Terrone4, Pierre I Karakiewicz5. 1. Department of Surgical and Diagnostic Integrated Sciences (DISC), University of Genova, Genova, Italy. francesco.chierigo@gmail.com. 2. Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada. francesco.chierigo@gmail.com. 3. Department of Urology, Policlinico San Martino Hospital, University of Genova, Largo Rosanna Benzi 10, 16132, Genova, Italy. francesco.chierigo@gmail.com. 4. Department of Surgical and Diagnostic Integrated Sciences (DISC), University of Genova, Genova, Italy. 5. Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada. 6. Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany. 7. Department of Maternal-Child and Urological Sciences, Policlinico Umberto I Hospital, Sapienza Rome University, Rome, Italy. 8. Division of Experimental Oncology/Unit of Urology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy. 9. Department of Urology, University Hospital Frankfurt, Frankfurt am Main, Germany. 10. Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria. 11. Departments of Urology, Weill Cornell Medical College, New York, NY, USA. 12. Department of Urology, University of Texas Southwestern, Dallas, TX, USA. 13. Department of Urology, Second Faculty of Medicine, Charles University, Prague, Czech Republic. 14. Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University, Moscow, Russia. 15. Division of Urology, Department of Special Surgery, Jordan University Hospital, The University of Jordan, Amman, Jordan.
Abstract
AIM: To compare cancer-specific mortality (CSM) rates between radical prostatectomy (RP) vs. external beam radiotherapy (RT) in patients with ductal carcinoma (DC) of the prostate. MATERIALS AND METHODS: Within the Surveillance, Epidemiology, and End Results (SEER) database (2004-2016), we identified 369 DC patients, of whom 303 (82%) vs. 66 (18%) were treated with RP vs. RT, respectively. Kaplan-Meier plots and uni- and stepwise multivariate Cox regression models addressed CSM in the unmatched population. After propensity score matching (PSM) and inverse probability of treatment weighting (IPTW), Kaplan-Meier curve and Cox regression models tested the effect of RP vs RT on CSM. RESULTS: Overall, RT patients were older, harbored higher PSA values, higher clinical T and higher Gleason grade groups. 5-year CSM rates were respectively 4.2 vs. 10% for RP vs. RT (HR 0.40, 95% CI 0.16-0.99, p = 0.048, favoring RP). At step-by-step multivariate Cox regression, after adding possible confounders, the central tendency of the HR for RP vs. RT approached 1. PSM resulted into 124 vs. 53 patients treated respectively with RP vs. RT. After PSM, as well as after IPTW, the protective effect of RP was no longer present (HR 1.16, 95% CI 0.23-5.73, p = 0.9 and 0.97, 95% CI 0.35-2.66, p = 0.9, respectively). CONCLUSIONS: Although CSM rate of ductal carcinoma RP patients is lower of that of RT patients, this apparent benefit disappears after statistical adjustment for population differences.
AIM: To compare cancer-specific mortality (CSM) rates between radical prostatectomy (RP) vs. external beam radiotherapy (RT) in patients with ductal carcinoma (DC) of the prostate. MATERIALS AND METHODS: Within the Surveillance, Epidemiology, and End Results (SEER) database (2004-2016), we identified 369 DC patients, of whom 303 (82%) vs. 66 (18%) were treated with RP vs. RT, respectively. Kaplan-Meier plots and uni- and stepwise multivariate Cox regression models addressed CSM in the unmatched population. After propensity score matching (PSM) and inverse probability of treatment weighting (IPTW), Kaplan-Meier curve and Cox regression models tested the effect of RP vs RT on CSM. RESULTS: Overall, RT patients were older, harbored higher PSA values, higher clinical T and higher Gleason grade groups. 5-year CSM rates were respectively 4.2 vs. 10% for RP vs. RT (HR 0.40, 95% CI 0.16-0.99, p = 0.048, favoring RP). At step-by-step multivariate Cox regression, after adding possible confounders, the central tendency of the HR for RP vs. RT approached 1. PSM resulted into 124 vs. 53 patients treated respectively with RP vs. RT. After PSM, as well as after IPTW, the protective effect of RP was no longer present (HR 1.16, 95% CI 0.23-5.73, p = 0.9 and 0.97, 95% CI 0.35-2.66, p = 0.9, respectively). CONCLUSIONS: Although CSM rate of ductal carcinoma RP patients is lower of that of RT patients, this apparent benefit disappears after statistical adjustment for population differences.
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