Robot yardımlı radikal prostatektomi sonrasında biyokimyasal rekürrensi predikte eden faktörler: tek merkez deneyimi

Year 2022, , 70 - 76, 30.09.2022

Ferhat Yakup Suçeken , Eyüp Veli Küçük

https://doi.org/10.54233/endouroloji.1164973

Abstract

Amaç: Bu çalışmada uzun takip süresine sahip hastalarda Biyokimyasal Rekürrens (BCR) gelişimini predikte eden faktörleri araştırmayı hedefledik.
Gereç ve Yöntemler: Robot Yardımlı Radikal Prostatektomi (RARP) uygulanan 758 hastanın verileri geriye dönük olarak tarandı. Postoperatif dönemde prostat spesifik antijen (PSA) değerlerinin 0,2 ng/mL ve üzeri saptanması BCR olarak kabul edildi. BCR gelişmeyen grup Grup 1, BCR gelişen grup Grup 2 olarak sınıflandırıldı.
Bulgular: Ortalama yaş iki grup arasında benzerdi. BCR gelişen grupta PSA değerleri anlamlı oranda yüksek izlendi (p<0,001). BCR gelişen grupta biyopsi gleason skoru (GS), risk sınıflaması ve spesmene ait GS oranları anlamlı olarak yüksek izlendi (sırasıyla p=0,02, p<0,001, <0,001). BCR gelişen grupta pozitif cerrahi sınır (PSM), ekstra prostatik yayılım (EPE), seminal vezikül invazyonu (SVI) ve lenf nodu pozitifliği (LNI) oranları anlamlı olarak yüksek izlendi. Çok değişkenli analizlerde; PSA, risk sınıflaması, spesmene ait GS, PSM, SVI ve T evreleri anlamlı parametreler olarak izlendi.
Sonuç: BCR gelişimini predikte eden değerler PSA, risk sınıflaması, spesmene ait GS, PSM, SVI ve T evresidir. Bu konuda ortak kabul gören modellerin yaygınlaşması ile hasta yönetimi ve hasta beklentilerinin optimizasyonunun sağlanabileceği kanaatindeyiz.

Keywords

robot yardımlı radikal prostatektomi, biyokimyasal rekürrens, prostat kanseri, PSA

Factors predicting biochemical recurrence following robot-assisted radical prostatectomy: single-center experience

Abstract

Objective: In this study, we aimed to investigate factors predicting the development of biochemical recurrence (BCR) in our clinical experience with patients over a long follow-up.
Material and Methods: The data of 758 patients who underwent robot-assisted radical prostatectomy (RARP) were retrospectively reviewed. In the postoperative period, the prostate-specific antigen (PSA) value is measured as 0.2 ng/mL and above, regarded as biochemical recurrence (BCR). The non-BCR group was regarded as Group 1, and the BCR group as Group 2.
Results: The mean age was similar between the two groups. The PSA values ​​were significantly higher in the group that developed BCR (p<0.001). The biopsy Gleason score (GS), risk classification, and specimen GS were significantly higher in this group (p=0.02, p<0.001, and p<0.001, respectively). The BCR group also had statistically significantly higher positive surgical margin (PSM), extraprostatic extension (EPE), seminal vesicle invasion (SVI), and lymph node invasion rates. According to the multivariate analyses, PSA, risk classification, specimen GS, PSM, SVI, and T stage were significant parameters in the prediction of BCR.
Conclusion: The parameters ​​that predict the development were determined as the PSA value, risk classification, specimen GS, PSM, SVI, and T stage. The widespread adoption of commonly accepted methods will help achieve better patient management and optimize patient expectations.

Keywords

robot assisted radical prostatectomy, biochemical recurrence, prostate cancer, PSA

References

• 1. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7–34. https://doi.org/10.3322/caac.21551
• 2. Freedland SJ, Humphreys EB, Mangold LA, et al (2005) Risk of prostate cancer-specific mortality following biochemical recurrence after radical prostatectomy. J Am Med Assoc 294:433–439. https://doi.org/10.1001/jama.294.4.433
• 3. Walz J, Chun FKH, Klein EA, et al (2009) Nomogram Predicting the Probability of Early Recurrence After Radical Prostatectomy for Prostate Cancer. J Urol 181:601–608. https://doi.org/10.1016/j.juro.2008.10.033
• 4. Diaz M, Peabody JO, Kapoor V, et al (2015) Oncologic outcomes at 10 years following robotic radical prostatectomy. Eur Urol 67:1168–1176. https://doi.org/10.1016/j.eururo.2014.06.025
• 5. Wong NC, Lam C, Patterson L, Shayegan B (2019) Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy. BJU Int 123:51–57. https://doi.org/10.1111/bju.14477
• 6. Messing EM, Manola J, Yao J, et al (2006) Immediate versus deferred androgen deprivation treatment in patients with node-positive prostate cancer after radical prostatectomy and pelvic lymphadenectomy. Lancet Oncol 7:472–479. https://doi.org/10.1016/S1470-2045(06)70700-8
• 7. Thompson IM, Tangen CM, Paradelo J, et al (2009) Adjuvant Radiotherapy for Pathological T3N0M0 Prostate Cancer Significantly Reduces Risk of Metastases and Improves Survival: Long-Term Followup of a Randomized Clinical Trial. J Urol 181:956–962. https://doi.org/10.1016/j.juro.2008.11.032
• 8. Cooperberg MR, Hilton JF, Carroll PR (2011) The CAPRA-S score: A straightforward tool for improved prediction of outcomes after radical prostatectomy. Cancer 117:5039–5046. https://doi.org/10.1002/cncr.26169
• 9. Poulakis V, Witzsch U, De Vries R, et al (2004) Preoperative neural network using combined magnetic resonance imaging variables, prostate-specific antigen, and gleason score to predict positive surgical margins. Urology 64:516–521. https://doi.org/10.1016/j.urology.2004.04.027
• 10. Hattori S, Kosaka T, Mizuno R, et al (2014) Prognostic value of preoperative multiparametric magnetic resonance imaging (MRI) for predicting biochemical recurrence after radical prostatectomy. BJU Int 113:741–747. https://doi.org/10.1111/bju.12329
• 11. Ekşi M, Evren İ, Akkaş F, et al (2021) Machine learning algorithms can more efficiently predict biochemical recurrence after robot-assisted radical prostatectomy. Prostate 81:913–920. https://doi.org/10.1002/pros.24188
• 12. Wolfram M, Brautigam R, Engl T, et al (2003) Robotic-assisted laparoscopic radical prostatectomy: the Frankfurt technique. World J Urol 21:128–132. https://doi.org/10.1007/s00345-003-0346-z
• 13. Edge SB, Compton CC (2010) The american joint committee on cancer: The 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 17:1471–1474. https://doi.org/10.1245/s10434-010-0985-4
• 14. (2011) National Comprehensive Cancer Network. In: Encycl. Cancer. http://www.nccn.org/professionals/physician_ gls/pdf/prostate.pdf. Accessed 19 Apr 2020
• 15. Pound CR, Partin AW, Eisenberger MA, et al (1999) Natural history of progression after PSA elevation following radical prostatectomy. J Am Med Assoc. https://doi.org/10.1001/jama.281.17.1591
• 16. Punnen S, Freedland SJ, Presti JC, et al (2014) Multi-institutional validation of the CAPRA-S score to predict disease recurrence and mortality after radical prostatectomy. Eur Urol 65:1171–1177. https://doi.org/10.1016/j.eururo.2013.03.058
• 17. Siddiqui MM, Truong H, Rais-Bahrami S, et al (2015) Clinical implications of a multiparametric magnetic resonance imaging based nomogram applied to prostate cancer active surveillance. J Urol 193:1943–1949. https://doi.org/10.1016/j.juro.2015.01.088
• 18. Obermeyer Z, Emanuel EJ (2016) Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. N Engl J Med 375:1216–1219. https://doi.org/10.1056/nejmp1606181
• 19. Kattan MW, Eastham JA, Stapleton AMF, et al (1998) A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst 90:766–771. https://doi.org/10.1093/jnci/90.10.766
• 20. Han M, Partin AW, Zahurak M, et al (2003) Biochemical (prostate specific antigen) recurrence probability following radical prostatectomy for clinically localized prostate cancer. J Urol 169:517–523. https://doi.org/10.1016/S0022-5347(05)63946-8
• 21. Donovan MJ, Fernandez G, Scott R, et al (2018) Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test. Prostate Cancer Prostatic Dis 21:594–603. https://doi.org/10.1038/s41391-018-0067-4
• 22. Yilmaz S, Yılmaz M, Yalçın S, et al (2021) Basic factors predicting prostate cancer in Prostate Imaging Reporting and Data System-3 lesions. 16:184–189
• 23. Sokmen BK, Sokmen D, Comez Yİ, Eksi M (2022) Prediction of Prostate Cancer Aggressiveness Using a Novel Multiparametric Magnetic Resonance Imaging Parameter: Tumor Heterogeneity Index. Urol Int 1–8. https://doi.org/10.1159/000521606
• 24. Ward JF, Blute ML, Slezak J, et al (2003) The long-term clinical impact of biochemical recurrence of prostate cancer 5 or more years after radical prostatectomy. J Urol 170:1872–1876. https://doi.org/10.1097/01.ju.0000091876.13656.2e
• 25. Molitierno J, Evans A, Mohler JL, et al (2006) Characterization of biochemical recurrence after radical prostatectomy. Urol Int 77:130–134. https://doi.org/10.1159/000093906
• 26. Tağcı S, Özden C, Kızılkan Y, et al (2021) The relationship between the CAPRA-S and the time of biochemical recurrence following radical prostatectomy. 16:254–261