Biochemical recurrence prediction after robot-assisted radical prostatectomy (BCR-PRARP)
2024

Predicting Biochemical Recurrence After Prostate Surgery

Sample size: 1700 publication Evidence: moderate

Author Information

Author(s): Tanan Bejrananda, Takahara Kiyoshi, Sowanthip Dutsadee, Motonaga Tomonari, Yagi Kota, Nakamura Wataru, Saruta Masanobu, Nukaya Takuhisa, Takenaka Masashi, Zennami Kenji, Ichino Manabu, Sasaki Hitomi, Sumitomo Makoto, Shiroki Ryoichi

Primary Institution: Division of Urology, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand

Hypothesis

This study aimed to establish a robust predictive model for biochemical recurrence (BCR) in patients with prostate cancer who underwent robot-assisted radical prostatectomy.

Conclusion

The developed nomogram accurately predicts the likelihood of BCR-free status within 3 years following RARP.

Supporting Evidence

  • 161 instances of BCR were observed during a median follow-up of 61.0 months.
  • The 5-year BCR-free survival rate for the cohort was 25%.
  • High PSA ≥20 ng/mL was identified as an independent predictor of BCR.
  • Pathologic T stage 3–4 was also a significant predictor of BCR.
  • The model exhibited a C-index of 0.743, indicating good predictive accuracy.

Takeaway

Doctors created a tool to help predict if prostate cancer will come back after surgery, which can help them take better care of patients.

Methodology

Cox proportional hazards regression was used to identify predictive variables for BCR, and a nomogram was constructed using R software.

Potential Biases

Data were obtained from a single center, which may limit generalizability.

Limitations

The long time span of the study may introduce variation due to changing surgical techniques and case volumes.

Participant Demographics

Median age was 67 years, with 66.9% of participants aged 65 or older.

Statistical Information

P-Value

0.034

Confidence Interval

95% CI: 0.741–0.745

Statistical Significance

p<0.001

Digital Object Identifier (DOI)

10.1016/j.heliyon.2024.e41031

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