Purpose: The aim of this study was to assess the value of the pelvic cavity index (PCI), as an objective pelvimetry feature, to predict operative time, margin status, and early urine continence after extraperitoneal single-port robotic radical prostatectomy (RP). We sought to define an optimal cutoff point for PCI in predicting postoperative outcomes. Methods: A total of 94 patients who underwent extraperitoneal single-port robotic RP and preoperative cross-sectional imaging were enrolled. PCI was calculated as follows: Pelvicinletdiameter×PelvicoutletdiameterPelvicdepth. The predictive value of PCI for operative time, surgical margin status, and 3-month urinary continence recovery was assessed using regression models. To report the optimum cutoff value, on receiver operating characteristic (ROC) analysis, we calculated the performance of PCI cutoff points ranging from 5.56 to 10.80 cm by every 0.01 increment. Results: No significant associations were noted between clinical characteristics (including PCI) and operative time. Similarly, other than pathological stage, no clinical variables (including PCI) were predictive of the positive surgical margin. However, a higher PCI was associated with a significantly higher rate of continence 3 months after surgery [odds ratio 2.44 (1.75-5.33); p = 0.01]. On ROC analysis, a PCI cutoff value = 8.21 cm yielded the best accuracy (area under the curve = 0.733, 95% confidence interval 0.615-0.851; p = 0.001). No association was noted between variables and 6-month continence rates. Conclusions: With a single-port robotic system, the operative time, positive surgical margin rate, and long-term continence after prostatectomy would be independent of the bony pelvic cavity. However, a higher PCI is associated with a higher rate of early continence after surgery. PCI at a cutoff of 8.21 cm has the optimum performance to predict postoperative urine continence recovery. If validated, this information may be helpful regarding patient counseling before single-port robotic RP.
All Science Journal Classification (ASJC) codes