PSA density versus risk stratification for lymphadenectomy-making decision in patients with prostate cancer undergoing radical prostatectomy

Fully automated prostate segmentation on MRI: comparison with manual segmentation methods and specimen volumes

OBJECTIVE

The objective of our study was to compare calculated prostate volumes derived from tridimensional MR measurements (ellipsoid formula), manual segmentation, and a fully automated segmentation system as validated by actual prostatectomy specimens.

MATERIALS AND METHODS

Ninety-eight consecutive patients (median age, 60.6 years; median prostate-specific antigen [PSA] value, 6.85 ng/mL) underwent triplane T2-weighted MRI on a 3-T magnet with an endorectal coil while undergoing diagnostic workup for prostate cancer. Prostate volume estimates were determined using the formula for ellipsoid volume based on tridimensional measurements, manual segmentation of triplane MRI, and automated segmentation based on normalized gradient fields cross-correlation and graph-search refinement. Estimates of prostate volume based on ellipsoid volume, manual segmentation, and automated segmentation were compared with prostatectomy specimen volumes. Prostate volume estimates were compared using the Pearson correlation coefficient and linear regression analysis. The Dice similarity coefficient was used to quantify spatial agreement between manual segmentation and automated segmentation.

RESULTS

The Pearson correlation coefficient revealed strong positive correlation between prostatectomy specimen volume and prostate volume estimates derived from manual segmentation (R = 0.89-0.91, p < 0.0001) and automated segmentation (R = 0.88-0.91, p < 0.0001). No difference was observed between manual segmentation and automated segmentation. Mean partial and full Dice similarity coefficients of 0.92 and 0.89, respectively, were achieved for axial automated segmentation.

CONCLUSION

Prostate volume estimates obtained with a fully automated 3D segmentation tool based on normalized gradient fields cross-correlation and graph-search refinement can yield highly accurate prostate volume estimates in a clinically relevant time of 10 seconds. This tool will assist in developing a broad range of applications including routine prostate volume estimations, image registration, biopsy guidance, and decision support systems.

Evaluating PSA Density as a Predictor of Biochemical Failure after Radical Prostatectomy: Results of a Prospective Study after a Median Follow-Up of 36 Months

Purpose. To evaluate the predictive ability of PSA density for biochemical relapse after radical prostatectomy in patients operated for clinically localized disease and to compare its predictive strength with preoperative PSA and Gleason score. Patients and Methods. The study evaluated 244 patients with localized disease who underwent an open retropubic radical prostatectomy between February 2007 and April 2011. PSA was measured every 3 months after surgery with a mean follow-up period of 36 months. Two consecutive rises >0.2 ng/mL were considered as biochemical relapse. Results. Biochemical recurrence was observed in 71 (29.1%). A great correlation was found between relapse and PSA (P = 0.005), PSA density (P = 0.002), Gleason score (P = 0.015), pathological stage (P = 0.001), positive surgical margins (P = 0.021), and invasion of seminal vesicles (P < 0.001) and lymph nodes (P < 0.001). We also found that PSA density was associated with adverse pathological findings. In univariate and multivariate analysis both PSA (P = 0.006) and PSA density (P = 0.009) were found to be significant predictors for relapse in contrast to tumor grade. Conclusion. PSA density is a valuable parameter in estimating the danger of biochemical failure and it may increase predictive potential through the incorporation in preoperative nomograms.