Seminal vesicle-sparing perineal radical prostatectomy improves early functional results in patients with low-risk prostate cancer

Novel nomogram for the prediction of seminal vesicle invasion including multiparametric magnetic resonance imaging

OBJECTIVES

To create a model that predicts side-specific seminal vesicle invasion using clinical, biopsy and multiparametric magnetic resonance imaging data.

METHODS

We analyzed data from 544 patients who underwent robot-assisted radical prostatectomy at a single institution. To develop a side-specific predictive model, we ultimately considered four variables: prostate-specific antigen, highest ipsilateral biopsy Gleason grade, highest ipsilateral percentage core involvement and seminal vesicle invasion on multiparametric magnetic resonance imaging. A binary multivariable logistic regression model was fitted to predict seminal vesicle invasion. A nomogram was then built based on the coefficients of the resulting logit function. The leave-one-out cross validation method was used for internal validation, and the decision curve analysis for the evaluation of the net clinical benefit.

RESULTS

We relied on 804 side-specific cases after excluding negative biopsy observations (n = 284). Seminal vesicle invasion was reported on multiparametric magnetic resonance imaging in 41 (5%) cases, and on final pathology in 64 (8%) cases. All variables in the model emerged as predictors of seminal vesicle invasion (all P ≤ 0.001) and were subsequently considered to build a nomogram. The area under the curve of multiparametric magnetic resonance imaging alone in predicting seminal vesicle invasion was 59.1%; whereas one of the clinical variables only was 85.1%. The area under the curve of the nomogram resulting from their combination was 86.5%. After internal validation, this resulted in 84.7%. The model achieved good calibration and the decision curve analysis showed its clinical benefit, especially when compared with relying only on multiparametric magnetic resonance imaging prediction of seminal vesicle invasion.

CONCLUSIONS

A nomogram based on clinical and multiparametric magnetic resonance imaging data can predict seminal vesicle invasion and serve as a tool to urologists for surgical planning.

Comparison of three versions of Partin tables to predict final pathologic stage in a Chinese cohort: a decision curve analysis

PURPOSE

To compare Partin tables (PTs) 1997, 2001, and 2007 for their clinical applicability in a Chinese cohort based upon a decision curve analysis (DCA).

METHODS

Clinical and pathologic data of 264 consecutive Chinese patients with clinically localized prostate cancer were used. These patients underwent open radical prostatectomy between 2005 and 2011. DCA quantified the net benefit of different PT versions relating to specific threshold probabilities of established capsular penetration (ECP), seminal vesicle involvement (SVI), and lymph node involvement (LNI).

RESULTS

Overall, ECP, SVI, and LNI were recorded in 23.1, 10.2, and 6.1%, respectively. When the threshold probability was below the prevalence for LNI and ECP predictions, the DCA favored the 2007 version versus the 1997 version for SVI.

CONCLUSIONS

DCA indicates that for low threshold probability, decision models are useful to discriminate the performance differences of three PT versions, although net benefit differences were not apparent. For high threshold probability, there may not be an important benefit from the use of PTs and the current analysis cannot translate into meaningful net gains differences.

Comparison of three different tools for prediction of seminal vesicle invasion at radical prostatectomy

BACKGROUND

Statistical prediction tools are increasingly common, but there is considerable disagreement about how they should be evaluated. Three tools--Partin tables, the European Society for Urological Oncology (ESUO) criteria, and the Gallina nomogram--have been proposed for the prediction of seminal vesicle invasion (SVI) in patients with clinically localized prostate cancer who are candidates for a radical prostatectomy.

OBJECTIVES

Using different statistical methods, we aimed to determine which of these tools should be used to predict SVI.

DESIGN, SETTINGS, AND PARTICIPANTS

The independent validation cohort consisted of 2584 patients treated surgically for clinically localized prostate cancer at four North American tertiary care centers between 2002 and 2007.

INTERVENTIONS

Robot-assisted laparoscopic radical prostatectomy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Primary outcome was the presence of SVI. Traditional (area under the receiver operating characteristic [ROC] curve, calibration plots, the Brier score, sensitivity and specificity, positive and negative predictive value) and novel (decision curve analysis and predictiveness curves) statistical methods quantified the predictive abilities of the three models.

RESULTS AND LIMITATIONS

Traditional statistical methods (ie, ROC plots and Brier scores) could not clearly determine which one of the three SVI prediction tools should be preferred. For example, ROC plots and Brier scores seemed biased against the binary decision tool (ESUO criteria) and gave discordant results for the continuous predictions of the Partin tables and the Gallina nomogram. The results of the calibration plots were discordant with those of the ROC plots. Conversely, the decision curve indicated that the Partin tables represent the best strategy for stratifying the risk of SVI, resulting in the highest net benefit within the whole range of threshold probabilities.

CONCLUSIONS

When predicting SVI, surgeons should prefer the Partin tables over the ESUO criteria and the Gallina nomogram because this tool provided the highest net benefit. In contrast to traditional statistical methods, decision curve analysis gave an unambiguous result applicable to both continuous and binary models, providing an insight into clinical utility.

Is it necessary to remove the seminal vesicles completely at radical prostatectomy? decision curve analysis of European Society of Urologic Oncology criteria

PURPOSE

A publication on behalf of the European Society of Urological Oncology questioned the need for removing the seminal vesicles during radical prostatectomy in patients with prostate specific antigen less than 10 ng/ml except when biopsy Gleason score is greater than 6 or there are greater than 50% positive biopsy cores. We applied the European Society of Urological Oncology algorithm to an independent data set to determine its predictive value.

MATERIALS AND METHODS

Data on 1,406 men who underwent radical prostatectomy and seminal vesicle removal between 1998 and 2004 were analyzed. Patients with and without seminal vesicle invasion were classified as positive or negative according to the European Society of Urological Oncology algorithm.

RESULTS

Of 90 cases with seminal vesicle invasion 81 (6.4%) were positive for 90% sensitivity, while 656 of 1,316 without seminal vesicle invasion were negative for 50% specificity. The negative predictive value was 98.6%. In decision analytic terms if the loss in health when seminal vesicles are invaded and not completely removed is considered at least 75 times greater than when removing them unnecessarily, the algorithm proposed by the European Society of Urological Oncology should not be used.

CONCLUSIONS

Whether to use the European Society of Urological Oncology algorithm depends not only on its accuracy, but also on the relative clinical consequences of false-positive and false-negative results. Our threshold of 75 is an intermediate value that is difficult to interpret, given uncertainties about the benefit of seminal vesicle sparing and harm associated with untreated seminal vesicle invasion. We recommend more formal decision analysis to determine the clinical value of the European Society of Urological Oncology algorithm.