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

Abstract

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.

Contemporary seminal vesicle invasion rates in NCCN high‐risk prostate cancer patients

“…Third, imaging data are unavailable in the SEER database. Two previous investigators (Gandaglia et al 30 and Martini et al 31 ) integrated MRI findings within a nomogram predicting SVI. However, neither investigator relied exclusively on high‐risk PCa patients nor have they provided information on absolute number or specific proportions of high‐risk PCa patients, within their study cohorts 30,31 .…”

“…Two previous investigators (Gandaglia et al 30 and Martini et al 31 ) integrated MRI findings within a nomogram predicting SVI. However, neither investigator relied exclusively on high‐risk PCa patients nor have they provided information on absolute number or specific proportions of high‐risk PCa patients, within their study cohorts 30,31 . Moreover, both studies relied on institutional cohorts with relatively small sample sizes ( n = 504 31 and n = 614 30 ).…”

Prognostic implication of staging of seminal vesicle invasion in patients with prostatic adenocarcinoma after prostatectomy

“…To diagnose SVI precisely with preoperative information, the nomogram might assist in the accuracy of pT3b. 21 Our level classification was quite simple and required no special instrument for measurement. To assess the level of SVI, the SVs of RP specimens are macroscopically examined and categorized into three groups.…”

Prognostic value of seminal vesicle invasion on preoperative multi-parametric magnetic resonance imaging in pathological stage T3b prostate cancer

“…Several studies have proposed mpMRI to identify patients at a higher risk of adverse pathology, in addition to conventional clinical parameters 22,23 . Gandaglia et al 22 reported that the combination of mpMRI with accurate biopsy information on MRI-targeted and systematic biopsies improves the accuracy of multivariable models based on clinical and mpMRI data alone.…”

“…Gandaglia et al 22 reported that the combination of mpMRI with accurate biopsy information on MRI-targeted and systematic biopsies improves the accuracy of multivariable models based on clinical and mpMRI data alone. In addition, by integrating clinical data with mpMRI information, Martini et al 23 developed a simple model with the low number of variables. They reported that the model performs better at predicting SVI than relying on mpMRI alone; model could be a tool to estimate side-specific SVI.…”