Combined Clinical Parameters and Multiparametric Magnetic Resonance Imaging for the Prediction of Extraprostatic Disease-A Risk Model for Patient-tailored Risk Stratification When Planning Radical Prostatectomy

Abstract

Background: Multiparametric magnetic resonance imaging (mpMRI) facilitates the detection of significant prostate cancer. Therefore, addition of mpMRI to clinical parameters might improve the prediction of extraprostatic extension (EPE) in radical prostatectomy (RP) specimens.

Objective: To investigate the accuracy of a novel risk model (RM) combining clinical and mpMRI parameters to predict EPE in RP specimens.

Design, setting, and participants: We added prebiopsy mpMRI to clinical parameters and developed an RM to predict individual side-specific EPE (EPE-RM). Clinical parameters of 264 consecutive men with mpMRI prior to MRI/transrectal ultrasound fusion biopsy and subsequent RP between 2012 and 2015 were retrospectively analysed.

Outcome measurements and statistical analysis: Multivariate regression analyses were used to determine significant EPE predictors for RM development. The prediction performance of the novel EPE-RM was compared with clinical T stage (cT), MR-European Society of Urogenital Radiology (ESUR) classification for EPE, two established nomograms (by Steuber et al and Ohori et al) and a clinical nomogram based on the coefficients of the established nomograms, and was constructed based on the data of the present cohort, using receiver operating characteristics (ROCs). For comparison, models' likelihood ratio (LR) tests and Vuong tests were used. Discrimination and calibration of the EPE-RM were validated based on resampling methods using bootstrapping.

Results and limitations: International society of Urogenital Pathology grade on biopsy, ESUR criteria, prostate-specific antigen, cT, prostate volume, and capsule contact length were included in the EPE-RM. Calibration of the EPE-RM was good (error 0.018). The ROC area under the curve for the EPE-RM was larger (0.87) compared with cT (0.66), Memorial Sloan Kettering Cancer Center nomogram (0.73), Steuber nomogram (0.70), novel clinical nomogram (0.79), and ESUR classification (0.81). Based on LR and Vuong tests, the EPE-RM's model fit was significantly better than that of cT, all clinical models, and ESUR classification alone (p<0.001). Limitations include monocentric design and expert reading of MRI.

Conclusions: This novel EPE-RM, incorporating clinical and MRI parameters, performed better than contemporary clinical RMs and MRI predictors, therefore providing an accurate patient-tailored preoperative risk stratification of side-specific EPE.

Patient summary: Extraprostatic extension of prostate cancer can be predicted accurately using a combination of magnetic resonance imaging and clinical parameters. This novel risk model outperforms magnetic resonance imaging and clinical predictors alone and can be useful when planning nerve-sparing radical prostatectomy.

Keywords: Extraprostatic extension; Magnetic resonance imaging; Nomogram; Prostate cancer; Radical prostatectomy; Risk model.