Prostate cancer metastatic risk is correlated to its volume and grade. These parameters are now best estimated preoperatively with MRI and MRI-guided biopsy. The objective is to estimate the risk of metastatic recurrence after radical prostatectomy (RP) in our model versus conventional clinical EAU classification. The secondary objective is biochemical recurrence (BCR).
Retrospective study of cohort of 713 patients having underdone, MRI-guided biopsies and RP between 2009 and 2018. The preoperative variables included PSA, cT-stage, tumor volume (TV) based on the lesion’s largest diameter at MRI, the percentage of Gleason Pattern 4/5 (%GP4/5) at MRI-guided biopsy and volume of GP4/5 (VolGP4/5) calculated as TVx%GP4/5. Outcomes measurements and statistical analysis: The variables’ ability to predict recurrence was determined in univariable and multivariable Fine-and-Gray models, according to the Akaike criteria information (AIC) and Harrell’s C-index.
Overall, 176 (25%), 430 (60%) and 107 (15%) had low, intermediate and high-risk disease according to the EAU classification. During a median follow-up period of 57 months, metastatic recurrence was observed in 48 patients with 5-year probability of 5.6% [95%CI 3.9-7.7]. VolGP4/5 (categories: <0.5; 0.5-1.0; 1.01-3.2; >3.2 ml) was the parameter with the lowest AIC and the highest C-index for metastatic recurrence 0.82 [95%CI 0.76-0.88] and for BCR 0.73 [95%CI 0.68-0.78]. In a multivariable model that included %GP4/5 and TV, C-index was 0.86 [95%CI 0.79-0.91] for metastatic recurrence and 0.77 [0.72-0.82] for BCR. Same results for EAU classification were 0.74 [0.67-0.80] and 0.67 [0.63-0.72] respectively.Similarly, DCA revealed that the different models improved clinical risk prediction of BCR and metastatic recurrence compared with the EAU risk tools over all threshold probability (Figure 1). Limitations were in short follow-up and absence of PSMA-PET/CT availability.
We developed a preoperative risk tool integrating the volume of GP4/5 based on MRI and MRI-guided biopsies to predict metastatic recurrence after RP. Our model showed higher accuracy over conventional clinical risk models. These findings might enable physicians to provide more personalized patient care