Quantitative imaging parameters to predict the local staging of prostate cancer in intermediate- to high-risk patients
Riccardo Laudicella, Stephan Skawran, Daniela A. Ferraro, Urs Jakob Mühlematter, Alexander Maurer, Hannes Grünig, Hendrik J. Rüschoff, Niels J. Rupp, Olivio F. Donati, Daniel Eberli, Irene A. Burger
Abstract
Abstract Objectives PSMA PET/MRI showed the potential to increase the sensitivity for extraprostatic disease (EPD) assessment over mpMRI; however, the interreader variability for EPD is still high. Therefore, we aimed to assess whether quantitative PSMA and mpMRI imaging parameters could yield a more robust EPD prediction. Methods We retrospectively evaluated PCa patients who underwent staging mpMRI and [ 68 Ga]PSMA-PET, followed by radical prostatectomy at our institution between 01.02.2016 and 31.07.2019. Fifty-eight cases with PET/MRI and 15 cases with PET/CT were identified. EPD was determined on histopathology and correlated with quantitative PSMA and mpMRI parameters assessed by two readers: ADC (mm 2 /1000 s), longest capsular contact (LCC, mm), tumor volume (cm 3 ), PSMA-SUV max and volume-based parameters using a fixed threshold at SUV > 4 to delineate PSMA total (g/ml) and PSMA vol (cm 3 ). The t test was used to compare means, Pearson’s test for categorical correlation, and ROC curve to determine the best cutoff. Interclass correlation (ICC) was performed for interreader agreement (95% CI). Results Seventy-three patients were included (64.5 ± 6.0 years; PSA 14.4 ± 17.1 ng/ml), and 31 had EPD (42.5%). From mpMRI, only LCC reached significance ( p = 0.005), while both volume-based PET parameters PSMA total and PSMA vol were significantly associated with EPD ( p = 0.008 and p = 0.004, respectively). On ROC analysis, LCC, PSMA total , and PSMA vol reached an AUC of 0.712 ( p = 0.002), 0.709 ( p = 0.002), and 0.718 ( p = 0.002), respectively. ICC was moderate–good for LCC 0.727 (0.565–0.828) and excellent for PSMA total and PSMA vol with 0.944 (0.990–0.996) and 0.985 (0.976–0.991), respectively. Conclusions Quantitative PSMA parameters have a similar potential as mpMRI LCC to predict EPD of PCa, with a significantly higher interreader agreement.