Affiliation:
1. Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI IRCCS San Raffaele Scientific Institute Vita‐Salute San Raffaele University Milan Italy
2. Department of Radiology IRCCS San Raffaele Scientific Institute Vita‐Salute San Raffaele University Milan Italy
Abstract
AbstractBackgroundTo assess the variation of multiparametric magnetic resonance imaging (mpMRI) positive predictive value (PPV) according to each patient's risk of clinically significant prostate cancer (csPCa) based exclusively on clinical factors.MethodsWe evaluated 999 patients with positive mpMRI (PI‐RADS ≥ 3) receiving targeted (TBx) plus systematic prostate biopsy. We built a multivariable logistic regression analysis (MVA) using clinical risk factors to calculate the individual patients' risk of harboring csPCa at TBx. A second MVA tested the association between individual patients' clinical risk and mpMRI PPV accounting for the PI‐RADS score. Finally, we plotted the PPV of each PI‐RADS score by the individual patient pretest probability of csPCa using a LOWESS approach.ResultsOverall, TBx found csPCa in 21%, 51%, and 80% of patients with PI‐RADS 3, 4, and 5 lesions, respectively. At MVA, age, PSA, digital rectal examination (DRE), and prostate volume were significantly associated with the risk of csPCa at biopsy. DRE yielded the highest odds ratio (OR: 2.88; p < 0.001). The individual patient's clinical risk was significantly associated with mpMRI PPV (OR: 2.49; p < 0.001) using MVA. Plotting the mpMRI PPV according to the predicted clinical risks, we observed that for patients with clinical risk close to 0 versus patients with risk higher than 90%, the mpMRI PPV of PI‐RADS 3, 4, and 5 ranged from 0% to 75%, from 0% to 96%, and from 45% to 100%, respectively.ConclusionmpMRI PPV varies according to the individual pretest patient's risk based on clinical factors. These findings should be considered in the decision‐making process for patients with suspect MRI findings referred for a prostate biopsy. Moreover, our data support the need for further studies to create an individualized risk prediction tool.