Win Shun Lai1, Jennifer B Gordetsky1,2, John V Thomas3, Jeffrey W Nix1, Soroush Rais-Bahrami1,3. 1. Department of Urology, University of Alabama at Birmingham, Birmingham, Alabama. 2. Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama. 3. Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama.
Abstract
BACKGROUND: The objective of this study was to create a nomogram model integrating clinical and multiparametric magnetic resonance imaging (MP-MRI)-based variables to predict prostate cancer upgrading in a population of active surveillance (AS) patients. METHODS: Prostate cancer patients on AS who underwent MP-MRI with magnetic resonance imaging (MRI)/ultrasound (US) fusion-guided biopsy were identified. Clinical and imaging variables, including the prostate-specific antigen density (PSAD), number of lesions, total lesion volume, total lesion density, Prostate Imaging Reporting and Data System magnetic resonance imaging suspicion score (MRI-SS), and duration between prereferral systematic and MRI/US fusion-guided biopsy sessions, were assessed. Logistic regression modeling was used to assess upgrading on MRI/US fusion-guided biopsy. A predictive model for upgrading was calculated with the significant factors identified. RESULTS: Seventy-six patients were analyzed with a mean age of 62.5 years and a median prostate-specific antigen (PSA) level of 5.1 ng/mL. The average duration between prereferral and MRI/US biopsies was 21 months. Twenty patients (26.32%) were upgraded. The PSAD, duration between prereferral and MRI/US biopsies, MRI-SS, and MRI total lesion density were significantly associated with upgrading. A logistic regression model using these factors to predict upgrading on confirmatory MRI/US fusion biopsy had an area under the curve (AUC) of 0.84, whereas the AUC was 0.69 with PSA alone. On the basis of this model, a nomogram was generated, and using a probability cutoff of 22% as an indication of upgrading, it produced sensitivity, specificity, positive predictive, and negative predictive values of 80%, 81.25%, 57.1%, and 92.86%, respectively. CONCLUSIONS: The integration of MRI findings with clinical parameters can add value to a model predicting upgrading from a Gleason score of 3 + 3 = 6 in men on AS. This can potentially be used as a noninvasive approach to confirm AS patients with low-risk disease for whom biopsy may be deferred. Cancer 2017;123:1941-1948.
BACKGROUND: The objective of this study was to create a nomogram model integrating clinical and multiparametric magnetic resonance imaging (MP-MRI)-based variables to predict prostate cancer upgrading in a population of active surveillance (AS) patients. METHODS:Prostate cancerpatients on AS who underwent MP-MRI with magnetic resonance imaging (MRI)/ultrasound (US) fusion-guided biopsy were identified. Clinical and imaging variables, including the prostate-specific antigen density (PSAD), number of lesions, total lesion volume, total lesion density, Prostate Imaging Reporting and Data System magnetic resonance imaging suspicion score (MRI-SS), and duration between prereferral systematic and MRI/US fusion-guided biopsy sessions, were assessed. Logistic regression modeling was used to assess upgrading on MRI/US fusion-guided biopsy. A predictive model for upgrading was calculated with the significant factors identified. RESULTS: Seventy-six patients were analyzed with a mean age of 62.5 years and a median prostate-specific antigen (PSA) level of 5.1 ng/mL. The average duration between prereferral and MRI/US biopsies was 21 months. Twenty patients (26.32%) were upgraded. The PSAD, duration between prereferral and MRI/US biopsies, MRI-SS, and MRI total lesion density were significantly associated with upgrading. A logistic regression model using these factors to predict upgrading on confirmatory MRI/US fusion biopsy had an area under the curve (AUC) of 0.84, whereas the AUC was 0.69 with PSA alone. On the basis of this model, a nomogram was generated, and using a probability cutoff of 22% as an indication of upgrading, it produced sensitivity, specificity, positive predictive, and negative predictive values of 80%, 81.25%, 57.1%, and 92.86%, respectively. CONCLUSIONS: The integration of MRI findings with clinical parameters can add value to a model predicting upgrading from a Gleason score of 3 + 3 = 6 in men on AS. This can potentially be used as a noninvasive approach to confirm AS patients with low-risk disease for whom biopsy may be deferred. Cancer 2017;123:1941-1948.
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