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Assessment of DCE Utility for PCa Diagnosis Using PI-RADS v2.1: Effects on Diagnostic Accuracy and Reproducibility

Valentina Brancato, Giuseppe Costanzo, Luca Basso, Liberatore Tramontano, Marta Puglia, Alfonso Ragozzino, Carlo Cavaliere

2020Diagnostics38 citationsDOIOpen Access PDF

Abstract

The role of dynamic contrast-enhanced-MRI (DCE-MRI) for Prostate Imaging-Reporting and Data System (PI-RADS) scoring is a controversial topic. In this retrospective study, we aimed to measure the added value of DCE-MRI in combination with T2-weighted (T2W) and diffusion-weighted imaging (DWI) using PI-RADS v2.1, in terms of reproducibility and diagnostic accuracy, for detection of prostate cancer (PCa) and clinically significant PCa (CS-PCa, for Gleason Score ≥ 7). 117 lesions in 111 patients were identified as suspicion by multiparametric MRI (mpMRI) and addressed for biopsy. Three experienced readers independently assessed PI-RADS score, first using biparametric MRI (bpMRI, including DWI and T2W), and then multiparametric MRI (also including DCE). The inter-rater and inter-method agreement (bpMRI- vs. mpMRI-based scores) were assessed by Cohen's kappa (κ). Receiver operating characteristics (ROC) analysis was performed to evaluate the diagnostic accuracy for PCa and CS-PCa detection among the two scores. Inter-rater agreement was excellent for the three pairs of readers (κ ≥ 0.83), while the inter-method agreement was good (κ ≥ 0.73). Areas under the ROC curve (AUC) showed similar high-values (0.8 ≤ AUC ≤ 0.85). The reproducibility of PI-RADS v2.1 scoring was comparable and high among readers, without relevant differences, depending on the MRI protocol used. The inclusion of DCE did not influence the diagnostic accuracy.

Topics & Concepts

ReproducibilityReceiver operating characteristicMedicineKappaMultiparametric MRIDiagnostic accuracyProstate cancerRadiologyBiopsyNuclear medicineCohen's kappaCancerMathematicsStatisticsInternal medicineGeometryMRI in cancer diagnosisProstate Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical Imaging
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