Objective: The aim of the study was to assess the diagnostic value of computed tomography perfusion (CTp) of prostate in distinguishing between normal tissue and malignant lesion by using quantitative threshold values of CTp parameters. Materials and Methods: Sixty-one consecutive men with indication for radical prostatectomy were prospectively enrolled. All patients were intravenously injected with 80-mL bolus of nonionic iodinated contrast medium during cine-mode acquisition protocol. Perfusion data sets were analyzed by a dedicated software system and values for each of the 4 CTp parameters (blood volume, blood flow, mean transit time, and permeability surface-area product measurements) were recorded. Receiver operating characteristic curves were calculated to find which CTp parameter and which cutoff value might reveal the best diagnostic accuracy. Histopathology was used as reference standard. Results: Statistical correlation between radiological and pathological results was performed on 48 patients using 3456 segmented squares. Blood volume and permeability surface revealed the best diagnostic accuracy for differentiating between malignant and benign squares, with cutoff values of 6.1 and 16.5, respectively, and a sensitivity of 84.8% and 81.8%, respectively. All parameters showed also a high negative predictive value: 97.1% for blood volume and 95.4% for permeability surface. Conclusions: Blood volume and permeability surface are the 2 CTp parameters with the highest diagnostic accuracy in differentiating between normal tissue and prostatic neoplasia. Due to the extremely high negative predictive value, they are particularly valuable in excluding the presence of cancer and thus resulting potentially useful in assessing cancer response to adjuvant therapy. Copyright (C) 2016 Wolters Kluwer Health, Inc. All rights reserved.
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Αλέξανδρος Γ. Σφακιανάκης Medicine by Alexandros G. Sfakianakis,Anapafseos 5 Agios Nikolaos 72100 Crete Greece,00302841026182,0030693260717...
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heory of COVID-19 pathogenesis Publication date: November 2020Source: Medical Hypotheses, Volume 144Author(s): Yuichiro J. Suzuki ScienceD...
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