Arterial input function for quantitative dynamic contrast-enhanced MRI to diagnose prostate cancer.

PURPOSE This study aims to analyze the ability of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish between prostate cancer (PCa) and benign lesions in transition zone (TZ) and peripheral zone (PZ) using different methods for arterial input function (AIF) det...

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Publicado en:Diagnostic & Interventional Radiology Vol. 28; no. 2; pp. 108 - 115
Autores principales: Ziayee, Farid, Mueller-Lutz, Anja, Gross, Janina, Ullrich, Tim, Quentin, Michael, Arsov, Christian, Antoch, Gerald, Wittsack, Hans-Jörg, Schimmöller, Lars
Formato: Journal Article
Publicado: Galenos Yayinevi Tic. LTD. STI Mar/Apr2022
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Galenos Yayinevi Tic. LTD. STI
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        atl: Arterial input function for quantitative dynamic contrast-enhanced MRI to diagnose prostate cancer.
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          Ziayee, Farid
          Mueller-Lutz, Anja
          Gross, Janina
          Ullrich, Tim
          Quentin, Michael
          Arsov, Christian
          Antoch, Gerald
          Wittsack, Hans-Jörg
          Schimmöller, Lars
        affil: Department of Diagnostic and Interventional Radiology, University Dusseldorf, Faculty of Medicine, Dusseldorf, Germany.
      sug:
        subj:
          Prostatic Neoplasms
          Prostatic Neoplasms Pathology
          Contrast Media
          Magnetic Resonance Imaging Methods
          Male
          Arteries
          Retrospective Design
          Algorithms
          Scales
          Arthritis Impact Measurement Scales
          Male
      ab: PURPOSE This study aims to analyze the ability of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish between prostate cancer (PCa) and benign lesions in transition zone (TZ) and peripheral zone (PZ) using different methods for arterial input function (AIF) determination. Study endpoints are identification of a standard AIF method and optimal quantitative perfusion parameters for PCa detection. METHODS DCE image data of 50 consecutive patients with PCa who underwent multiparametric MRI were analyzed retrospectively with three different methods of AIF acquisition. First, a region of interest was manually defined in an artery (AIFm); second, an automated algorithm was used (AIFa); and third, a population-based AIF (AIFp) was applied. Values of quantitative parameters after Tofts (Ktrans, ve, and kep) in PCa, PZ, and TZ in the three different AIFs were analyzed. RESULTS Ktrans and kep were significantly higher in PCa than in benign tissue independent from the AIF method. Whereas in PZ, Ktrans and kep could differentiate PCa (P < .001), in TZ only kep using AIFpdemonstrated a significant difference (P = .039). The correlations of the perfusion parameters that resulted from AIFm and AIFa were higher than those that resulted from AIFp, and the absolute values of Ktrans, kep, and ve were significantly lower when using AIFp. The values of quantitative perfusion parameters for PCa were similar regardless of whether PCa was located in PZ or TZ. CONCLUSION Ktrans and kep were able to differentiate PCa from benign PZ independent of the AIF method. AIFaseems to be the most feasible method of AIF determination in clinical routine. For TZ, none of the quantitative perfusion parameters provided satisfying results.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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