ArticleDiagnostic and interventional radiology (Ankara, Turkey)2022
Arterial input function for quantitative dynamic contrast-enhanced MRI to diagnose prostate cancer.
Article in Diagnostic and interventional radiology (Ankara, Turkey), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 9 citations in OpenAlex.
- Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging data.Journal of applied clinical medical physics · 2025Article
- Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review.Diagnostics (Basel, Switzerland) · 2023Review
- Can the Gleason score be predicted in patients with prostate cancer? A dynamic contrast-enhanced MRI,Diagnostic and interventional radiology (Ankara, Turkey) · 2023Article
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9 authors at 1 institution in 1 country.
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Abstract
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.
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