• Title of article

    Prediction of early response to radiotherapy of uterine carcinoma with dynamic contrast-enhanced MR imaging using pixel analysis of MR perfusion imaging

  • Author/Authors

    Takayama، نويسنده , , Yukihisa and Ohno، نويسنده , , Tatsuya and Kishimoto، نويسنده , , Riwa and Kato، نويسنده , , Shingo and Yoneyama، نويسنده , , Ryuichi and Kandatsu، نويسنده , , Susumu and Tsujii، نويسنده , , Hirohiko and Obata، نويسنده , , Takayuki، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2009
  • Pages
    7
  • From page
    370
  • To page
    376
  • Abstract
    Purpose ess the predictability of the response to radiotherapy of uterine carcinoma, this study retrospectively analyzed dynamic contrast-enhanced magnetic resonance images (DCE-MRI) taken before radiotherapy. als and Methods two patients with uterine carcinoma were studied, of whom 22 had adenocarcinoma and 20 had squamous cell carcinoma (SCC). In DCE-MRI analysis, two parameters, SIe and Rdown, were measured. SIe is a median value for the degree of signal intensity change in all selected pixels in the tumor at 1–2 min after contrast agent injection. Rdown is the ratio of the number of down-sloped pixels to that of all selected pixels 3–7 min after injection. The tumor volume reduction rate (TVRR) was measured by MRI-based volumetry in pre- and post-radiotherapy transverse T2-weighted images. s l, TVRR was significantly correlated to both SIe (r=0.37, P=.015) and Rdown (r=0.73, P<.0001). In the separate patient groups, SIe but not Rdown was significantly different between the adenocarcinoma and SCC patients (t=3.64, P<.001). TVRR was not correlated to SIe in any group. TVRR was significantly correlated to Rdown in adenocarcinoma patients (r=0.78, P<.001) but not in SCC patients. sion y reflect differences in histological characteristics. Rdown may be useful for predicting the response to radiotherapy of uterine carcinoma.
  • Keywords
    Uterine carcinoma , Dynamic contrast enhanced magnetic resonance imaging , radiotherapy , Treatment response prediction
  • Journal title
    Magnetic Resonance Imaging
  • Serial Year
    2009
  • Journal title
    Magnetic Resonance Imaging
  • Record number

    1832818