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Could perfusion heterogeneity at dynamic contrast-enhanced MRI be used to predict rectal cancer sensitivity to chemoradiotherapy?
To evaluate whether perfusion heterogeneity of rectal cancer prior to chemoradiotherapy (CRT) using histogram analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) quantitative parameters can predict response to treatment. Twenty-one patients with histologically proven rectal...
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Published in: | Clinical radiology 2018-10, Vol.73 (10), p.911.e1-911.e7 |
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Main Authors: | , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | To evaluate whether perfusion heterogeneity of rectal cancer prior to chemoradiotherapy (CRT) using histogram analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) quantitative parameters can predict response to treatment.
Twenty-one patients with histologically proven rectal adenocarcinoma were enrolled prospectively. All patients underwent 1.5 T DCE-MRI before CRT. Tumour volumes were drawn on Ktrans and Ve maps, using T2-weighted (W) images as reference, and the following first-order texture parameters of Ve and Ktrans values were extracted: 25th, 50th, 75th percentile, mean, standard deviation, skewness, and kurtosis. After CRT, patients underwent surgery and according with Rödel's tumour regression grade (TRG), they were classified as poor responders “non-GR” (TRG 0–2) and good responders “GR” (TRG 3–4). Differences between GR and non-GR in DCE-MRI first-order texture parameters were evaluated using the Mann–Whitney test, and their role in the prediction of response was investigated using receiver operating characteristic (ROC) curve analysis.
Sixteen (76%) patients were classified as GR and five (24%) were non-GR. Skewness and kurtosis of Ve was significantly higher in non-GR (4.886±1.320 and 36.402±24.486, respectively) than in GR patients (1.809±1.280, p=0.003 and 6.268±8.130, p= 0.011). Ve skewness |
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ISSN: | 0009-9260 1365-229X |
DOI: | 10.1016/j.crad.2018.06.007 |