Aim: 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. Materials and methods: 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. Results: 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 <3.635 was able to predict GR with an area under the ROC curve (AUC) of 0.988, sensitivity 93.8%, specificity 80%, and accuracy 90.5%. Ve kurtosis <21.095 was able to predict response with an AUC of 0.963, sensitivity 93.8%, specificity 80%, and accuracy 90.5%. Other parameters were not different between groups or predictors of response. Conclusion: Ve skewness and kurtosis seem to be promising in the prediction of response to CRT in rectal cancer patients.

Could perfusion heterogeneity at dynamic contrast-enhanced MRI be used to predict rectal cancer sensitivity to chemoradiotherapy?

Esposito, A.;Rancoita, P. M. V.;Rosati, R.;Del Maschio, A.;De Cobelli, F.;
2018-01-01

Abstract

Aim: 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. Materials and methods: 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. Results: 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 <3.635 was able to predict GR with an area under the ROC curve (AUC) of 0.988, sensitivity 93.8%, specificity 80%, and accuracy 90.5%. Ve kurtosis <21.095 was able to predict response with an AUC of 0.963, sensitivity 93.8%, specificity 80%, and accuracy 90.5%. Other parameters were not different between groups or predictors of response. Conclusion: Ve skewness and kurtosis seem to be promising in the prediction of response to CRT in rectal cancer patients.
2018
Radiology, Nuclear Medicine and Imaging
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/84534
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