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9 March 2011 A CAD system based on multi-parametric analysis for cancer prostate detection on DCE-MRI
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Computer-aided diagnosis (CAD) systems using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data may be developed to help localize prostate cancer and guide biopsy, avoiding random sampling of the whole gland. The purpose of this study is to present a DCE-MRI CAD system, which calculates the likelihood of malignancy in a given area of the prostate by combining model-based and model-free parameters. The dataset includes 10 patients with prostate cancer, with a total of 13 foci of adenocarcinoma. The post-processing is based on the following steps: testing of registration quality, noise filtering, and extracting the proposed features needed to the CAD. Parameters with the best performance in discriminating between normal and cancer regions are selected by computing the area under the ROC curve, and by evaluating the correlation between pairs of features. A 6-dimensional parameters vector is generated for each pixel and fed into a Bayesian classifier, in which the output is the probability of malignancy. The classification performance is estimated using the leave-one-out method. The resulting area under the ROC curve is 0.899 (95%CI:0.893-0.905); sensitivity and specificity are 82.4% and 82.1% respectively at the best cut-off point (0.352). Preliminary results show that the system is accurate in detecting areas of the gland that are involved by tumor. Further studies will be necessary to confirm these promising preliminary results.
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Simone Mazzetti, Massimo De Luca, Christian Bracco, Anna Vignati, Valentina Giannini, Michele Stasi, Filippo Russo, Enrico Armando, Silvano Agliozzo, and Daniele Regge "A CAD system based on multi-parametric analysis for cancer prostate detection on DCE-MRI", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79633Q (9 March 2011);

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