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Proceedings Paper

A prostate CAD system based on multiparametric analysis of DCE T1-w, and DW automatically registered images
Author(s): Valentina Giannini; Anna Vignati; Simone Mazzetti; Massimo De Luca; Christian Bracco; Michele Stasi; Filippo Russo; Enrico Armando; Daniele Regge
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Paper Abstract

Prostate specific antigen (PSA)-based screening reduces the rate of death from prostate cancer (PCa) by 31%, but this benefit is associated with a high risk of overdiagnosis and overtreatment. As prostate transrectal ultrasound-guided biopsy, the standard procedure for prostate histological sampling, has a sensitivity of 77% with a considerable false-negative rate, more accurate methods need to be found to detect or rule out significant disease. Prostate magnetic resonance imaging has the potential to improve the specificity of PSA-based screening scenarios as a non-invasive detection tool, in particular exploiting the combination of anatomical and functional information in a multiparametric framework. The purpose of this study was to describe a computer aided diagnosis (CAD) method that automatically produces a malignancy likelihood map by combining information from dynamic contrast enhanced MR images and diffusion weighted images. The CAD system consists of multiple sequential stages, from a preliminary registration of images of different sequences, in order to correct for susceptibility deformation and/or movement artifacts, to a Bayesian classifier, which fused all the extracted features into a probability map. The promising results (AUROC=0.87) should be validated on a larger dataset, but they suggest that the discrimination on a voxel basis between benign and malignant tissues is feasible with good performances. This method can be of benefit to improve the diagnostic accuracy of the radiologist, reduce reader variability and speed up the reading time, automatically highlighting probably cancer suspicious regions.

Paper Details

Date Published: 28 February 2013
PDF: 6 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86703E (28 February 2013); doi: 10.1117/12.2006336
Show Author Affiliations
Valentina Giannini, Institute for Cancer Research and Treatment (Italy)
Anna Vignati, Institute for Cancer Research and Treatment (Italy)
Simone Mazzetti, Institute for Cancer Research and Treatment (Italy)
Massimo De Luca, Institute for Cancer Research and Treatment (Italy)
Christian Bracco, Institute for Cancer Research and Treatment (Italy)
Michele Stasi, Institute for Cancer Research and Treatment (Italy)
Filippo Russo, Institute for Cancer Research and Treatment (Italy)
Enrico Armando, Institute for Cancer Research and Treatment (Italy)
Daniele Regge, Institute for Cancer Research and Treatment (Italy)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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