A prostate CAD system based on multiparametric analysis of DCE T1-w, and DW automatically registered images

Valentina Giannini; Anna Vignati; Simone Mazzetti; Massimo De Luca; Christian Bracco; Michele Stasi; Filippo Russo; Enrico Armando; Daniele Regge

doi:10.1117/12.2006336

28 February 2013 A prostate CAD system based on multiparametric analysis of DCE T1-w, and DW automatically registered images

Valentina Giannini, Anna Vignati, Simone Mazzetti, Massimo De Luca, Christian Bracco, Michele Stasi, Filippo Russo, Enrico Armando, Daniele Regge

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86703E (2013) https://doi.org/10.1117/12.2006336
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

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Valentina Giannini, Anna Vignati, Simone Mazzetti, Massimo De Luca, Christian Bracco, Michele Stasi, Filippo Russo, Enrico Armando, and Daniele Regge "A prostate CAD system based on multiparametric analysis of DCE T1-w, and DW automatically registered images", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86703E (28 February 2013); https://doi.org/10.1117/12.2006336

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