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Proceedings Paper

Prostate cancer: computer-aided diagnosis on multiparametric MRI
Author(s): Laura Marin; Daniel Racoceanu; Raphaele Renard Penna; Malek Ezziane
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Paper Abstract

Prostate cancer (PCa) is one of the most common cancers in men, being also the second most deadly cancer after lung cancer. There is increasing interest in active surveillance and minimally invasive focal therapies in PCa to avoid morbidities associated with whole gland therapy. Tumor volume represents an essential prognostic factor of PCa and the definition of index lesion volume is critical for appropriate decision making, especially for image guide focal treatment or in case of active surveillance. Multi-parametric Magnetic Resonance Imaging (mp-MRI) is the modality of choice for the detection and the localization of PCa foci. However, little has been published on mp-MRI accuracy in determining PCa volume, especially at 3T. There is insufficient evidence and no consensus to determine which of the methods for measuring volume is optimal.

The objective of this study concerns the elaboration of an algorithm for automatic interpretation of mp-MRI. We determine the accuracy of the proposed method by comparing the prostate tumor volume issued from the automated volumetric mp-MRI measurements of the tumoral region, with manual and semi-automated volumetric measurements done by and respectively with radiologists. Information issued from whole mount histopathology is used to validate the whole approach.

Paper Details

Date Published: 17 November 2017
PDF: 6 pages
Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057213 (17 November 2017); doi: 10.1117/12.2283404
Show Author Affiliations
Laura Marin, Pontifical Catholic Univ. of Peru (Peru)
Daniel Racoceanu, Pontifical Catholic Univ. of Peru (Peru)
Raphaele Renard Penna, Hospital La Pitie-Salpetriere (France)
Malek Ezziane, Hospital La Pitie-Salpetriere (France)


Published in SPIE Proceedings Vol. 10572:
13th International Conference on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Juan David García, Editor(s)

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