Prostate cancer: computer-aided diagnosis on multiparametric MRI

Laura Marin; Daniel Racoceanu; Raphaele Renard Penna; Malek Ezziane

doi:10.1117/12.2283404

17 November 2017 Prostate cancer: computer-aided diagnosis on multiparametric MRI

Laura Marin, Daniel Racoceanu, Raphaele Renard Penna, Malek Ezziane

Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 1057213 (2017) https://doi.org/10.1117/12.2283404
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia

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.

Citation Download Citation

Laura Marin, Daniel Racoceanu, Raphaele Renard Penna, and Malek Ezziane "Prostate cancer: computer-aided diagnosis on multiparametric MRI", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057213 (17 November 2017); https://doi.org/10.1117/12.2283404

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