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Authors: Alessandro Savino 1 ; Alfredo Benso 1 ; Stefano Di Carlo 2 ; Valentina Giannini 3 ; Anna Vignati 3 ; Gianfranco Politano 3 ; Simone Mazzetti 2 and Daniele Regge 4

Affiliations: 1 Department of Control and Computer Engineering and Consorzio Interuniversitario Nazionale per l’Informatica, Italy ; 2 Department of Control and Computer Engineering, Italy ; 3 Institute for Cancer Research and Treatment, Italy ; 4 Institute for Cancer Research and Treatment and Candiolo (TO), Italy

Keyword(s): Prostate Cancer, Computer Aided Diagnosis, Malignancies Probabilistic Classification, Magnetic Resonance Imaging (MRI), Software Design.

Related Ontology Subjects/Areas/Topics: Bioimaging ; Biomedical Engineering ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Health Engineering and Technology Applications ; Image Archiving and Communication ; Magnetic Resonance Imaging ; Medical Imaging and Diagnosis ; NeuroSensing and Diagnosis ; Neurotechnology, Electronics and Informatics ; Quantitative Bioimaging

Abstract: Prostate Cancer (PCa) is the most common solid neoplasm in males and a major cause of cancer-related death. Screening based on Prostate Specific Antigen (PSA) reduces the rate of death by 31%, but it is associated with a high risk of over-diagnosis and over-treatment. Prostate Magnetic Resonance Imaging (MRI) has the potential to improve the specificity of PSA-based screening scenarios as a non-invasive detection tool. Research community effort focused on classification techniques based on MRI in order to produce a malignancy likelihood map. The paper describes the prototyping design, the implemented work-flow and the software architecture of a Computer Aided Diagnosis (CAD) software which aims at providing a comprehensive diagnostic tool, including an integrated classification stack, from a preliminary registration of images to the classification process. This software can improve the diagnostic accuracy of the radiologist, reduce reader variability and speed up the whole diagnostic work-up. (More)

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Paper citation in several formats:
Savino, A.; Benso, A.; Di Carlo, S.; Giannini, V.; Vignati, A.; Politano, G.; Mazzetti, S. and Regge, D. (2014). A Prostate Cancer Computer Aided Diagnosis Software including Malignancy Tumor Probabilistic Classification. In Proceedings of the International Conference on Bioimaging (BIOSTEC 2014) - BIOIMAGING; ISBN 978-989-758-014-7; ISSN 2184-4305, SciTePress, pages 49-54. DOI: 10.5220/0004799100490054

@conference{bioimaging14,
author={Alessandro Savino. and Alfredo Benso. and Stefano {Di Carlo}. and Valentina Giannini. and Anna Vignati. and Gianfranco Politano. and Simone Mazzetti. and Daniele Regge.},
title={A Prostate Cancer Computer Aided Diagnosis Software including Malignancy Tumor Probabilistic Classification},
booktitle={Proceedings of the International Conference on Bioimaging (BIOSTEC 2014) - BIOIMAGING},
year={2014},
pages={49-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004799100490054},
isbn={978-989-758-014-7},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Bioimaging (BIOSTEC 2014) - BIOIMAGING
TI - A Prostate Cancer Computer Aided Diagnosis Software including Malignancy Tumor Probabilistic Classification
SN - 978-989-758-014-7
IS - 2184-4305
AU - Savino, A.
AU - Benso, A.
AU - Di Carlo, S.
AU - Giannini, V.
AU - Vignati, A.
AU - Politano, G.
AU - Mazzetti, S.
AU - Regge, D.
PY - 2014
SP - 49
EP - 54
DO - 10.5220/0004799100490054
PB - SciTePress

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