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.
A Prostate Cancer Computer Aided Diagnosis Software including Malignancy Tumor Probabilistic Classification / Savino, Alessandro; Benso, Alfredo; DI CARLO, Stefano; Giannini, V.; Vignati, A.; Politano, GIANFRANCO MICHELE MARIA; Mazzetti, S.; Regge, D.. - STAMPA. - (2014), pp. 49-54. (Intervento presentato al convegno International Conference on Bioimaging (BIOIMAGING) tenutosi a Eseo, Angers, FR nel 3-6 March, 2014) [10.5220/0004799100490054].
A Prostate Cancer Computer Aided Diagnosis Software including Malignancy Tumor Probabilistic Classification
SAVINO, ALESSANDRO;BENSO, Alfredo;DI CARLO, STEFANO;Giannini V.;POLITANO, GIANFRANCO MICHELE MARIA;
2014
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.File | Dimensione | Formato | |
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2014-BIOIMAGING-CADPROSTATE-AuthorVersion.pdf
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https://hdl.handle.net/11583/2538694
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