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• W2969314199 abstract "Radiomics can quantify tumor phenotypic characteristics non-invasively by defining a signature correlated with biological information. Thanks to algorithms derived from computer vision to extract features from images, and machine learning methods to mine data, Radiomics is the perfect case study of application of Artificial Intelligence in the context of precision medicine. In this study we investigated the association between radiomic features extracted from multi-parametric magnetic resonance imaging (mp-MRI)of prostate cancer (PCa) and the tumor histologic subtypes (using Gleason Score) using machine learning algorithms, in order to identify which of the mp-MRI derived radiomic features can distinguish high and low risk PCa." @default.
• W2969314199 created "2019-08-29" @default.
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• W2969314199 date "2019-01-01" @default.
• W2969314199 modified "2023-10-11" @default.
• W2969314199 title "May Radiomic Data Predict Prostate Cancer Aggressiveness?" @default.
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• W2969314199 doi "https://doi.org/10.1007/978-3-030-29930-9_7" @default.
• W2969314199 hasPublicationYear "2019" @default.
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