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Article

Radiomics for Gleason Score Detection through Deep Learning

1
Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
2
Institute for Informatics and Telematics, National Research Council of Italy, 56121 Pisa, Italy
3
Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80100 Napoli, Italy
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(18), 5411; https://doi.org/10.3390/s20185411
Received: 1 September 2020 / Accepted: 18 September 2020 / Published: 21 September 2020
(This article belongs to the Special Issue Deep Learning for Cancer Detection)
Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer is a task usually performed by radiologists. In this paper we propose a deep architecture, based on several convolutional layers, aimed to automatically assign the Gleason score to Magnetic Resonance Imaging (MRI) under analysis. We exploit a set of 71 radiomic features belonging to five categories: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix and Gray Level Size Zone Matrix. The radiomic features are gathered directly from segmented MRIs using two free-available dataset for research purpose obtained from different institutions. The results, obtained in terms of accuracy, are promising: they are ranging between 0.96 and 0.98 for Gleason score prediction. View Full-Text
Keywords: prostate; cancer; radiomic; deep learning prostate; cancer; radiomic; deep learning
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MDPI and ACS Style

Brunese, L.; Mercaldo, F.; Reginelli, A.; Santone, A. Radiomics for Gleason Score Detection through Deep Learning. Sensors 2020, 20, 5411. https://doi.org/10.3390/s20185411

AMA Style

Brunese L, Mercaldo F, Reginelli A, Santone A. Radiomics for Gleason Score Detection through Deep Learning. Sensors. 2020; 20(18):5411. https://doi.org/10.3390/s20185411

Chicago/Turabian Style

Brunese, Luca, Francesco Mercaldo, Alfonso Reginelli, and Antonella Santone. 2020. "Radiomics for Gleason Score Detection through Deep Learning" Sensors 20, no. 18: 5411. https://doi.org/10.3390/s20185411

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