Radiomics for Gleason Score Detection through Deep Learning.
Sensors (Basel)
; 20(18)2020 Sep 21.
Article
en En
| MEDLINE
| ID: mdl-32967291
ABSTRACT
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias de la Próstata
/
Clasificación del Tumor
/
Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
/
Male
Idioma:
En
Revista:
Sensors (Basel)
Año:
2020
Tipo del documento:
Article
País de afiliación:
Italia