Radiomics for Gleason Score Detection through Deep Learning

Sensors

Luca Brunese¹  Antonella Santone¹  Francesco Mercaldo¹  Alfonso Reginelli² 

[1] Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, Campobasso 86100, Italy;Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, Napoli 80100, Italy;

关键词: prostate;    cancer;    radiomic;    deep learning;   

DOI  :  10.3390/s20185411

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown