Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning.
Yu SunHayley M ReynoldsDarren WraithScott WilliamsMary E FinneganCatherine MitchellDeclan MurphyAnnette HaworthPublished in: Acta oncologica (Stockholm, Sweden) (2018)
Prostate cell density can be quantitatively estimated non-invasively from mpMRI data using high-quality co-registered data at a voxel level. These cell density predictions could be used for tissue classification, treatment response evaluation and personalised radiotherapy.
Keyphrases
- machine learning
- magnetic resonance imaging
- single cell
- prostate cancer
- cell therapy
- big data
- electronic health record
- deep learning
- computed tomography
- radiation therapy
- early stage
- stem cells
- mesenchymal stem cells
- magnetic resonance
- artificial intelligence
- benign prostatic hyperplasia
- radiation induced
- locally advanced