Radiomics in prostate cancer: an up-to-date review.
Matteo FerroOttavio de CobelliGennaro MusiFrancesco Del GiudiceGiuseppe CarrieriGian Maria BusettoUgo Giovanni FalagarioAlessandro SciarraMartina MaggiFelice CrocettoBiagio BaroneVincenzo Francesco CaputoMichele MarchioniGiuseppe LucarelliCiro ImbimboFrancesco Alessandro MistrettaStefano LuzzagoMihai Dorin VartolomeiLuigi CormioRiccardo AutorinoOctavian Sabin TătaruPublished in: Therapeutic advances in urology (2022)
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
Keyphrases
- prostate cancer
- artificial intelligence
- lymph node metastasis
- deep learning
- end stage renal disease
- radical prostatectomy
- machine learning
- contrast enhanced
- high resolution
- big data
- newly diagnosed
- chronic kidney disease
- ejection fraction
- systematic review
- prognostic factors
- squamous cell carcinoma
- peritoneal dialysis
- combination therapy
- computed tomography
- magnetic resonance
- current status
- replacement therapy
- mass spectrometry
- optical coherence tomography
- convolutional neural network
- photodynamic therapy
- fluorescence imaging