Radiomics in prostate cancer: an up-to-date review

Abstract

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.

Keywords: MRI; PET-CT; artificial intelligence; prostate cancer; radiomics.

Figure 1.

A typical radiomics workflow, including the extraction of features, the data integration and analysis, and the production of predictive model. CNN, convolutional neural network; DL, deep learning; GLCM, gray-level co-occurrence matrix; GLDZM, gray-level distance zone matrix; GLRLM, gray-level run length matrix; GLSZM, gray-level size-zone matrix; ML, machine-learning.

Figure 2.

PRISMA flowchart of included studies.