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Review
. 2022 Jul 4:14:17562872221109020.
doi: 10.1177/17562872221109020. eCollection 2022 Jan-Dec.

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

Matteo Ferro  1 , Ottavio de Cobelli  2 , Gennaro Musi  2 , Francesco Del Giudice  3 , Giuseppe Carrieri  4 , Gian Maria Busetto  4 , Ugo Giovanni Falagario  4 , Alessandro Sciarra  3 , Martina Maggi  3 , Felice Crocetto  5 , Biagio Barone  5 , Vincenzo Francesco Caputo  5 , Michele Marchioni  6 , Giuseppe Lucarelli  7 , Ciro Imbimbo  5 , Francesco Alessandro Mistretta  8 , Stefano Luzzago  8 , Mihai Dorin Vartolomei  9 , Luigi Cormio  10 , Riccardo Autorino  11 , Octavian Sabin Tătaru  12
Affiliations
Review

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

Matteo Ferro et al. Ther Adv Urol. .

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.

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Conflict of interest statement

Conflict of interest statement: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
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.
Figure 2.
PRISMA flowchart of included studies.
See this image and copyright information in PMC

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