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.

Radiomics in prostate cancer: an up-to-date review / M. Ferro, O. de Cobelli, G. Musi, F. Del Giudice, G. Carrieri, G.M. Busetto, U.G. Falagario, A. Sciarra, M. Maggi, F. Crocetto, B. Barone, V.F. Caputo, M. Marchioni, G. Lucarelli, C. Imbimbo, F.A. Mistretta, S. Luzzago, M.D. Vartolomei, L. Cormio, R. Autorino, O.S. Tătaru. - In: THERAPEUTIC ADVANCES IN UROLOGY. - ISSN 1756-2872. - 14:(2022 Jul), pp. 17562872221109020.1-17562872221109020.37. [10.1177/17562872221109020]

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

O. de Cobelli
Secondo
;
G. Musi;F.A. Mistretta;S. Luzzago;
2022

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.
MRI; PET-CT; artificial intelligence; prostate cancer; radiomics
Settore MED/24 - Urologia
lug-2022
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/951453
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