Dataset Information

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.

SUBMITTER: Ferro M

PROVIDER: S-EPMC9260602 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature

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

Ferro Matteo M de Cobelli Ottavio O Musi Gennaro G Del Giudice Francesco F Carrieri Giuseppe G Busetto Gian Maria GM Falagario Ugo Giovanni UG Sciarra Alessandro A Maggi Martina M Crocetto Felice F Barone Biagio B Caputo Vincenzo Francesco VF Marchioni Michele M Lucarelli Giuseppe G Imbimbo Ciro C Mistretta Francesco Alessandro FA Luzzago Stefano S Vartolomei Mihai Dorin MD Cormio Luigi L Autorino Riccardo R Tătaru Octavian Sabin OS

Therapeutic advances in urology 20220101

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 ...[more]

PMID: 35814914

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Project description:Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.

Dataset Information

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

Publications

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

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