TY - JOUR
T1 - Prostate cancer radiogenomics—from imaging to molecular characterization
AU - Ferro, Matteo
AU - de Cobelli, Ottavio
AU - Vartolomei, Mihai Dorin
AU - Lucarelli, Giuseppe
AU - Crocetto, Felice
AU - Barone, Biagio
AU - Sciarra, Alessandro
AU - Del Giudice, Francesco
AU - Muto, Matteo
AU - Maggi, Martina
AU - Carrieri, Giuseppe
AU - Busetto, Gian Maria
AU - Falagario, Ugo
AU - Terracciano, Daniela
AU - Cormio, Luigi
AU - Musi, Gennaro
AU - Tataru, Octavian Sabin
N1 - Funding Information:
The authors would like to express their deepest gratitude to Fondazione Muto Onlus in Naples for the support of the publication of this manuscript.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/9
Y1 - 2021/9
N2 - Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radio-logical assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-de-signed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.
AB - Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radio-logical assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-de-signed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.
KW - Genomics
KW - Molecular characterization
KW - MRI
KW - PET-CT
KW - Prostate cancer
KW - Radiogenomics
KW - Radiomics
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U2 - 10.3390/ijms22189971
DO - 10.3390/ijms22189971
M3 - Review article
AN - SCOPUS:85114888219
VL - 22
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
SN - 1661-6596
IS - 18
M1 - 9971
ER -