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Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature
Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization an...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534893/ https://www.ncbi.nlm.nih.gov/pubmed/34679527 http://dx.doi.org/10.3390/diagnostics11101829 |
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author | Midiri, Federico Vernuccio, Federica Purpura, Pierpaolo Alongi, Pierpaolo Bartolotta, Tommaso Vincenzo |
author_facet | Midiri, Federico Vernuccio, Federica Purpura, Pierpaolo Alongi, Pierpaolo Bartolotta, Tommaso Vincenzo |
author_sort | Midiri, Federico |
collection | PubMed |
description | Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization and cancer staging. mp-MRI plays a key role in risk stratification of naïve patients, in active surveillance for low-risk patients, and in monitoring recurrence after definitive therapy. Radiomics is an emerging and promising tool which allows a quantitative tumor evaluation from radiological images via conversion of digital images into mineable high-dimensional data. The purpose of radiomics is to increase the features available to detect PCa, to avoid unnecessary biopsies, to define tumor aggressiveness, and to monitor post-treatment recurrence of PCa. The integration of radiomics data, including different imaging modalities (such as PET-CT) and other clinical and histopathological data, could improve the prediction of tumor aggressiveness as well as guide clinical decisions and patient management. The purpose of this review is to describe the current research applications of radiomics in PCa on MR images. |
format | Online Article Text |
id | pubmed-8534893 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85348932021-10-23 Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature Midiri, Federico Vernuccio, Federica Purpura, Pierpaolo Alongi, Pierpaolo Bartolotta, Tommaso Vincenzo Diagnostics (Basel) Review Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization and cancer staging. mp-MRI plays a key role in risk stratification of naïve patients, in active surveillance for low-risk patients, and in monitoring recurrence after definitive therapy. Radiomics is an emerging and promising tool which allows a quantitative tumor evaluation from radiological images via conversion of digital images into mineable high-dimensional data. The purpose of radiomics is to increase the features available to detect PCa, to avoid unnecessary biopsies, to define tumor aggressiveness, and to monitor post-treatment recurrence of PCa. The integration of radiomics data, including different imaging modalities (such as PET-CT) and other clinical and histopathological data, could improve the prediction of tumor aggressiveness as well as guide clinical decisions and patient management. The purpose of this review is to describe the current research applications of radiomics in PCa on MR images. MDPI 2021-10-03 /pmc/articles/PMC8534893/ /pubmed/34679527 http://dx.doi.org/10.3390/diagnostics11101829 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Midiri, Federico Vernuccio, Federica Purpura, Pierpaolo Alongi, Pierpaolo Bartolotta, Tommaso Vincenzo Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature |
title | Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature |
title_full | Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature |
title_fullStr | Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature |
title_full_unstemmed | Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature |
title_short | Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature |
title_sort | multiparametric mri and radiomics in prostate cancer: a review of the current literature |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534893/ https://www.ncbi.nlm.nih.gov/pubmed/34679527 http://dx.doi.org/10.3390/diagnostics11101829 |
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