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Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical–Radiological Model
PI-RADS 3 prostate lesions clinical management is still debated, with high variability among different centers. Identifying clinically significant tumors among PI-RADS 3 is crucial. Radiomics applied to multiparametric MR (mpMR) seems promising. Nevertheless, reproducibility assessment by external v...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9656103/ https://www.ncbi.nlm.nih.gov/pubmed/36362530 http://dx.doi.org/10.3390/jcm11216304 |
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author | Corsi, Andrea De Bernardi, Elisabetta Bonaffini, Pietro Andrea Franco, Paolo Niccolò Nicoletta, Dario Simonini, Roberto Ippolito, Davide Perugini, Giovanna Occhipinti, Mariaelena Da Pozzo, Luigi Filippo Roscigno, Marco Sironi, Sandro |
author_facet | Corsi, Andrea De Bernardi, Elisabetta Bonaffini, Pietro Andrea Franco, Paolo Niccolò Nicoletta, Dario Simonini, Roberto Ippolito, Davide Perugini, Giovanna Occhipinti, Mariaelena Da Pozzo, Luigi Filippo Roscigno, Marco Sironi, Sandro |
author_sort | Corsi, Andrea |
collection | PubMed |
description | PI-RADS 3 prostate lesions clinical management is still debated, with high variability among different centers. Identifying clinically significant tumors among PI-RADS 3 is crucial. Radiomics applied to multiparametric MR (mpMR) seems promising. Nevertheless, reproducibility assessment by external validation is required. We retrospectively included all patients with at least one PI-RADS 3 lesion (PI-RADS v2.1) detected on a 3T prostate MRI scan at our Institution (June 2016–March 2021). An MRI-targeted biopsy was used as ground truth. We assessed reproducible mpMRI radiomic features found in the literature. Then, we proposed a new model combining PSA density and two radiomic features (texture regularity (T2) and size zone heterogeneity (ADC)). All models were trained/assessed through 100-repetitions 5-fold cross-validation. Eighty patients were included (26 with GS ≥ 7). In total, 9/20 T2 features (Hector’s model) and 1 T2 feature (Jin’s model) significantly correlated to biopsy on our dataset. PSA density alone predicted clinically significant tumors (sensitivity: 66%; specificity: 71%). Our model obtained a sensitivity of 80% and a specificity of 76%. Standard-compliant works with detailed methodologies achieve comparable radiomic feature sets. Therefore, efforts to facilitate reproducibility are needed, while complex models and imaging protocols seem not, since our model combining PSA density and two radiomic features from routinely performed sequences appeared to differentiate clinically significant cancers. |
format | Online Article Text |
id | pubmed-9656103 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96561032022-11-15 Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical–Radiological Model Corsi, Andrea De Bernardi, Elisabetta Bonaffini, Pietro Andrea Franco, Paolo Niccolò Nicoletta, Dario Simonini, Roberto Ippolito, Davide Perugini, Giovanna Occhipinti, Mariaelena Da Pozzo, Luigi Filippo Roscigno, Marco Sironi, Sandro J Clin Med Article PI-RADS 3 prostate lesions clinical management is still debated, with high variability among different centers. Identifying clinically significant tumors among PI-RADS 3 is crucial. Radiomics applied to multiparametric MR (mpMR) seems promising. Nevertheless, reproducibility assessment by external validation is required. We retrospectively included all patients with at least one PI-RADS 3 lesion (PI-RADS v2.1) detected on a 3T prostate MRI scan at our Institution (June 2016–March 2021). An MRI-targeted biopsy was used as ground truth. We assessed reproducible mpMRI radiomic features found in the literature. Then, we proposed a new model combining PSA density and two radiomic features (texture regularity (T2) and size zone heterogeneity (ADC)). All models were trained/assessed through 100-repetitions 5-fold cross-validation. Eighty patients were included (26 with GS ≥ 7). In total, 9/20 T2 features (Hector’s model) and 1 T2 feature (Jin’s model) significantly correlated to biopsy on our dataset. PSA density alone predicted clinically significant tumors (sensitivity: 66%; specificity: 71%). Our model obtained a sensitivity of 80% and a specificity of 76%. Standard-compliant works with detailed methodologies achieve comparable radiomic feature sets. Therefore, efforts to facilitate reproducibility are needed, while complex models and imaging protocols seem not, since our model combining PSA density and two radiomic features from routinely performed sequences appeared to differentiate clinically significant cancers. MDPI 2022-10-26 /pmc/articles/PMC9656103/ /pubmed/36362530 http://dx.doi.org/10.3390/jcm11216304 Text en © 2022 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 | Article Corsi, Andrea De Bernardi, Elisabetta Bonaffini, Pietro Andrea Franco, Paolo Niccolò Nicoletta, Dario Simonini, Roberto Ippolito, Davide Perugini, Giovanna Occhipinti, Mariaelena Da Pozzo, Luigi Filippo Roscigno, Marco Sironi, Sandro Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical–Radiological Model |
title | Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical–Radiological Model |
title_full | Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical–Radiological Model |
title_fullStr | Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical–Radiological Model |
title_full_unstemmed | Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical–Radiological Model |
title_short | Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical–Radiological Model |
title_sort | radiomics in pi-rads 3 multiparametric mri for prostate cancer identification: literature models re-implementation and proposal of a clinical–radiological model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9656103/ https://www.ncbi.nlm.nih.gov/pubmed/36362530 http://dx.doi.org/10.3390/jcm11216304 |
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