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Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer
BACKGROUND: Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS asses...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730331/ https://www.ncbi.nlm.nih.gov/pubmed/36505875 http://dx.doi.org/10.3389/fonc.2022.961985 |
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author | Singh, Dharmesh Kumar, Virendra Das, Chandan J. Singh, Anup Mehndiratta, Amit |
author_facet | Singh, Dharmesh Kumar, Virendra Das, Chandan J. Singh, Anup Mehndiratta, Amit |
author_sort | Singh, Dharmesh |
collection | PubMed |
description | BACKGROUND: Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS assessment. The purpose of this study was to evaluate the diagnostic performance of an in-house developed semi-automated model for PI-RADS v2.1 scoring using machine learning methods. METHODS: The study cohort included an MRI dataset of 59 patients (PI-RADS v2.1 score 2 = 18, score 3 = 10, score 4 = 16, and score 5 = 15). The proposed semi-automated model involved prostate gland and zonal segmentation, 3D co-registration, lesion region of interest marking, and lesion measurement. PI-RADS v2.1 scores were assessed based on lesion measurements and compared with the radiologist PI-RADS assessment. Machine learning methods were used to evaluate the diagnostic accuracy of the proposed model by classification of PI-RADS v2.1 scores. RESULTS: The semi-automated PI-RADS assessment based on the proposed model correctly classified 50 out of 59 patients and showed a significant correlation (r = 0.94, p < 0.05) with the radiologist assessment. The proposed model achieved an accuracy of 88.00% ± 0.98% and an area under the receiver-operating characteristic curve (AUC) of 0.94 for score 2 vs. score 3 vs. score 4 vs. score 5 classification and accuracy of 93.20 ± 2.10% and AUC of 0.99 for low score vs. high score classification using fivefold cross-validation. CONCLUSION: The proposed semi-automated PI-RADS v2.1 assessment system could minimize the inter-reader variability among radiologists and improve the objectivity of scoring. |
format | Online Article Text |
id | pubmed-9730331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97303312022-12-09 Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer Singh, Dharmesh Kumar, Virendra Das, Chandan J. Singh, Anup Mehndiratta, Amit Front Oncol Oncology BACKGROUND: Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS assessment. The purpose of this study was to evaluate the diagnostic performance of an in-house developed semi-automated model for PI-RADS v2.1 scoring using machine learning methods. METHODS: The study cohort included an MRI dataset of 59 patients (PI-RADS v2.1 score 2 = 18, score 3 = 10, score 4 = 16, and score 5 = 15). The proposed semi-automated model involved prostate gland and zonal segmentation, 3D co-registration, lesion region of interest marking, and lesion measurement. PI-RADS v2.1 scores were assessed based on lesion measurements and compared with the radiologist PI-RADS assessment. Machine learning methods were used to evaluate the diagnostic accuracy of the proposed model by classification of PI-RADS v2.1 scores. RESULTS: The semi-automated PI-RADS assessment based on the proposed model correctly classified 50 out of 59 patients and showed a significant correlation (r = 0.94, p < 0.05) with the radiologist assessment. The proposed model achieved an accuracy of 88.00% ± 0.98% and an area under the receiver-operating characteristic curve (AUC) of 0.94 for score 2 vs. score 3 vs. score 4 vs. score 5 classification and accuracy of 93.20 ± 2.10% and AUC of 0.99 for low score vs. high score classification using fivefold cross-validation. CONCLUSION: The proposed semi-automated PI-RADS v2.1 assessment system could minimize the inter-reader variability among radiologists and improve the objectivity of scoring. Frontiers Media S.A. 2022-11-24 /pmc/articles/PMC9730331/ /pubmed/36505875 http://dx.doi.org/10.3389/fonc.2022.961985 Text en Copyright © 2022 Singh, Kumar, Das, Singh and Mehndiratta https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Singh, Dharmesh Kumar, Virendra Das, Chandan J. Singh, Anup Mehndiratta, Amit Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer |
title | Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer |
title_full | Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer |
title_fullStr | Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer |
title_full_unstemmed | Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer |
title_short | Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer |
title_sort | machine learning-based analysis of a semi-automated pi-rads v2.1 scoring for prostate cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730331/ https://www.ncbi.nlm.nih.gov/pubmed/36505875 http://dx.doi.org/10.3389/fonc.2022.961985 |
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