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Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination
OBJECTIVE: To assess the diagnostic value of predictive models based on synthetic magnetic resonance imaging (syMRI), multiplexed sensitivity encoding (MUSE) sequences, and Breast Imaging Reporting and Data System (BI-RADS) in the differentiation of benign and malignant breast lesions. METHODS: Clin...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9936239/ https://www.ncbi.nlm.nih.gov/pubmed/36818669 http://dx.doi.org/10.3389/fonc.2022.1080580 |
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author | Liu, Jinrui Xu, Mengying Ren, Jialiang Li, Zhihao Xi, Lu Chen, Bing |
author_facet | Liu, Jinrui Xu, Mengying Ren, Jialiang Li, Zhihao Xi, Lu Chen, Bing |
author_sort | Liu, Jinrui |
collection | PubMed |
description | OBJECTIVE: To assess the diagnostic value of predictive models based on synthetic magnetic resonance imaging (syMRI), multiplexed sensitivity encoding (MUSE) sequences, and Breast Imaging Reporting and Data System (BI-RADS) in the differentiation of benign and malignant breast lesions. METHODS: Clinical and MRI data of 158 patients with breast lesions who underwent dynamic contrast-enhanced MRI (DCE-MRI), syMRI, and MUSE sequences between September 2019 and December 2020 were retrospectively collected. The apparent diffusion coefficient (ADC) values of MUSE and quantitative relaxation parameters (longitudinal and transverse relaxation times [T1, T2], and proton density [PD] values) of syMRI were measured, and the parameter variation values and change in their ratios were calculated. The patients were randomly divided into training (n = 111) and validation (n = 47) groups at a ratio of 7:3. A nomogram was built based on univariate and multivariate logistic regression analyses in the training group and was verified in the validation group. The discriminatory and predictive capacities of the nomogram were assessed by the receiver operating characteristic curve and area under the curve (AUC). The AUC was compared by DeLong test. RESULTS: In the training group, univariate analysis showed that age, lesion diameter, menopausal status, ADC, T2(pre), PD(pre), PD(Gd), T2(Delta), and T2(ratio) were significantly different between benign and malignant breast lesions (P < 0.05). Multivariate logistic regression analysis showed that ADC and T2(pre) were significant variables (all P < 0.05) in breast cancer diagnosis. The quantitative model (model A: ADC, T2(pre)), BI-RADS model (model B), and multi-parameter model (model C: ADC, T2(pre), BI-RADS) were established by combining the above independent variables, among which model C had the highest diagnostic performance, with AUC of 0.965 and 0.986 in the training and validation groups, respectively. CONCLUSIONS: The prediction model established based on syMRI, MUSE sequence, and BI-RADS is helpful for clinical differentiation of breast tumors and provides more accurate information for individualized diagnosis. |
format | Online Article Text |
id | pubmed-9936239 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99362392023-02-18 Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination Liu, Jinrui Xu, Mengying Ren, Jialiang Li, Zhihao Xi, Lu Chen, Bing Front Oncol Oncology OBJECTIVE: To assess the diagnostic value of predictive models based on synthetic magnetic resonance imaging (syMRI), multiplexed sensitivity encoding (MUSE) sequences, and Breast Imaging Reporting and Data System (BI-RADS) in the differentiation of benign and malignant breast lesions. METHODS: Clinical and MRI data of 158 patients with breast lesions who underwent dynamic contrast-enhanced MRI (DCE-MRI), syMRI, and MUSE sequences between September 2019 and December 2020 were retrospectively collected. The apparent diffusion coefficient (ADC) values of MUSE and quantitative relaxation parameters (longitudinal and transverse relaxation times [T1, T2], and proton density [PD] values) of syMRI were measured, and the parameter variation values and change in their ratios were calculated. The patients were randomly divided into training (n = 111) and validation (n = 47) groups at a ratio of 7:3. A nomogram was built based on univariate and multivariate logistic regression analyses in the training group and was verified in the validation group. The discriminatory and predictive capacities of the nomogram were assessed by the receiver operating characteristic curve and area under the curve (AUC). The AUC was compared by DeLong test. RESULTS: In the training group, univariate analysis showed that age, lesion diameter, menopausal status, ADC, T2(pre), PD(pre), PD(Gd), T2(Delta), and T2(ratio) were significantly different between benign and malignant breast lesions (P < 0.05). Multivariate logistic regression analysis showed that ADC and T2(pre) were significant variables (all P < 0.05) in breast cancer diagnosis. The quantitative model (model A: ADC, T2(pre)), BI-RADS model (model B), and multi-parameter model (model C: ADC, T2(pre), BI-RADS) were established by combining the above independent variables, among which model C had the highest diagnostic performance, with AUC of 0.965 and 0.986 in the training and validation groups, respectively. CONCLUSIONS: The prediction model established based on syMRI, MUSE sequence, and BI-RADS is helpful for clinical differentiation of breast tumors and provides more accurate information for individualized diagnosis. Frontiers Media S.A. 2023-02-03 /pmc/articles/PMC9936239/ /pubmed/36818669 http://dx.doi.org/10.3389/fonc.2022.1080580 Text en Copyright © 2023 Liu, Xu, Ren, Li, Xi and Chen 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 Liu, Jinrui Xu, Mengying Ren, Jialiang Li, Zhihao Xi, Lu Chen, Bing Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination |
title | Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination |
title_full | Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination |
title_fullStr | Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination |
title_full_unstemmed | Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination |
title_short | Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination |
title_sort | synthetic mri, multiplexed sensitivity encoding, and bi-rads for benign and malignant breast cancer discrimination |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9936239/ https://www.ncbi.nlm.nih.gov/pubmed/36818669 http://dx.doi.org/10.3389/fonc.2022.1080580 |
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