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Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions
OBJECTIVE: The conventional breast Diffusion-weighted imaging (DWI) was subtly influenced by microcirculation owing to the insufficient selection of the b values. However, the multiparameter derived from multiple b-value exhibits more reliable image quality and maximize the diagnostic accuracy. We a...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832757/ https://www.ncbi.nlm.nih.gov/pubmed/36631781 http://dx.doi.org/10.1186/s12880-022-00950-y |
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author | Lin, Chu-Xin Tian, Ye Li, Jia-Min Liao, Shu-Ting Liu, Yu-Tao Zhan, Run-Gen Du, Zhong-Li Yu, Xiang-Rong |
author_facet | Lin, Chu-Xin Tian, Ye Li, Jia-Min Liao, Shu-Ting Liu, Yu-Tao Zhan, Run-Gen Du, Zhong-Li Yu, Xiang-Rong |
author_sort | Lin, Chu-Xin |
collection | PubMed |
description | OBJECTIVE: The conventional breast Diffusion-weighted imaging (DWI) was subtly influenced by microcirculation owing to the insufficient selection of the b values. However, the multiparameter derived from multiple b-value exhibits more reliable image quality and maximize the diagnostic accuracy. We aim to evaluate the diagnostic performance of stand-alone parameter or in combination with multiparameter derived from multiple b-value DWI in differentiating malignant from benign breast lesions. METHODS: A total of forty-one patients diagnosed with benign breast tumor and thirty-eight patients with malignant breast tumor underwent DWI using thirteen b values and other MRI functional sequence at 3.0 T magnetic resonance. Data were accepted mono-exponential, bi-exponential, stretched-exponential, aquaporins (AQP) model analysis. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of quantitative parameter or multiparametric combination. The Youden index, sensitivity and specificity were used to assess the optimal diagnostic model. T-test, logistic regression analysis, and Z-test were used. P value < 0.05 was considered statistically significant. RESULT: The ADC(avg), ADC(max), f, and α value of the malignant group were lower than the benign group, while the ADC(fast) value was higher instead. The ADC(min), ADC(slow), DDC and ADC(AQP) showed no statistical significance. The combination (ADC(avg)-ADC(fast)) yielded the largest area under curve (AUC = 0.807) with sensitivity (68.42%), specificity (87.8%) and highest Youden index, indicating that multiparametric combination (ADC(avg)-ADC(fast)) was validated to be a useful model in differentiating the benign from breast malignant lesion. CONCLUSION: The current study based on the multiple b-value diffusion model demonstrated quantitatively multiparametric combination (ADC(avg-)ADC(fast)) exhibited the optimal diagnostic efficacy to differentiate malignant from benign breast lesions, suggesting that multiparameter would be a promising non-invasiveness to diagnose breast lesions. |
format | Online Article Text |
id | pubmed-9832757 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-98327572023-01-12 Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions Lin, Chu-Xin Tian, Ye Li, Jia-Min Liao, Shu-Ting Liu, Yu-Tao Zhan, Run-Gen Du, Zhong-Li Yu, Xiang-Rong BMC Med Imaging Research OBJECTIVE: The conventional breast Diffusion-weighted imaging (DWI) was subtly influenced by microcirculation owing to the insufficient selection of the b values. However, the multiparameter derived from multiple b-value exhibits more reliable image quality and maximize the diagnostic accuracy. We aim to evaluate the diagnostic performance of stand-alone parameter or in combination with multiparameter derived from multiple b-value DWI in differentiating malignant from benign breast lesions. METHODS: A total of forty-one patients diagnosed with benign breast tumor and thirty-eight patients with malignant breast tumor underwent DWI using thirteen b values and other MRI functional sequence at 3.0 T magnetic resonance. Data were accepted mono-exponential, bi-exponential, stretched-exponential, aquaporins (AQP) model analysis. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of quantitative parameter or multiparametric combination. The Youden index, sensitivity and specificity were used to assess the optimal diagnostic model. T-test, logistic regression analysis, and Z-test were used. P value < 0.05 was considered statistically significant. RESULT: The ADC(avg), ADC(max), f, and α value of the malignant group were lower than the benign group, while the ADC(fast) value was higher instead. The ADC(min), ADC(slow), DDC and ADC(AQP) showed no statistical significance. The combination (ADC(avg)-ADC(fast)) yielded the largest area under curve (AUC = 0.807) with sensitivity (68.42%), specificity (87.8%) and highest Youden index, indicating that multiparametric combination (ADC(avg)-ADC(fast)) was validated to be a useful model in differentiating the benign from breast malignant lesion. CONCLUSION: The current study based on the multiple b-value diffusion model demonstrated quantitatively multiparametric combination (ADC(avg-)ADC(fast)) exhibited the optimal diagnostic efficacy to differentiate malignant from benign breast lesions, suggesting that multiparameter would be a promising non-invasiveness to diagnose breast lesions. BioMed Central 2023-01-11 /pmc/articles/PMC9832757/ /pubmed/36631781 http://dx.doi.org/10.1186/s12880-022-00950-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Lin, Chu-Xin Tian, Ye Li, Jia-Min Liao, Shu-Ting Liu, Yu-Tao Zhan, Run-Gen Du, Zhong-Li Yu, Xiang-Rong Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions |
title | Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions |
title_full | Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions |
title_fullStr | Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions |
title_full_unstemmed | Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions |
title_short | Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions |
title_sort | diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832757/ https://www.ncbi.nlm.nih.gov/pubmed/36631781 http://dx.doi.org/10.1186/s12880-022-00950-y |
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