Evidence map›Paper›PMID 38106260›Full record

ArticleQuantitative imaging in medicine and surgery2023

A background parenchymal enhancement quantification framework of breast magnetic resonance imaging.

Boya Zhang, Jingjin Zhu, Peifang Zhang, Yufan Wei, Yan Li, Aoxi Xu, Yiheng Zhang, Hongye Zheng, Xiaohan Dong, Kaizhou Yang and 4 more

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Boya ZhangSchool of Medicine, Nankai University, Tianjin, China.
Jingjin ZhuSchool of Medicine, Nankai University, Tianjin, China.
Peifang ZhangDepartment of Big Data Center, The First Medical Center of Chinese PLA General Hospital, Beijing, China.
Yufan WeiSchool of Medicine, Nankai University, Tianjin, China.
Yan LiDepartment of General Surgery, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Aoxi XuSchool of Medicine, Nankai University, Tianjin, China.
Yiheng ZhangDepartment of General Surgery, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Hongye ZhengSchool of Medicine, Nankai University, Tianjin, China.
Xiaohan DongDepartment of Radiology, The Sixth Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Kaizhou YangDepartment of Radiology, The Sixth Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Chuang DongDepartment of Radiology, The Sixth Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Zhengming ChenDepartment of Radiology, The Sixth Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Xiru LiSchool of Medicine, Nankai University, Tianjin, China.
Liuquan ChengDepartment of Radiology, The Sixth Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Background parenchymal enhancement (BPE) is defined as the enhanced proportion of normal fibroglandular tissue on enhanced magnetic resonance imaging. BPE shows promise as a quantitative imaging biomarker (QIB). However, the lack of consensus among radiologists in their semi-quantitative grading of BPE limits its clinical utility. Methods: The main objective of this study was to develop a BPE quantification model according to clinical expertise, with the BPE integral being used as a QIB to incorporate both the volume and intensity of the enhancement metrics. The model was applied to 2,786 cases to compare our quantitative results with radiologists' semi-quantitative BPE grading to evaluate the effectiveness of using the BPE integral as a QIB for analyzing BPE. Comparisons between multiple groups of nonnormally distributed BPE integrals were performed using the Kruskal-Wallis test. Results: Our study found a considerable degree of concordance between our BPE quantitative integral and radiologists' semi-quantitative assessments. Specifically, our research results revealed significant variability in BPE integral attained through the BPE quantification framework among all semi-quantitative BPE grading groups labeled by experienced radiologists, including mild-moderate (P<0.001), mild-marked (P<0.001), and moderate-marked (P<0.001). Furthermore, there was an apparent correlation between BPE integral and BPE grades, with marked BPE displaying the highest BPE integral, followed by moderate BPE, with mild BPE exhibiting the lowest BPE integral value. Conclusions: The study developed and implemented a BPE quantification framework, which incorporated both the volume and intensity of enhancement and which could serve as a QIB for BPE.

Indexed as

Background parenchymal enhancement (BPE)breast magnetic resonance imaging (breast MRI)quantitative imaging biomarker (QIB)

Identifiers

PMID38106260
PMCPMC10721989

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