Evidence map›Paper›PMID 36033543›Full record

ArticleFrontiers in oncology2022

Image quality and whole-lesion histogram and texture analysis of diffusion-weighted imaging of breast MRI based on advanced ZOOMit and simultaneous multislice readout-segmented echo-planar imaging.

Kun Sun, Hong Zhu, Bingqing Xia, Xinyue Li, Weimin Chai, Caixia Fu, Benkert Thomas, Wei Liu, Robert Grimm, Weiland Elisabeth and 1 more

Abstract read
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Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Kun SunDepartment of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Hong ZhuDepartment of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Bingqing XiaDepartment of Radiology, International Peace Maternity and Child Health Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Xinyue LiDepartment of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Weimin ChaiDepartment of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Caixia FuMR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China.
Benkert ThomasMR Application Predevelopment, Siemens Healthcare, Erlangen, Germany.
Wei LiuMR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China.
Robert GrimmMR Application Predevelopment, Siemens Healthcare, Erlangen, Germany.
Weiland ElisabethMR Application Predevelopment, Siemens Healthcare, Erlangen, Germany.
Fuhua YanDepartment of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To investigate the image quality and diagnostic capability a of whole-lesion histogram and texture analysis of advanced ZOOMit (A-ZOOMit) and simultaneous multislice readout-segmented echo-planar imaging (SMS-RS-EPI) to differentiate benign from malignant breast lesions. Study design: From February 2020 to October 2020, diffusion-weighted imaging (DWI) using SMS-RS-EPI and A-ZOOMit were performed on 167 patients. Three breast radiologists independently ranked the image datasets. The inter-/intracorrelation coefficients (ICCs) of mean image quality scores and lesion conspicuity scores were calculated between these three readers. Histogram and texture features were extracted from the apparent diffusion coefficient (ADC) maps, respectively, based on a WL analysis. Student's t-tests, one-way ANOVAs, Mann-Whitney U tests, and receiver operating characteristic curves were used for statistical analysis. Results: The overall image quality scores and lesion conspicuity scores for A-ZOOMit and SMS-RS-EPI showed statistically significant differences (4.92 ± 0.27 Conclusions: A-ZOOMit achieved higher image quality and lesion conspicuity than SMS-RS-EPI. Entropy based on A-ZOOMit is recommended for differentiating benign from malignant breast lesions.

Indexed as

breast neoplasmdiffusion weighted imaginghistogram analysismagnetic resonance imagingtexture analysiswhole lesion

Identifiers

PMID36033543
PMCPMC9411810

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.