口腔颌面部低流量血管畸形的纹理分析:静脉畸形与淋巴畸形。

IF 0.9 Q4 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Polish Journal of Radiology Pub Date : 2022-09-05 eCollection Date: 2022-01-01 DOI:10.5114/pjr.2022.119473
Kotaro Ito, Hirotaka Muraoka, Naohisa Hirahara, Eri Sawada, Satoshi Tokunaga, Takashi Kaneda
{"title":"口腔颌面部低流量血管畸形的纹理分析:静脉畸形与淋巴畸形。","authors":"Kotaro Ito,&nbsp;Hirotaka Muraoka,&nbsp;Naohisa Hirahara,&nbsp;Eri Sawada,&nbsp;Satoshi Tokunaga,&nbsp;Takashi Kaneda","doi":"10.5114/pjr.2022.119473","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>It is challenging for radiologists to distinguish between venous malformations (VMs) and lymphatic malformations (LMs) using magnetic resonance imaging (MRI). Thus, this study aimed to differentiate VMs from LMs using non-contrast-enhanced MRI texture analysis.</p><p><strong>Material and methods: </strong>This retrospective case-control study included 12 LM patients (6 men and 6 women; mean age 43.58, range 7-85 years) and 29 VM patients (7 men and 22 women; mean age 53.10, range 19-76 years) who underwent MRI for suspected vascular malformations. LM and VM patients were identified by histopathological examination of tissues excised during surgery. The texture features of VM and LM were analysed using the open-access software MaZda version 3.3. Seventeen texture features were selected using the Fisher and probability of error and average correlation coefficient methods in MaZda from 279 original parameters calculated for VM and LM.</p><p><strong>Results: </strong>Among 17 selected texture features, the patients with LM and VM revealed significant differences in 1 histogram feature, 8 grey-level co-occurrence matrix (GLCM) features, and 1 grey-level run-length matrix feature. At the cut-off values of the histogram feature [skewness ≤ -0.131], and the GLCM features [S(0, 2) correlation ≥ 0.667, S(0, 3) correlation ≥ 0.451, S(0, 4) correlation ≥ 0.276, S(0, 5) correlation ≥ 0.389, S(1, 1) correlation ≥ 0.739, S(2, 2) correlation ≥ 0.446, S(2, -2) correlation ≥ 0.299, S(3, -3) correlation ≥ 0.091] had area under the curves of 0.724, 0.764, 0.773, 0.747, 0.733, 0.759, 0.730, 0.744 and 0.727, respectively.</p><p><strong>Conclusions: </strong>Non-contrast-enhanced MRI texture analysis allows us to differentiate between LMs and VMs.</p>","PeriodicalId":47128,"journal":{"name":"Polish Journal of Radiology","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/3f/79/PJR-87-47790.PMC9536206.pdf","citationCount":"0","resultStr":"{\"title\":\"Texture analysis of low-flow vascular malformations in the oral and maxillofacial region: venous malformation vs. lymphatic malformation.\",\"authors\":\"Kotaro Ito,&nbsp;Hirotaka Muraoka,&nbsp;Naohisa Hirahara,&nbsp;Eri Sawada,&nbsp;Satoshi Tokunaga,&nbsp;Takashi Kaneda\",\"doi\":\"10.5114/pjr.2022.119473\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>It is challenging for radiologists to distinguish between venous malformations (VMs) and lymphatic malformations (LMs) using magnetic resonance imaging (MRI). Thus, this study aimed to differentiate VMs from LMs using non-contrast-enhanced MRI texture analysis.</p><p><strong>Material and methods: </strong>This retrospective case-control study included 12 LM patients (6 men and 6 women; mean age 43.58, range 7-85 years) and 29 VM patients (7 men and 22 women; mean age 53.10, range 19-76 years) who underwent MRI for suspected vascular malformations. LM and VM patients were identified by histopathological examination of tissues excised during surgery. The texture features of VM and LM were analysed using the open-access software MaZda version 3.3. Seventeen texture features were selected using the Fisher and probability of error and average correlation coefficient methods in MaZda from 279 original parameters calculated for VM and LM.</p><p><strong>Results: </strong>Among 17 selected texture features, the patients with LM and VM revealed significant differences in 1 histogram feature, 8 grey-level co-occurrence matrix (GLCM) features, and 1 grey-level run-length matrix feature. At the cut-off values of the histogram feature [skewness ≤ -0.131], and the GLCM features [S(0, 2) correlation ≥ 0.667, S(0, 3) correlation ≥ 0.451, S(0, 4) correlation ≥ 0.276, S(0, 5) correlation ≥ 0.389, S(1, 1) correlation ≥ 0.739, S(2, 2) correlation ≥ 0.446, S(2, -2) correlation ≥ 0.299, S(3, -3) correlation ≥ 0.091] had area under the curves of 0.724, 0.764, 0.773, 0.747, 0.733, 0.759, 0.730, 0.744 and 0.727, respectively.</p><p><strong>Conclusions: </strong>Non-contrast-enhanced MRI texture analysis allows us to differentiate between LMs and VMs.</p>\",\"PeriodicalId\":47128,\"journal\":{\"name\":\"Polish Journal of Radiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2022-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/3f/79/PJR-87-47790.PMC9536206.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Polish Journal of Radiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5114/pjr.2022.119473\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q4\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Polish Journal of Radiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5114/pjr.2022.119473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

目的:利用磁共振成像(MRI)对放射科医生区分静脉畸形(vm)和淋巴畸形(LMs)具有挑战性。因此,本研究旨在通过非对比增强MRI纹理分析来区分vm和lm。材料和方法:本回顾性病例对照研究纳入12例LM患者(6男6女;平均年龄43.58岁,年龄范围7-85岁),29例VM患者(男性7例,女性22例;平均年龄53.10岁,年龄范围19-76岁),因怀疑血管畸形行MRI检查。LM和VM患者通过手术中切除组织的组织病理学检查来确定。利用马自达3.3版开放存取软件对VM和LM的纹理特征进行分析。采用Fisher、误差概率和马自达平均相关系数方法,从VM和LM计算的279个原始参数中选择17个纹理特征。结果:在选取的17个纹理特征中,LM和VM患者在1个直方图特征、8个灰度共生矩阵(GLCM)特征和1个灰度游程矩阵特征上存在显著差异。直方图特征[偏度≤-0.131]和GLCM特征[S(0,2)相关性≥0.667,S(0,3)相关性≥0.451,S(0,4)相关性≥0.276,S(0,5)相关性≥0.389,S(1,1)相关性≥0.739,S(2,2)相关性≥0.446,S(2, -2)相关性≥0.299,S(3, -3)相关性≥0.091]的截点下曲线面积分别为0.724、0.764、0.773、0.747、0.733、0.759、0.730、0.744、0.727。结论:非增强MRI纹理分析可以帮助我们区分LMs和vm。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Texture analysis of low-flow vascular malformations in the oral and maxillofacial region: venous malformation vs. lymphatic malformation.

Texture analysis of low-flow vascular malformations in the oral and maxillofacial region: venous malformation vs. lymphatic malformation.

Purpose: It is challenging for radiologists to distinguish between venous malformations (VMs) and lymphatic malformations (LMs) using magnetic resonance imaging (MRI). Thus, this study aimed to differentiate VMs from LMs using non-contrast-enhanced MRI texture analysis.

Material and methods: This retrospective case-control study included 12 LM patients (6 men and 6 women; mean age 43.58, range 7-85 years) and 29 VM patients (7 men and 22 women; mean age 53.10, range 19-76 years) who underwent MRI for suspected vascular malformations. LM and VM patients were identified by histopathological examination of tissues excised during surgery. The texture features of VM and LM were analysed using the open-access software MaZda version 3.3. Seventeen texture features were selected using the Fisher and probability of error and average correlation coefficient methods in MaZda from 279 original parameters calculated for VM and LM.

Results: Among 17 selected texture features, the patients with LM and VM revealed significant differences in 1 histogram feature, 8 grey-level co-occurrence matrix (GLCM) features, and 1 grey-level run-length matrix feature. At the cut-off values of the histogram feature [skewness ≤ -0.131], and the GLCM features [S(0, 2) correlation ≥ 0.667, S(0, 3) correlation ≥ 0.451, S(0, 4) correlation ≥ 0.276, S(0, 5) correlation ≥ 0.389, S(1, 1) correlation ≥ 0.739, S(2, 2) correlation ≥ 0.446, S(2, -2) correlation ≥ 0.299, S(3, -3) correlation ≥ 0.091] had area under the curves of 0.724, 0.764, 0.773, 0.747, 0.733, 0.759, 0.730, 0.744 and 0.727, respectively.

Conclusions: Non-contrast-enhanced MRI texture analysis allows us to differentiate between LMs and VMs.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Polish Journal of Radiology
Polish Journal of Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
2.10
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信