MRI-based radiomics in distinguishing Kaposiform hemangioendothelioma (KHE) and fibro-adipose vascular anomaly (FAVA) in extremities: A preliminary retrospective study

医学 接收机工作特性 Lasso(编程语言) 回顾性队列研究 支持向量机 血管异常 磁共振成像 队列 放射科 无线电技术 核医学 人工智能 外科 病理 内科学 计算机科学 万维网
作者
Yingjing Ding,Zuopeng Wang,Ping Xu,Yangyang Ma,Wei Yao,Kai Li,Ying Gong
出处
期刊:Journal of Pediatric Surgery [Elsevier]
卷期号:57 (7): 1228-1234 被引量:3
标识
DOI:10.1016/j.jpedsurg.2022.02.031
摘要

To investigate the pretreatment differentiation between Kaposiform hemangioendothelioma (KHE) and fibro-adipose vascular anomaly (FAVA) in extremities of pediatric patients. To build and validate an MRI-based radiomic model.In this retrospective study, we obtained imaging data from 43 patients. We collected and compared clinical information, sketched region of interest (ROI), and extracted radiomic features from fat-suppressed T2-weighted (T2FS) images of the two cohorts of 30 and 13 patients respectively (training versus testing cohort 7:3). To select features, we used two sample t-test and the least absolute shrinkage and selection operator (LASSO) regression. The support vector machine (SVM) classification was constructed and evaluated by receiver operating characteristic (ROC) analysis.Thirty patients with KHE and 13 patients with FAVA in the extremities were included. Most lesions demonstrated low to intermediate signal intensity on T1-weighted images and hyperintense signals on T2-weighted ones. They also showed similar traits pathologically. Initially, 107 radiomic features were acquired and then three were finally selected. The support vector machine (SVM) model was able to differentiate the two anomalies from each other with an area under the curve (AUC) of 0.807 (95%CI 0.602-1.000) and 0.846 (95%CI 0.659-1.000) in training and testing cohort, respectively.The derived radiomic features were helpful in differentiating KHE from FAVA. A model which contained these features might further improve the performance and hopefully could serve as a potential tool for identification.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
yaya应助zoey采纳,获得20
刚刚
隐形曼青应助伶俐骁采纳,获得10
刚刚
刚刚
1秒前
1秒前
1秒前
福祸相依发布了新的文献求助10
1秒前
潇洒依白发布了新的文献求助10
2秒前
墨西哥猪肉卷完成签到,获得积分10
2秒前
易研学术完成签到,获得积分10
2秒前
旺旺大礼包完成签到,获得积分10
3秒前
wyg512发布了新的文献求助10
3秒前
3秒前
3秒前
cwy完成签到,获得积分10
3秒前
科研通AI6应助傲娇的汉堡采纳,获得30
3秒前
3秒前
面包小狗完成签到,获得积分10
4秒前
微风发布了新的文献求助10
4秒前
4秒前
想个名字完成签到,获得积分10
5秒前
小树发布了新的文献求助30
5秒前
bioglia完成签到,获得积分10
5秒前
catherine完成签到,获得积分10
5秒前
赘婿应助YY采纳,获得10
5秒前
五虎完成签到,获得积分10
5秒前
6秒前
6秒前
6秒前
搜集达人应助正直的煎蛋采纳,获得10
7秒前
7秒前
Cyyyy完成签到,获得积分10
7秒前
7秒前
面包小狗发布了新的文献求助10
7秒前
留胡子的书白完成签到,获得积分10
7秒前
VV发布了新的文献求助10
7秒前
8秒前
元白发布了新的文献求助10
8秒前
8秒前
iwsaml完成签到 ,获得积分10
8秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
List of 1,091 Public Pension Profiles by Region 1561
Current Trends in Drug Discovery, Development and Delivery (CTD4-2022) 800
Foregrounding Marking Shift in Sundanese Written Narrative Segments 600
Holistic Discourse Analysis 600
Beyond the sentence: discourse and sentential form / edited by Jessica R. Wirth 600
Science of Synthesis: Houben–Weyl Methods of Molecular Transformations 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5524025
求助须知:如何正确求助?哪些是违规求助? 4614655
关于积分的说明 14543905
捐赠科研通 4552420
什么是DOI,文献DOI怎么找? 2494845
邀请新用户注册赠送积分活动 1475559
关于科研通互助平台的介绍 1447219