A risk-scoring model based on endobronchial ultrasound multimodal imaging for predicting metastatic lymph nodes in lung cancer patients

Zhihong Huang, Lei Wang, Junxiang Chen, Xinxin Zhi, Jiayuan Sun
{"title":"A risk-scoring model based on endobronchial ultrasound multimodal imaging for predicting metastatic lymph nodes in lung cancer patients","authors":"Zhihong Huang, Lei Wang, Junxiang Chen, Xinxin Zhi, Jiayuan Sun","doi":"10.1097/eus.0000000000000051","DOIUrl":null,"url":null,"abstract":"\n \n \n Endobronchial ultrasound (EBUS) imaging is a valuable tool for predicting lymph node (LN) metastasis in lung cancer patients. This study aimed to develop a risk-scoring model based on EBUS multimodal imaging (grayscale, Doppler mode, elastography) to predict LN metastasis in lung cancer patients.\n \n \n \n This retrospective study analyzed 350 metastatic LNs in 314 patients with lung cancer and 124 reactive LNs in 96 patients with nonspecific inflammation. The sonographic findings were compared with the final pathology results and clinical follow-up. Univariate and multivariate logistic regression analyses were performed to evaluate the independent risk factors of metastatic LNs. According to the β coefficients of corresponding indicators in logistic regression analysis, a risk-scoring model was established. Receiver operating characteristic curve was applied to evaluate the predictive capability of model.\n \n \n \n Multivariate analysis showed that short axis >10 mm, distinct margin, absence of central hilar structure, presence of necrosis, nonhilar vascularity, and elastography score 4 to 5 were independent predictors of metastatic LNs. Both short axis and margin were scored 1 point, and the rest of independent predictors were scored 2 points. The combination of 3 EBUS modes had the highest area under the receiver operating characteristic and accuracy of 0.884 (95% confidence interval, 0.846–0.922) and 87.55%, respectively. The risk stratification was as follows: 0 to 2 points, malignancy rate of 11.11%, low suspicion; 3 to 10 points, malignancy rate of 86.77%, high suspicion.\n \n \n \n The risk-scoring model based on EBUS multimodal imaging can effectively evaluate metastatic LNs in lung cancer patients to support clinical decision making.\n","PeriodicalId":503969,"journal":{"name":"Endoscopic Ultrasound","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Endoscopic Ultrasound","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1097/eus.0000000000000051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Endobronchial ultrasound (EBUS) imaging is a valuable tool for predicting lymph node (LN) metastasis in lung cancer patients. This study aimed to develop a risk-scoring model based on EBUS multimodal imaging (grayscale, Doppler mode, elastography) to predict LN metastasis in lung cancer patients. This retrospective study analyzed 350 metastatic LNs in 314 patients with lung cancer and 124 reactive LNs in 96 patients with nonspecific inflammation. The sonographic findings were compared with the final pathology results and clinical follow-up. Univariate and multivariate logistic regression analyses were performed to evaluate the independent risk factors of metastatic LNs. According to the β coefficients of corresponding indicators in logistic regression analysis, a risk-scoring model was established. Receiver operating characteristic curve was applied to evaluate the predictive capability of model. Multivariate analysis showed that short axis >10 mm, distinct margin, absence of central hilar structure, presence of necrosis, nonhilar vascularity, and elastography score 4 to 5 were independent predictors of metastatic LNs. Both short axis and margin were scored 1 point, and the rest of independent predictors were scored 2 points. The combination of 3 EBUS modes had the highest area under the receiver operating characteristic and accuracy of 0.884 (95% confidence interval, 0.846–0.922) and 87.55%, respectively. The risk stratification was as follows: 0 to 2 points, malignancy rate of 11.11%, low suspicion; 3 to 10 points, malignancy rate of 86.77%, high suspicion. The risk-scoring model based on EBUS multimodal imaging can effectively evaluate metastatic LNs in lung cancer patients to support clinical decision making.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于支气管内超声多模态成像预测肺癌患者转移性淋巴结的风险评分模型
支气管内超声(EBUS)成像是预测肺癌患者淋巴结(LN)转移的重要工具。本研究旨在开发一种基于 EBUS 多模态成像(灰度、多普勒模式、弹性成像)的风险评分模型,以预测肺癌患者的淋巴结转移。 这项回顾性研究分析了 314 名肺癌患者的 350 个转移性 LN 和 96 名非特异性炎症患者的 124 个反应性 LN。声像图结果与最终病理结果和临床随访结果进行了比较。进行了单变量和多变量逻辑回归分析,以评估转移性 LN 的独立风险因素。根据逻辑回归分析中相应指标的β系数,建立了风险评分模型。应用接收者操作特征曲线评价模型的预测能力。 多变量分析表明,短轴>10毫米、边缘明显、无中央肝门结构、存在坏死、无肝门血管、弹性成像评分4至5分是转移性LN的独立预测指标。短轴和边缘均得 1 分,其余独立预测因子得 2 分。三种 EBUS 模式的组合具有最高的接收者操作特征下面积和准确率,分别为 0.884(95% 置信区间,0.846-0.922)和 87.55%。风险分层如下0 至 2 分,恶性率为 11.11%,低度怀疑;3 至 10 分,恶性率为 86.77%,高度怀疑。 基于 EBUS 多模态成像的风险评分模型可有效评估肺癌患者的转移性 LN,为临床决策提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
First case report of pancreatic angiomyolipoma diagnosed by EUS-guided fine-needle biopsy EUS-guided hepaticojejunostomy for biliary obstruction in near-total gastrectomy with Roux-en-Y: Trials and tribulations (with videos) EUS-assisted preoperative diagnosis of immunoglobulin G4–related cholecystitis mimicking gallbladder cancer in a Mirizzi syndrome case EUS-guided pancreaticojejunostomy under gel immersion for pancreaticojejunal anastomotic stricture (with video) EUS-assisted preoperative diagnosis of immunoglobulin G4–related cholecystitis mimicking gallbladder cancer in a Mirizzi syndrome case
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1