A nomogram incorporating clinical, conventional ultrasound and shear wave elastography findings for distinguishing pleomorphic adenoma from Warthin's tumor of the major salivary glands.

IF 2.9 2区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE Dento maxillo facial radiology Pub Date : 2023-10-01 Epub Date: 2023-06-22 DOI:10.1259/dmfr.20230051
Huan-Zhong Su, Jia-Jia Yang, Zhi-Yong Li, Long-Cheng Hong, Wen-Jin Lin, Cong Chen, Jie Guo, Zhen-Yan Fang, En-Sheng Xue
{"title":"A nomogram incorporating clinical, conventional ultrasound and shear wave elastography findings for distinguishing pleomorphic adenoma from Warthin's tumor of the major salivary glands.","authors":"Huan-Zhong Su, Jia-Jia Yang, Zhi-Yong Li, Long-Cheng Hong, Wen-Jin Lin, Cong Chen, Jie Guo, Zhen-Yan Fang, En-Sheng Xue","doi":"10.1259/dmfr.20230051","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Pre-operative differentiation between pleomorphic adenoma (PA) and Warthin's tumor (WT) of the major salivary glands is crucial for treatment decisions. The purpose of this study was to develop and validate a nomogram incorporating clinical, conventional ultrasound (CUS) and shear wave elastography (SWE) features to differentiate PA from WT.</p><p><strong>Methods: </strong>A total of 113 patients with histological diagnosis of PA or WT of the major salivary glands treated at Fujian Medical University Union Hospital were enrolled in training cohort (<i>n</i> = 75; PA = 41, WT = 34) and validation cohort (<i>n</i> = 38; PA = 22, WT = 16). The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening the most optimal clinical, CUS, and SWE features. Different models, including the nomogram model, clinic-CUS (Clin+CUS) and SWE model, were built using logistic regression. The performance levels of the models were evaluated and validated on the training and validation cohorts, and then compared among the three models.</p><p><strong>Results: </strong>The nomogram incorporating the clinical, CUS and SWE features showed favorable predictive value for differentiating PA from WT, with the area under the curves (AUCs) of 0.947 and 0.903 for the training cohort and validation cohort, respectively. Decision curve analysis showed that the nomogram model outperformed the Clin+CUS model and SWE model in terms of clinical usefulness.</p><p><strong>Conclusions: </strong>The nomogram had good performance in distinguishing major salivary PA from WT and held potential for optimizing the clinical decision-making process.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"20230051"},"PeriodicalIF":2.9000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552128/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Dento maxillo facial radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1259/dmfr.20230051","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/6/22 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
引用次数: 0

Abstract

Objective: Pre-operative differentiation between pleomorphic adenoma (PA) and Warthin's tumor (WT) of the major salivary glands is crucial for treatment decisions. The purpose of this study was to develop and validate a nomogram incorporating clinical, conventional ultrasound (CUS) and shear wave elastography (SWE) features to differentiate PA from WT.

Methods: A total of 113 patients with histological diagnosis of PA or WT of the major salivary glands treated at Fujian Medical University Union Hospital were enrolled in training cohort (n = 75; PA = 41, WT = 34) and validation cohort (n = 38; PA = 22, WT = 16). The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening the most optimal clinical, CUS, and SWE features. Different models, including the nomogram model, clinic-CUS (Clin+CUS) and SWE model, were built using logistic regression. The performance levels of the models were evaluated and validated on the training and validation cohorts, and then compared among the three models.

Results: The nomogram incorporating the clinical, CUS and SWE features showed favorable predictive value for differentiating PA from WT, with the area under the curves (AUCs) of 0.947 and 0.903 for the training cohort and validation cohort, respectively. Decision curve analysis showed that the nomogram model outperformed the Clin+CUS model and SWE model in terms of clinical usefulness.

Conclusions: The nomogram had good performance in distinguishing major salivary PA from WT and held potential for optimizing the clinical decision-making process.

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
结合临床、常规超声和剪切波弹性成像结果的列线图,用于区分大唾液腺多形性腺瘤和Warthin肿瘤。
目的:大涎腺多形性腺瘤(PA)和Warthin肿瘤(WT)的术前鉴别对于治疗决策至关重要。本研究的目的是开发和验证一种结合临床,常规超声(CUS)和剪切波弹性成像(SWE)特征来区分PA和WT。方法:在福建医科大学协和医院接受治疗的113名主要唾液腺组织学诊断为PA或WT的患者被纳入训练队列(n=75;PA=41,WT=34)和验证队列(n=38;PA=22,WT=16)。使用最小绝对收缩和选择算子(LASSO)回归算法来筛选最优化的临床、CUS和SWE特征。采用逻辑回归建立了不同的模型,包括列线图模型、临床CUS(Clin+CUS)和SWE模型。在训练和验证队列中评估和验证模型的性能水平,然后在三个模型之间进行比较。结果:结合临床、CUS和SWE特征的列线图显示出区分PA和WT的良好预测价值,训练队列和验证队列的曲线下面积(AUCs)分别为0.947和0.903。决策曲线分析表明,诺模图模型在临床实用性方面优于Clin+CUS模型和SWE模型。结论:该列线图在区分大唾液PA和WT方面具有良好的性能,并具有优化临床决策过程的潜力。
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
CiteScore
5.60
自引率
9.10%
发文量
65
审稿时长
4-8 weeks
期刊介绍: Dentomaxillofacial Radiology (DMFR) is the journal of the International Association of Dentomaxillofacial Radiology (IADMFR) and covers the closely related fields of oral radiology and head and neck imaging. Established in 1972, DMFR is a key resource keeping dentists, radiologists and clinicians and scientists with an interest in Head and Neck imaging abreast of important research and developments in oral and maxillofacial radiology. The DMFR editorial board features a panel of international experts including Editor-in-Chief Professor Ralf Schulze. Our editorial board provide their expertise and guidance in shaping the content and direction of the journal. Quick Facts: - 2015 Impact Factor - 1.919 - Receipt to first decision - average of 3 weeks - Acceptance to online publication - average of 3 weeks - Open access option - ISSN: 0250-832X - eISSN: 1476-542X
期刊最新文献
Can temporomandibular joint osteoarthritis be diagnosed on MRI proton density-weighted images with diagnostic support from the latest deep learning classification models? Development and validation of a CT-based deep learning radiomics signature to predict lymph node metastasis in oropharyngeal squamous cell carcinoma: a multicentre study. In vitro early proximal caries detection using trilateral short-wave infrared reflection at 1050 and 1550 nm. Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review. Evaluation of temporomandibular joint disc displacement with MRI-based radiomics analysis.
×
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