A nomogram based on ultrasound scoring system for differentiating between immunoglobulin G4-related sialadenitis and primary Sjögren syndrome.

IF 2.9 2区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

Dento maxillo facial radiology Pub Date : 2024-01-11 DOI:10.1093/dmfr/twad005

Huan-Zhong Su, Long-Cheng Hong, Mei Huang, Feng Zhang, Yu-Hui Wu, Zuo-Bing Zhang, Xiao-Dong Zhang

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引用次数: 0

Abstract

Objectives: Accurate distinguishing between immunoglobulin G4-related sialadenitis (IgG4-RS) and primary Sjögren syndrome (pSS) is crucial due to their different treatment approaches. This study aimed to construct and validate a nomogram based on the ultrasound (US) scoring system for the differentiation of IgG4-RS and pSS.

Methods: A total of 193 patients with a clinical diagnosis of IgG4-RS or pSS treated at our institution were enrolled in the training cohort (n = 135; IgG4-RS = 28, pSS = 107) and the validation cohort (n = 58; IgG4-RS = 15, pSS = 43). The least absolute shrinkage and selection operator regression algorithm was utilized to screen the most optimal clinical features and US scoring parameters. A model for the differential diagnosis of IgG4-RS or pSS was built using logistic regression and visualized as a nomogram. The performance levels of the nomogram model were evaluated and validated in both the training and validation cohorts.

Results: The nomogram incorporating clinical features and US scoring parameters showed better predictive value in differentiating IgG4-RS from pSS, with the area under the curves of 0.947 and 0.958 for the training cohort and the validation cohort, respectively. Decision curve analysis demonstrated that the nomogram was clinically useful.

Conclusions: A nomogram based on the US scoring system showed favourable predictive efficacy in differentiating IgG4-RS from pSS. It has the potential to aid in clinical decision-making.

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基于超声评分系统的提名图，用于区分免疫球蛋白 G4 相关性唾液腺炎和原发性斯约格伦综合征。

目的：由于免疫球蛋白 G4 相关性唾液腺炎（IgG4-RS）和原发性斯约格伦综合征（pSS）的治疗方法不同，因此准确区分这两种疾病至关重要。本研究旨在构建并验证基于超声（US）评分系统的提名图，以区分 IgG4-RS 和 pSS：在我院接受治疗的临床诊断为 IgG4-RS 或 pSS 的 193 名患者被纳入训练队列（n = 135；IgG4-RS = 28，pSS = 107）和验证队列（n = 58；IgG4-RS = 15，pSS = 43）。利用最小绝对收缩和选择算子回归算法筛选出最佳临床特征和 US 评分参数。利用逻辑回归法建立了 IgG4-RS 或 pSS 的鉴别诊断模型，并以提名图的形式显示出来。在训练组和验证组中对提名图模型的性能水平进行了评估和验证：结果：包含临床特征和 US 评分参数的提名图在区分 IgG4-RS 和 pSS 方面显示出更好的预测价值，训练队列和验证队列的曲线下面积分别为 0.947 和 0.958。决策曲线分析表明，提名图在临床上是有用的：结论：基于 US 评分系统的提名图在区分 IgG4-RS 和 pSS 方面显示出良好的预测效果。它具有帮助临床决策的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Dento maxillo facial radiology 医学-核医学

CiteScore

5.60

自引率

9.10%

发文量

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