基于超声诊断痛风性关节炎的临床放射组学图的开发和验证。

IF 2.4 3区医学 Q2 ACOUSTICS

Ultrasound in Medicine and Biology Pub Date : 2025-01-28 DOI:10.1016/j.ultrasmedbio.2024.12.009

Minghang Lin , Lei Yan , Mei He , Shuqiang Chen

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引用次数: 0

摘要

目的：建立并验证超声影像学特征与临床参数相结合的痛风性关节炎诊断模型。方法：共纳入604例疑似痛风性关节炎患者，按4:1的比例随机分为训练组（n = 483）和验证组（n = 121）。对临床数据进行单因素和多因素分析，以确定具有统计学意义的临床特征，以构建初始诊断模型。利用最小绝对收缩和选择算子（LASSO）回归分析在训练集中识别关键放射学特征，建立放射学模型。然后通过逻辑回归将临床（如c反应蛋白、红细胞沉降率和尿酸水平）和放射学特征结合起来，形成复合临床放射学图。通过受试者工作特征曲线、校准曲线和决策曲线分析，在验证集中评估临床模型、放射学模型和临床放射学nomogram的预测性能。结果：通过logistic回归综合影像学特征和临床特征的临床放射学nomogram在验证集中表现出较好的预测效果，曲线下面积（AUC）为0.936 (95% CI: 0.885-0.986)，优于临床(AUC = 0.924；95% CI: 0.873-0.976)和放射性模型(AUC = 0.828；95% CI: 0.738-0.918)。决策曲线分析进一步证实了该模型的临床应用，特别是在区分痛风性和非痛风性关节炎方面。结论：与独立的临床或放射学模型相比，基于超声的临床放射学模型对痛风性关节炎的预测准确性更高，为痛风性关节炎的早期诊断和治疗提供了一种新颖而有前景的方法。

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Development and Validation of an Ultrasound-Based Clinical Radiomics Nomogram for Diagnosing Gouty Arthritis

Objective

This study aimed to develop and validate a diagnostic model for gouty arthritis by integrating ultrasonographic radiomic features with clinical parameters.

Methods

A total of 604 patients suspected of having gouty arthritis were enrolled and randomly divided into a training set (n = 483) and a validation set (n = 121) in a 4:1 ratio. Univariate and multivariate analyses were conducted on the clinical data to identify statistically significant clinical features for constructing an initial diagnostic model. Key radiomic features were identified in the training set using least absolute shrinkage and selection operator (LASSO) regression analysis to establish a radiomic model. A composite clinicoradiomic nomogram was then developed by combining clinical (such as C-reactive protein, erythrocyte sedimentation rate and uric acid level) and radiomic features through logistic regression. The predictive performance of the clinical model, radiomic model and clinicoradiomic nomogram was evaluated in the validation set using receiver operating characteristic curves, calibration curves and decision curve analysis.

Results

The clinicoradiomic nomogram, which integrated imaging features and clinical characteristics via logistic regression, demonstrated superior predictive performance in the validation set, with an area under the curve (AUC) of 0.936 (95% CI: 0.885–0.986), surpassing both clinical (AUC = 0.924; 95% CI: 0.873–0.976) and radiomic models (AUC = 0.828; 95% CI: 0.738–0.918) alone. Decision curve analysis further confirmed the clinical utility of this model, particularly in differentiating between gouty and non-gouty arthritis.

Conclusion

Compared with standalone clinical or radiomic models, the ultrasonography-based clinicoradiomic model exhibited enhanced predictive accuracy for diagnosing gouty arthritis, presenting a novel and promising approach for the early diagnosis and management of gouty arthritis.

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来源期刊

Ultrasound in Medicine and Biology 医学-核医学

CiteScore

6.20

自引率

6.90%

发文量

325

审稿时长

70 days

期刊介绍： Ultrasound in Medicine and Biology is the official journal of the World Federation for Ultrasound in Medicine and Biology. The journal publishes original contributions that demonstrate a novel application of an existing ultrasound technology in clinical diagnostic, interventional and therapeutic applications, new and improved clinical techniques, the physics, engineering and technology of ultrasound in medicine and biology, and the interactions between ultrasound and biological systems, including bioeffects. Papers that simply utilize standard diagnostic ultrasound as a measuring tool will be considered out of scope. Extended critical reviews of subjects of contemporary interest in the field are also published, in addition to occasional editorial articles, clinical and technical notes, book reviews, letters to the editor and a calendar of forthcoming meetings. It is the aim of the journal fully to meet the information and publication requirements of the clinicians, scientists, engineers and other professionals who constitute the biomedical ultrasonic community.