Objectives Our work aimed to assess the clinical value of dual-energy computed tomography (DECT) parameters in predicting in-stent restenosis (ISR) after carotid artery stenting (CAS) and to develop a nomogram model incorporating these parameters to enhance the accuracy of ISR risk prediction. Materials and methods Our retrospective multicenter research enrolled 205 patients who underwent CAS from January 2018 to April 2023, with DECT scans performed prior to the procedure. Two radiologists independently measured the DECT parameters and evaluated the characteristics of carotid plaques. Univariate and multivariate analyses were conducted to identify independent predictors of ISR. Three models were developed: clinical model, DECT model, and nomogram model. These models were assessed based on the area under the curve (AUC) and calibration, with their clinical value assessed utilizing decision curve analysis (DCA). Results Among 205 patients, 35 in the training set and 15 in the validation set experienced ISR. Multivariate analysis identified plaque length, fat fraction, normalized iodine concentration, and effective atomic number as independent predictors of ISR. Nomogram model, combining clinical and DECT parameters, demonstrated high accuracy in predicting ISR, with 0.931 (95% CI: 0.878–0.966) AUC in the training set and 0.872 (95% CI: 0.754–0.947) in the validation set, exceeding both clinical (AUC: 0.797, 0.673) and DECT models (AUC: 0.880, 0.853). Calibration curve and DCA exhibited the nomogram’s excellent performance and clinical utility. Conclusion The nomogram model integrating DECT parameters and clinical predictors provides a reliable and noninvasive tool for predicting ISR risk following CAS. It facilitates the formulation of individualized treatment strategies and offers important references for early intervention. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Preoperative prediction of in-stent restenosis (ISR) risk after carotid artery stenting is critical for optimizing revascularization strategies, yet current assessment methods remain limited and subjective. <Emphasis Type="BoldItalic">Findings</Emphasis> The nomogram incorporating plaque length, fat fraction, normalized iodine, and effective atomic number for predicting ISR risk achieved AUCs of 0.931 and 0.872 in the training and validation sets. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> This nomogram can serve as an effective tool for predicting ISR risk to guide personalized treatment, potentially reducing ISR-related strokes and improving carotid stenting outcomes. Graphical Abstract

Predicting carotid in-stent restenosis with dual-energy CT: a multicenter study

期刊论文
发表日期：

2025年11月6日

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Weiming Hu,
Guihan Lin,
Weiyue Chen,
Ting Zhao,
Zhuohang Shi,
Cheng Ma,
Zhanning Hu,
Lei Xu,
Xusheng Qian,
Zhihan Yan,
Shuiwei Xia,
Chenying Lu,
Minjiang Chen,
Weiqian Chen,
Jiansong Ji

摘要

Objectives

Our work aimed to assess the clinical value of dual-energy computed tomography (DECT) parameters in predicting in-stent restenosis (ISR) after carotid artery stenting (CAS) and to develop a nomogram model incorporating these parameters to enhance the accuracy of ISR risk prediction.

Materials and methods

Our retrospective multicenter research enrolled 205 patients who underwent CAS from January 2018 to April 2023, with DECT scans performed prior to the procedure. Two radiologists independently measured the DECT parameters and evaluated the characteristics of carotid plaques. Univariate and multivariate analyses were conducted to identify independent predictors of ISR. Three models were developed: clinical model, DECT model, and nomogram model. These models were assessed based on the area under the curve (AUC) and calibration, with their clinical value assessed utilizing decision curve analysis (DCA).

Results

Among 205 patients, 35 in the training set and 15 in the validation set experienced ISR. Multivariate analysis identified plaque length, fat fraction, normalized iodine concentration, and effective atomic number as independent predictors of ISR. Nomogram model, combining clinical and DECT parameters, demonstrated high accuracy in predicting ISR, with 0.931 (95% CI: 0.878–0.966) AUC in the training set and 0.872 (95% CI: 0.754–0.947) in the validation set, exceeding both clinical (AUC: 0.797, 0.673) and DECT models (AUC: 0.880, 0.853). Calibration curve and DCA exhibited the nomogram’s excellent performance and clinical utility.

Conclusion

The nomogram model integrating DECT parameters and clinical predictors provides a reliable and noninvasive tool for predicting ISR risk following CAS. It facilitates the formulation of individualized treatment strategies and offers important references for early intervention.

Key Points

Question Preoperative prediction of in-stent restenosis (ISR) risk after carotid artery stenting is critical for optimizing revascularization strategies, yet current assessment methods remain limited and subjective.

Findings The nomogram incorporating plaque length, fat fraction, normalized iodine, and effective atomic number for predicting ISR risk achieved AUCs of 0.931 and 0.872 in the training and validation sets.

Clinical relevance This nomogram can serve as an effective tool for predicting ISR risk to guide personalized treatment, potentially reducing ISR-related strokes and improving carotid stenting outcomes.

Graphical Abstract