{"title":"颈动脉支架植入术后颈动脉周围脂肪组织放射组学预测支架内再狭窄的增量价值。","authors":"Dongqing Ren, Yu Lan, Hongyi Li, Dongbo Li, Ronghui Ju, Yang Hou","doi":"10.1136/jnis-2025-023865","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the potential of pericarotid adipose tissue radiomics to improve the prediction of in-stent restenosis (ISR) after carotid artery stenting (CAS).</p><p><strong>Methods: </strong>This retrospective study included 191 patients who underwent carotid CT angiography (CTA) and CAS within 1 week at two centers from September 2019 to December 2023. ISR was defined as ≥50% stenosis on follow-up Doppler ultrasound or CTA. Three predictive models were developed and defined as follows: Model A (Clinical), Model B (Clinical + Imaging), and Model C (Clinical + Imaging + Radiomics) using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis.</p><p><strong>Results: </strong>ISR occurred in 44 patients with a mean time interval of 11.3 months. Multivariate Cox regression analyses identified diabetes, fibrinogen, systolic blood pressure, calcified plaque volume, and pericarotid adipose tissue radiomics as independent predictors of ISR. The radiomics score, derived from 15 significant characteristics, outperformed conventional imaging markers. In the training set, Model C (AUC=0.881) significantly outperformed Model A (AUC=0.664) and Model B (AUC=0.840), with statistically significant differences (Model A vs Model C: P=0.001; Model B vs Model C: P=0.0246). This trend was consistent in the validation sets. Calibration curves showed good agreement between predicted and actual ISR probabilities, and decision curve analysis indicated that Model C provided greater net benefits.</p><p><strong>Conclusion: </strong>The radiomic characteristics of pericarotid adipose tissue provide incremental value in predicting ISR after CAS and serve as a valuable biomarker for restenosis risk assessment.</p>","PeriodicalId":16411,"journal":{"name":"Journal of NeuroInterventional Surgery","volume":" ","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Incremental value of pericarotid adipose tissue radiomics in predicting in-stent restenosis after carotid artery stenting.\",\"authors\":\"Dongqing Ren, Yu Lan, Hongyi Li, Dongbo Li, Ronghui Ju, Yang Hou\",\"doi\":\"10.1136/jnis-2025-023865\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To evaluate the potential of pericarotid adipose tissue radiomics to improve the prediction of in-stent restenosis (ISR) after carotid artery stenting (CAS).</p><p><strong>Methods: </strong>This retrospective study included 191 patients who underwent carotid CT angiography (CTA) and CAS within 1 week at two centers from September 2019 to December 2023. ISR was defined as ≥50% stenosis on follow-up Doppler ultrasound or CTA. Three predictive models were developed and defined as follows: Model A (Clinical), Model B (Clinical + Imaging), and Model C (Clinical + Imaging + Radiomics) using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis.</p><p><strong>Results: </strong>ISR occurred in 44 patients with a mean time interval of 11.3 months. Multivariate Cox regression analyses identified diabetes, fibrinogen, systolic blood pressure, calcified plaque volume, and pericarotid adipose tissue radiomics as independent predictors of ISR. The radiomics score, derived from 15 significant characteristics, outperformed conventional imaging markers. In the training set, Model C (AUC=0.881) significantly outperformed Model A (AUC=0.664) and Model B (AUC=0.840), with statistically significant differences (Model A vs Model C: P=0.001; Model B vs Model C: P=0.0246). This trend was consistent in the validation sets. Calibration curves showed good agreement between predicted and actual ISR probabilities, and decision curve analysis indicated that Model C provided greater net benefits.</p><p><strong>Conclusion: </strong>The radiomic characteristics of pericarotid adipose tissue provide incremental value in predicting ISR after CAS and serve as a valuable biomarker for restenosis risk assessment.</p>\",\"PeriodicalId\":16411,\"journal\":{\"name\":\"Journal of NeuroInterventional Surgery\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of NeuroInterventional Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1136/jnis-2025-023865\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROIMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of NeuroInterventional Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/jnis-2025-023865","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROIMAGING","Score":null,"Total":0}
Incremental value of pericarotid adipose tissue radiomics in predicting in-stent restenosis after carotid artery stenting.
Objective: To evaluate the potential of pericarotid adipose tissue radiomics to improve the prediction of in-stent restenosis (ISR) after carotid artery stenting (CAS).
Methods: This retrospective study included 191 patients who underwent carotid CT angiography (CTA) and CAS within 1 week at two centers from September 2019 to December 2023. ISR was defined as ≥50% stenosis on follow-up Doppler ultrasound or CTA. Three predictive models were developed and defined as follows: Model A (Clinical), Model B (Clinical + Imaging), and Model C (Clinical + Imaging + Radiomics) using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis.
Results: ISR occurred in 44 patients with a mean time interval of 11.3 months. Multivariate Cox regression analyses identified diabetes, fibrinogen, systolic blood pressure, calcified plaque volume, and pericarotid adipose tissue radiomics as independent predictors of ISR. The radiomics score, derived from 15 significant characteristics, outperformed conventional imaging markers. In the training set, Model C (AUC=0.881) significantly outperformed Model A (AUC=0.664) and Model B (AUC=0.840), with statistically significant differences (Model A vs Model C: P=0.001; Model B vs Model C: P=0.0246). This trend was consistent in the validation sets. Calibration curves showed good agreement between predicted and actual ISR probabilities, and decision curve analysis indicated that Model C provided greater net benefits.
Conclusion: The radiomic characteristics of pericarotid adipose tissue provide incremental value in predicting ISR after CAS and serve as a valuable biomarker for restenosis risk assessment.
期刊介绍:
The Journal of NeuroInterventional Surgery (JNIS) is a leading peer review journal for scientific research and literature pertaining to the field of neurointerventional surgery. The journal launch follows growing professional interest in neurointerventional techniques for the treatment of a range of neurological and vascular problems including stroke, aneurysms, brain tumors, and spinal compression.The journal is owned by SNIS and is also the official journal of the Interventional Chapter of the Australian and New Zealand Society of Neuroradiology (ANZSNR), the Canadian Interventional Neuro Group, the Hong Kong Neurological Society (HKNS) and the Neuroradiological Society of Taiwan.