PT  - JOURNAL ARTICLE
AU  - Zhai, Duchang
AU  - Wu, Yuanyuan
AU  - Cui, Manman
AU  - Liu, Yan
AU  - Zhou, Xiuzhi
AU  - Hu, Dongliang
AU  - Wang, Yuancheng
AU  - Ju, Shenghong
AU  - Fan, Guohua
AU  - Cai, Wu
TI  - Combinations of Clinical Factors, CT Signs, and Radiomics for Differentiating High-Density Areas after Mechanical Thrombectomy in Patients with Acute Ischemic Stroke
AID  - 10.3174/ajnr.A8434
DP  - 2025 Jan 01
TA  - American Journal of Neuroradiology
PG  - 66--74
VI  - 46
IP  - 1
4099  - http://www.ajnr.org/content/46/1/66.short
4100  - http://www.ajnr.org/content/46/1/66.full
SO  - Am. J. Neuroradiol.2025 Jan 01; 46
AB  - BACKGROUND AND PURPOSE: Clinically, hemorrhagic transformation (HT) after mechanical thrombectomy (MT) is a common complication. This study aimed to investigate the value of clinical factors, CT signs, and radiomics in the differential diagnosis of high-density areas (HDAs) in the brain after MT in patients with acute ischemic stroke with large-vessel occlusion (AIS-LVO).MATERIALS AND METHODS: A total of 156 eligible patients with AIS-LVO in Center I from December 2015 to June 2023 were retrospectively enrolled and randomly divided into training (n = 109) and internal validation (n = 47) sets at a ratio of 7:3. The data of 63 patients in Center II were collected as an external validation set. According to the diagnostic criteria, the patients in the 3 data sets were divided into an HT group and a non-HT group. The clinical and imaging data from Centers I and II were used to construct a clinical factor and CT-sign model, a radiomics model, and a combined model by logistic regression. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy of each model in the 3 data sets.RESULTS: Clinical blood glucose and the maximum cross-sectional area on CT were associated with the HT or non-HT of the HDA according to multivariate logistic regression analyses (P < .05). Among the 3 models, the combined model had the highest diagnostic efficiency, with area under the curve values of 0.895, 0.882, and 0.820 in the 3 data sets, which were significantly greater than the area under the curve values of the radiomics model (0.887, 0.898, 0.798) and clinical factor and CT-sign model (0.831, 0.744, 0.684).CONCLUSIONS: The combined model based on radiomics had the best performance, indicating that radiomics features can be used as imaging biomarkers to aid in the clinical judgment of the nature of HDA after MT.AIS-LVOacute ischemic stroke with large-vessel occlusionAreamaxmaximum cross-sectional areaAUCarea under the curveDCAdecision curve analysisD-DD-dimerDECTdual-energy CTDPTdoor-to-puncture timeHDAhigh-density areaHThemorrhagic transformationICEiodine contrast extravasationIOMiodine overlay mapLASSOleast absolute shrinkage and selection operatorLRlogistic regressionMTmechanical thrombectomyPLTplateletROCreceiver operating characteristicTGtotal cholesterol triglycerideVNCvirtual noncontrast