Prediction of Carotid In-Stent Restenosis by Computed Tomography Angiography Carotid Plaque-Based Radiomics.
Xiaoqing ChengZheng DongJia LiuHongxia LiChangsheng ZhouFandong ZhangChuran WangZhiqiang ZhangGuangming LuPublished in: Journal of clinical medicine (2022)
In-stent restenosis (ISR) after carotid artery stenting (CAS) critically influences long-term CAS benefits and safety. The study was aimed at screening preoperative ISR-predictive features and developing predictive models. Thus, we retrospectively analyzed clinical and imaging data of 221 patients who underwent pre-CAS carotid computed tomography angiography (CTA) and whose digital subtraction angiography data for verifying ISR presence were available. Carotid plaque characteristics determined using CTA were used to build a traditional model. Backward elimination (likelihood ratio) was used for the radiomics model. Furthermore, a combined model was built using the traditional and radiomics features. Five-fold cross-validation was used to evaluate the accuracy of the trained classifier and stability of the selected features. Follow-up angiography showed ISR in 30 patients. Carotid plaque length and thickness were independently associated with ISR (multivariate analysis); regarding the conventional model, the area under the curve (AUC) was 0.84 and 0.82 in the training and validation cohorts, respectively. The corresponding AUC values for the radiomics-based model were 0.87 and 0.82, and those for the optimal combined model were 0.88 and 0.83. Plaque length and thickness could independently predict post-CAS ISR, and the combination of radiomics and plaque features afforded the best predictive performance.
Keyphrases
- end stage renal disease
- crispr cas
- coronary artery disease
- optical coherence tomography
- lymph node metastasis
- ejection fraction
- newly diagnosed
- coronary artery
- chronic kidney disease
- computed tomography
- prognostic factors
- squamous cell carcinoma
- peritoneal dialysis
- magnetic resonance imaging
- electronic health record
- big data
- mass spectrometry
- artificial intelligence
- data analysis
- acute coronary syndrome