Radiomics-based intracranial thrombus features on preoperative noncontrast CT predicts successful recanalization of mechanical thrombectomy in acute ischemic stroke

Abstract

Background: To evaluate the predictive value of radiomics features extracted from the thrombus on preoperative computed tomography images to identify successful recanalization after stent retrieve (SR) treatment in patients with acute ischemic stroke (AIS). Methods: Two hundred fifty-six patients newly diagnosed AIS between March 2017 and September 2020 from two institutes, including the first affiliated hospital of Soochow university (institute I) and Northern Jiangsu People's hospital (institute II), were enrolled continuously and retrospectively. Patients with unsatisfactory image quality were excluded. The remaining patients of institute I were randomly divided into the training and internal validation cohorts at a ratio of 7 to 3, and patients of institute II were collected as the external validation cohort. After extraction and selection of the optimal radiomics features from training cohort, six machine learning (ML) classifiers including naïve Bayes (NB), random forest (RF), logistic regression (LR), linear support vector machine (L.SVM), radial SVM (R.SVM), and an artificial neural network (ANN) were developed to predict successful recanalization with SR treatment and compared. A combined model based on the optimal ML classifier was constructed using the optimal radiomics model and clinical-radiological risk variables. Finally, the performance of the model was selected based on the Matthews correlation coefficient (MCC) and the area under the receiver operating (AUC) and independently evaluated on the internal validation and external validation cohorts. Results: We automatically extracted 1,130 radiomics features from the voxel of interest (VOI) using PyRadiomics. The eight most relevant radiomics features were identified using Intraclass coefficient, single-factor logistic regression analysis, and least absolute shrinkage and selection operator algorithm in the training cohort. Among the six ML classifiers, the ANN classifier using thrombus radiomics features achieved the best prediction of early recanalization under SR with MCCs of 0.913, 0.693 and 0.505 in training, internal and external validation cohorts, respectively. Moreover, receiver operating characteristic curves showed that the combined model [AUC =0.860, 95% confidence interval (CI): 0.731-0.936; AUC =0.849, 95% CI: 0.759-0.831] was not significantly better than radiomics model based on the ANN classifier alone (AUC =0.873, 95% CI: 0.803-0.891; AUC =0.805, 95% CI: 0.864-0.971) (P>0.05, De-Long Test) in internal and external validation cohorts. Conclusions: A radiomics model based on the ANN classifier has the ability to predict successfulThe study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was approved by the institutional review board of Northern Jiangsu People's hospital (No. 2022ky058), and individual consent for this retrospective analysis was waived. 256 patients newly diagnosed AIS between March 2017 and recanalization after SR in patients with AIS, thus allowing a potentially better s...

Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis

“…Notably, 11 of these studies performed a comparation between models using only clinical features and models incorporating radiomics features. While 4 of these studies ( 41 – 43 , 46 ) did not observe a significant difference in predictive performance, the remaining 7 ( 13 , 38 , 39 , 44 , 47 – 49 ) demonstrated that the inclusion of radiomics significantly improved the predictive ability for AIS prognosis. Moreover, among these 7 studies, 3 ( 38 , 39 , 44 ) directly compared the performance of the radiomics model with the clinical model, and all found that the radiomics model was superior.…”

“…Within these studies, ten are dedicated to predicting long-term prognosis, five focus on reperfusion ( 37 , 40 , 41 , 43 , 51 ), and one addresses both aspects ( 42 ). For long-term prognosis, assessment of the mRS spanned a follow-up period of 3–12 months, while assessments of the NIHSS occurred at discharge or on day 7.…”

Textural Analysis of the Hyperdense Artery Sign in Patients with Acute Ischemic Stroke Predicts the Outcome of Thrombectomy

“…al. [ 13 ] on the radiomics features of thrombi in AIS patients proves that some features have a significant predictive power in the outcome of thrombectomy procedures. Out of all the parameters determined in their study, we identify ShortRunLowGrayLevelEmphasis ( p = 0.0016, AUC = 0.813, Sensitivity 81.8%, Specificity 83.3%) and LargeAreaHighGrayLevelEmphasis ( p = 0.037, AUC = 0.737, Sensitivity 50%, Specificity 99.4%) as predictors for the outcome of mechanical thrombectomy procedures.…”

“…Additionally, radiomics can be harnessed for predictive modelling, where the extracted features are correlated with clinical outcomes. This enhances the prognostic stratification and helps tailor patient-specific interventions, thereby optimizing stroke management [ 13 ].…”

Application of radiomics in ischemic stroke

“… 37 Many investigations have highlighted the potential of combining radiomic and clinical data features into clinical prediction models, which may allow the precise prediction of patients who are likely to have favorable reperfusion outcomes. 38 – 40 …”