CT-based Intra-thrombus and Peri-thrombus Radiomics for Prediction of Prognosis After Endovascular Thrombectomy: A Retrospective Study Across Two Centers.

BACKGROUND AND PURPOSE: Complications from endovascular thrombectomy (EVT) can negatively affect clinical outcomes, making the development of a more precise and objective prediction model essential. This research aimed to assess the effectiveness of radiomic features derived from pre-surgical CT scans in predicting the prognosis post- EVT in acute ischemic stroke patients. MATERIALS AND METHODS: This investigation included 336 acute ischemic stroke patients from two medical centers, spanning from March 2018 to March 2024. The participants were split into a training cohort of 161 patients and a validation cohort of 175 patients. Patient outcomes were rated with the mRS: 0-2 for good, 3-6 for poor. A total of 428 radiomic features were derived from intra-thrombus and peri-thrombus regions in non-contrast CT and CT angiography images. Feature selection was conducted using a least absolute shrinkage and selection operator regression model. The efficacy of eight different supervised learning models was assessed using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: Among all models tested in the validation cohort, the logistic regression algorithm for combined model achieved the highest AUC (0.87, with a 95% confidence interval of 0.81 to 0.92), outperforming other algorithms. The combined use of radiomic features from both the intra-thrombus and peri-thrombus regions significantly enhanced diagnostic accuracy over models using features from a single region (0.81 vs 0.70, 0.77), highlighting the benefit of integrating data from both regions for improved prediction. CONCLUSIONS: The findings suggest that a combined radiomics model based on CT imaging serves as a potent approach to assessing the prognosis following EVT. The logistic regression model, in particular, proved to be both effective and stable, offering critical insights for the management of stroke. ABBREVIATIONS: AUC=area under the curve; EVT=endovascular thrombectomy; KNN=k-nearest neighbors; LASSO=least absolute shrinkage and selection operator; LightGBM=Light Gradient Boosting Machine; LR=logistic regression; MLP=multi-layer perceptron; RF=random forest; SVM=support vector machine; XGBoost=extreme gradient boosting.

Radiomics of intrathrombus and perithrombus regions for Post-EVT intracranial hemorrhage risk Prediction: A multicenter CT study.

OBJECTIVES: This study aimed to assess the predictive performance of radiomics derived from computed tomography (CT) images of thrombus regions in predicting the risk of intracranial hemorrhage (ICH) following endovascular thrombectomy (EVT). MATERIALS AND METHODS: This retrospective multicenter study included 336 patients who underwent admission CT and EVT for acute anterior-circulation large vessel occlusion between December 2018 and December 2023. Follow-up imaging was performed 24 h post-procedure to evaluate the occurrence of ICH. 230 patients from centers A and B were randomly allocated into training and test groups in a 7:3 ratio, while the remaining 106 patients from center C comprised the validation cohort. Radiologists manually segmenting the thrombus on CT images, and the perithrombus region was defined by expanding the initial region of interest (ROI). A total of 428 radiomics features were extracted from both intrathrombus and perithrombus regions on CT images. The Mann-Whitney U test was used for feature selection, and least absolute shrinkage and selection operator (LASSO) regression was employed for model development, followed by validation using a 5-fold cross-validation approach. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC). RESULTS: Among the eligible patients, 128 (38.1 %) experienced ICH after EVT. The combined model exhibited superior performance in the training cohort (AUC: 0.913, 95 % CI: 0.861-0.965), test cohort (AUC: 0.868, 95 % CI: 0.775-0.962), and validation cohort (AUC: 0.850, 95 % CI: 0.768-0.912). Notably, in the validation group, both the perithrombus and combined models demonstrated higher predictive accuracy compared to the intrathrombus model (0.837 vs. 0.684, p = 0.02; AUC: 0.850 vs. 0.684, p = 0.01). CONCLUSIONS: Radiomics features derived from the perithrombus region significantly enhance the prediction of ICH after EVT, providing valuable insights for optimizing post-procedural clinical decisions. CLINICAL RELEVANCE STATEMENT: This study highlights the importance of radiomics extracted from intrathrombus and perithrombus region in predicting intracranial hemorrhagefollowing endovascular thrombectomy, which can aid in improving patient outcomes.

A dual-energy computed tomography-based radiomics nomogram for predicting time since stroke onset: a multicenter study.

OBJECTIVES: We aimed to develop and validate a radiomics nomogram based on dual-energy computed tomography (DECT) images and clinical features to classify the time since stroke (TSS), which could facilitate stroke decision-making. MATERIALS AND METHODS: This retrospective three-center study consecutively included 488 stroke patients who underwent DECT between August 2016 and August 2022. The eligible patients were divided into training, test, and validation cohorts according to the center. The patients were classified into two groups based on an estimated TSS threshold of ≤ 4.5 h. Virtual images optimized the visibility of early ischemic lesions with more CT attenuation. A total of 535 radiomics features were extracted from polyenergetic, iodine concentration, virtual monoenergetic, and non-contrast images reconstructed using DECT. Demographic factors were assessed to build a clinical model. A radiomics nomogram was a tool that the Rad score and clinical factors to classify the TSS using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) was used to compare the clinical utility and benefits of different models. RESULTS: Twelve features were used to build the radiomics model. The nomogram incorporating both clinical and radiomics features showed favorable predictive value for TSS. In the validation cohort, the nomogram showed a higher AUC than the radiomics-only and clinical-only models (AUC: 0.936 vs 0.905 vs 0.824). DCA demonstrated the clinical utility of the radiomics nomogram model. CONCLUSIONS: The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients. CLINICAL RELEVANCE STATEMENT: The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS. KEY POINTS: Accurately determining the TSS onset is crucial in deciding a treatment approach. The radiomics-clinical nomogram showed the best performance for predicting the TSS. Using the developed model to identify patients at different times since stroke can facilitate individualized management.

The Value of Dual-Energy Computed Tomography Angiography-Derived Parameters in the Evaluation of Clot Composition.

OBJECTIVES: We aimed to assess the value of dual-energy computed tomography angiography (DE-CTA) derived parameters as a quantitative biomarker of thrombus composition in acute ischemic stroke (AIS). METHODS: AIS patients who underwent DE-CTA before thrombectomy between August 2016 and September 2022 were included in this study. We assessed the relative proportion of red blood cells (RBCs) and the fibrin/platelet ratio (F/P) of the retrieved clots and categorized the clots as RBC-dominant (RBCs > F/P) or F/P-dominant (F/P > RBCs). The thrombus based parameters were measured on polyenergetic images (PEI), virtual monoenergetic (VM), virtual non-contrast (VNC), iodine concentration (IC), and effective atomic number (Zeff) images respectively, and the slope of the spectral Hounsfield unit curve (λHU) was calculated. These parameters were compared in the DE-CTA images of RBC- and F/P-dominant thrombi. The diagnostic performance of the parameters was analyzed using the ROC curve. Correlations between thrombus composition and DE-CTA-derived parameters were assessed. RESULTS: The retrieved clots in 54 of 88 patients (61.36%) were RBC-dominant. The RBC-dominant thrombi showed significantly higher VNC values and lower IC, λHU, and Zeff values than the F/P-dominant thrombi (p < 0.05). The CT density measured on IC images showed the largest AUC value (AUC, 0.94; sensitivity, 77.78%; specificity, 100.00%). The Spearman rank-order correlation coefficient values showed that CT density measured on IC images of the thrombus showed the strongest association with the proportion of RBCs (r = -0.64, p < 0.001) and F/P (r = 0.65, p < 0.001). CONCLUSIONS: DE-CTA-derived parameters, especially the CT density measured on IC images, could be associated with thrombus composition and allow for personalized thrombectomy strategies.

Value of T1 mapping on gadoxetic acid-enhanced MRI for microvascular invasion of hepatocellular carcinoma: a retrospective study.

BACKGROUND: To evaluate the utility of non-invasive parameters derived from T1 mapping and diffusion-weighted imaging (DWI) on gadoxetic acid-enhanced MRI for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHODS: A total of 94 patients with single HCC undergoing partial hepatectomy was analyzed in this retrospective study. Preoperative T1 mapping and DWI on gadoxetic acid-enhanced MRI was performed. The parameters including precontrast, postcontrast and reduction rate of T1 relaxation time and apparent diffusion coefficient (ADC) values were measured for differentiating MVI-positive HCCs (n = 38) from MVI-negative HCCs (n = 56). The receiver operating characteristic curve (ROC) was analyzed to compare the diagnostic performance of the calculated parameters. RESULTS: MVI-positive HCCs demonstrated a significantly lower reduction rate of T1 relaxation time than that of MVI-negative HCCs (39.4% vs 49.9, P < 0.001). The areas under receiver operating characteristic curve (AUC) were 0.587, 0.728, 0.824, 0,690 and 0.862 for the precontrast, postcontrast, reduction rate of T1 relaxation time, ADC and the combination of reduction rate and ADC, respectively. The cut-off value of the reduction rate and ADC calculated through maximal Youden index in ROC analyses was 44.9% and 1553.5 s/mm2. To achieve a better diagnostic performance, the criteria of combining the reduction rate lower than 44.9% and the ADC value lower than 1553.5 s/mm2 was proposed with a high specificity of 91.8% and accuracy of 80.9%. CONCLUSIONS: The proposed criteria of combining the reduction rate of T1 relaxation time lower than 44.9% and the ADC value lower than 1553.5 s/mm2 on gadoxetic acid-enhanced MRI holds promise for evaluating MVI status of HCC.

Significance of the ADC ratio in the differential diagnosis of breast lesions.

BACKGROUND: Magnetic resonance imaging (MRI) has high sensitivity but low specificity for breast cancer, and consequently, new techniques to improve the specificity of breast MRI in diagnosing breast cancer are under development. PURPOSE: To assess the ability of the apparent diffusion coefficient (ADC) compared with the ADC ratio (ADCr) to differentially diagnose benign compared with malignant breast lesions. MATERIAL AND METHODS: Forty-eight women with breast lesions (average age, 45 years) underwent MRI scanning including T1-weighted dynamic contrast-enhanced (DCE) scanning and diffusion-weighted imaging (DWI). The average ADC and ADCr values for both lesions and pectoralis major muscles (ADCrmuscle and ADCrmuscle) were measured in patients with malignant (n = 25) and benign (n = 23) breast lesions. The ADCr of the contralateral breast (ADCr contralateral) was also evaluated. All histology was confirmed by pathological analysis of biopsied tissue. ADC and ADCr values were analyzed using receiver-operating characteristic (ROC) curves. RESULTS: For benign lesions compared with malignant lesions, lesion-side ADC was 1.45 vs. 1.05, respectively (P < 0.001), normal-side ADC was 1.82 vs.1.64 (P = 0.002), ADCrmuscle was 1.35 vs. 0.9 (P < 0.001), and ADCrcontralateral was 0.79 vs. 0.64 (P = 0.001). ADCrmuscle showed higher sensitivity (82.61%) and specificity (96.00%) than ADCrcontralateral (60.87% and 92.00%, respectively) and ADC (69.57% and 96.00%) for discriminating malignant from benign lesions. The AUC using ADCrmuscle had higher discriminatory power (0.92, P < 0.001) for malignant versus benign breast lesions compared with either ADC (0.82, P < 0.001) or ADCrcontralateral (0.78, P = 0.001). CONCLUSION: The ADCrmuscle value showed higher sensitivity and specificity and improved diagnostic accuracy compared with either ADC or ADCrcontralateral in differentiating benign from malignant breast lesions.