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Risk estimation of intracranial aneurysm rupture is critical in determining treatment strategy. There is a scarcity of multicenter studies on the predictive power of clinical-radiomics models for aneurysm rupture. This study aims to develop a clinical-radiomics model and explore its additional value in the discrimination of aneurysm rupture. Methods: A total of 516 aneurysms, including 273 (52.9%) with ruptured aneurysms, were retrospectively enrolled from four hospitals between January 2019 and August 2020. Relevant clinical features were collected, and radiomic characteristics associated with aneurysm were extracted. Subsequently, three models, including a clinical model, a radiomics model, and a clinical-radiomics model were constructed using multivariate logistic regression analysis to effectively classify aneurysm rupture. The performance of models was analyzed through operating characteristic curves, decision curve, and calibration curves analysis. Different models' comparison used DeLong tests. To offer an understandable and intuitive scoring system for assessing rupture risk, we developed a comprehensive nomogram based on the developed model. Results: Three clinical risk factors and fourteen radiomics features were explored to establish three models. The area under the receiver operating curve (AUC) for the radiomics model was 0.775 (95% CI,0.719– 0.830), 0.752 (95% CI,0.663– 0.841), 0.747 (95% CI,0.658– 0.835) in the training, internal and external test datasets, respectively. The AUC for clinical model was 0.802 (95% CI, 0.749– 0.854), 0.736 (95% CI, 0.644– 0.828), 0.789 (95% CI, 0.709– 0.870) in these three sets, respectively. The clinical-radiomics model showed an AUC of 0.880 (95% CI,0.840– 0.920), 0.807 (95% CI,0.728– 0.887), 0.815 (95% CI,0.740– 0.891) in three datasets respectively. Compared with the radiomics and clinical models, the clinical-radiomics model demonstrated better diagnostic performance (DeLong' test P < 0.05). Conclusion: The clinical-radiomics model represents a promising approach for predicting rupture of intracranial aneurysms. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop and verify a magnetic resonance imaging (MRI)-based radiomics model for predicting the microvascular invasion grade (MVI) before surgery in individuals diagnosed with nodular hepatocellular carcinoma (HCC). Methods: A total of 198 patients were included in the study and were randomly stratified into two groups: a training group consisting of 139 patients and a test group comprising 59 patients. The tumor lesion was manually segmented on the largest cross-sectional slice using ITK SNAP, with agreement reached between two radiologists. The selection of radiomics features was carried out using the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm. Radiomics models were then developed through maximum correlation, minimum redundancy, and logistic regression analyses. The performance of the models in predicting MVI grade was assessed using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. Results: There were no notable statistical differences in sex, age, BMI (body mass index), tumor size, and location between the training and test groups. The AP and PP radiomic model constructed for predicting MVI grade demonstrated an AUC of 0.83 (0.75– 0.88) and 0.73 (0.64– 0.80) in the training group and an AUC of 0.74 (0.61– 0.85) and 0.62 (0.48– 0.74) in test group, respectively. The combined model consists of imaging data and clinical data (age and AFP), achieved an AUC of 0.85 (0.78– 0.91) and 0.77 (0.64– 0.87) in the training and test groups, respectively. Conclusion: A radiomics model utilizing-contrast-enhanced MRI demonstrates strong predictive capability for differentiating MVI grades in individuals with nodular HCC. This model could potentially function as a dependable and resilient tool to support hepatologists and radiologists in their preoperative decision-making processes. [ABSTRACT FROM AUTHOR]