A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion

Abstract

Background: We aimed to construct and validate a nomogram model based on the combination of radiomic features and satellite sign number for predicting intracerebral hematoma expansion.Methods: A total of 129 patients from two institutions were enrolled in this study. The preprocessed initial CT images were used for radiomic feature extraction. The ANOVA-Kruskal-Wallis test and least absolute shrinkage and selection operator regression were applied to identify candidate radiomic features and construct the Radscore. A nomogram model was developed by integrating the Radscore with a satellite sign number. The discrimination performance of the proposed model was evaluated by receiver operating characteristic (ROC) analysis, and the predictive accuracy was assessed via a calibration curve. Decision curve analysis (DCA) and Kaplan-Meier (KM) survival analysis were performed to evaluate the clinical value of the model.Results: Four optimal features were ultimately selected and contributed to the Radscore construction. A positive correlation was observed between the satellite sign number and Radscore (Pearson's r: 0.451). The nomogram model showed the best performance with high area under the curves in both training cohort (0.881, sensitivity: 0.973; specificity: 0.787) and external validation cohort (0.857, sensitivity: 0.950; specificity: 0.766). The calibration curve, DCA, and KM analysis indicated the high accuracy and clinical usefulness of the nomogram model for hematoma expansion prediction. Conclusion:A nomogram model of integrated radiomic signature and satellite sign number based on noncontrast CT images could serve as a reliable and convenient measurement of hematoma expansion prediction.

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

“…This method allows us to combine radiomics features into a radiomics signature [31][32][33]. Multi-factor analysis that incorporates individual factors into a factor panel has been widely used in recent studies [34][35][36]. For example, Wang et al [34] constructed an MRI-based radiomics model to predict the muscle-invasive status of bladder cancer and confirmed that the radiomics could be an efficient tool for preoperative prediction.…”

“…For example, Wang et al [34] constructed an MRI-based radiomics model to predict the muscle-invasive status of bladder cancer and confirmed that the radiomics could be an efficient tool for preoperative prediction. Similarly, Xu et al [35] developed a radiomics nomogram to predict intracerebral hematoma expansion and found that the nomogram could serve as a convenient measurement. Pan et al [36] used the LASSO logistic method to identify optimal radiomics features for preoperative classification of ovarian cystadenoma.…”

Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH‐2 trial intracerebral hemorrhage population

“…In this study, we devised and validated hematoma radiomics signa- Radiomics analysis has been widely applied in oncologic imaging for molecular subtyping, survival prognostication, and prediction of treatment response [8,[21][22][23]. Recent studies suggested that hematoma radiomics features can predict the likelihood of hematoma expansion [24][25][26][27]. In this study, we showed that while hematoma volume and radiomic shape features had strong association with severity of baseline clinical presentation and b R. R. Wilcox percentile bootstrap method for comparing dependent robust correlations [28].…”

Predicting intraventricular hemorrhage growth with a machine learning-based, radiomics-clinical model

“…It is widely used for evaluating tumor prognosis, selecting appropriate treatment, and predicting lymph node metastasis [ 7 , 8 ]. Although some researchers have predicted parenchymal hemorrhage enlargement with radiomics technology [ 9 – 11 ], few have tried to predict IVH growth. In this study, we aimed to develop a model that incorporates clinical and radiomics features to identify patients at high risk for IVH growth in the acute phase of ICH.…”