Dataset Information

Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke.

ABSTRACT:

Objective

To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes.

Materials and methods

Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses.

Results

Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness.

Conclusion

The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC9340229 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke.

Zhou Yiran Y Wu Di D Yan Su S Xie Yan Y Zhang Shun S Lv Wenzhi W Qin Yuanyuan Y Liu Yufei Y Liu Chengxia C Lu Jun J Li Jia J Zhu Hongquan H Liu Weiyin Vivian WV Liu Huan H Zhang Guiling G Zhu Wenzhen W

Korean journal of radiology 20220527 8

<h4>Objective</h4>To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes.<h4>Materials and methods</h4>Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (m ...[more]

PMID: 35695316

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Project description:Objective To develop and validate a model based on the radiomics features of the infarct areas on non-contrast-enhanced CT to predict hemorrhagic transformation (HT) in acute ischemic stroke. Materials and methods A total of 118 patients diagnosed with acute ischemic stroke in two centers from January 2019 to February 2022 were included. The radiomics features of infarcted areas on non-contrast-enhanced CT were extracted using 3D-Slicer. A univariate analysis and the least absolute shrinkage and selection operator (LASSO) were used to select features, and the radiomics score (Rad-score) was then constructed. The predictive model of HT was constructed by analyzing the Rad-score and clinical and imaging features in the training cohort, and it was verified in the validation cohort. The model was evaluated with the receiver operating characteristic curve, calibration curve and decision curve, and the prediction performance of the model in different scenarios was further discussed hierarchically. Results Of the 118 patients, 52 developed HT, including 21 cases of hemorrhagic infarct (HI) and 31 cases of parenchymal hematoma (PH). The Rad-score was constructed from five radiomics features and was the only independent predictor for HT. The predictive model was constructed from the Rad-score. The area under the curve (AUCs) of the model for predicting HT in the training and validation cohorts were 0.845 and 0.750, respectively. Calibration curve and decision curve analyses showed that the model performed well. Further analysis found that the model predicted HT for different infarct sizes or treatment methods in the training and validation cohorts with 78.3 and 71.4% accuracy, respectively. For all samples, the model predicted an AUC of 0.754 for HT in patients within 4.5 h since stroke onset, and predicted an AUC of 0.648 for PH. Conclusion This model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.

Project description:Background: Post-ischemic inflammatory response might be affected by many factors. We chose leukocyte count as a marker of inflammatory response and investigated whether the effects of leukocyte count on the clinical outcomes in acute ischemic stroke patients are different according to different factors. Methods: We derived data from the China National Stroke Registry II. Patients with ischemic stroke were classified into four groups by leukocyte count quartiles within the first 24 h after admission. Adverse clinical outcomes were defined as recurrent stroke, all-cause death, and poor functional outcomes (3 ? mRS ? 5) at 3-months and 1-year follow-up. The subgroup factors were age, sex, history of hypertension, history of diabetes, history of previous stroke, or transient ischemic attack and smoking status. We assessed the association between leukocyte count and adverse clinical outcomes and evaluated this association in different subgroups. Results: A total of 14,678 patients were included. Patients in higher quartiles were likely to be younger, male, smokers, and drinkers, and to have a shorter time from symptom onset to arrival, a more proportion of history of diabetes, atrial fibrillation, and hypertension, and a higher severity of stroke. Higher quartiles were associated with elevated risk of adverse clinical outcomes at 3-months and 1-year follow-up. Leukocyte count had a moderate accuracy to predict clinical outcomes. There was no difference in the relationship between leukocyte count and adverse clinical outcomes across subgroups such as age, sex, history of hypertension, and smoking. The effect of leukocyte count on all-cause death was pronounced among patients with previous stroke or transient ischemic attack, and the effect of leukocyte count on short-term poor functional outcomes was also pronounced among patients without diabetes. Conclusions: Leukocyte count is associated with short-term and long-term clinical outcomes of acute ischemic stroke and may have predictive value, especially in patients with certain specific characteristics.

Dataset Information

Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke.

Objective

Materials and methods

Results

Conclusion

Publications

Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke.

Similar Datasets

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