Multimodal MRI Prediction Of Clinical Outcomes In Patients With Acute Ischemic Stroke Receiving Conventional Supportive Care

Part Ⅰ Prediction of prognosis of AIS by baseline DWI combined with clinical dataObjective:To investigate the value of baseline DWI combined with clinical data in predicting the clinical prognosis of AIS patients receiving conventional supportive care.Methods:There were 153 AIS patients diagnosed by MRI within 24-72 hours of onset and receiving conventional supportive care.According to the modified Rankin scale(mRS)score at 90 days,the patients were divided into good outcome group and poor outcome group.The general data of patients such as age,gender,living habits,physical examination,laboratory examination,DWI infarct volume and relative ADC were collected.Independent sample t test was used to compare the measurement data between the two groups,and chi-square test was used to compare the count data.The differential parameters were analyzed by binary Logistic analysis and a regression model was established.The receiver operating characteristic(ROC)curve was used to test the predictive efficacy of different parameters and models for the clinical prognosis of patients.P<0.05 was considered statistically significant.Results:There were significant differences in DWI infarct volume,rADC,age and systolic blood pressure between the two groups(P<0.05).The rADC,DWI infarct volume and age were independent factors for the clinical prognosis of AIS patients.The ROC curve showed that the combined model of the three had the best predictive value,with a sensitivity,specificity and accuracy of 75.0%,87.1%and 82.4%,respectively.Conclusion:The combination of baseline DWI and clinical data can significantly improve the prognostic value of AIS patients receiving conventional supportive care.Part Ⅱ Prediction of prognosis of AIS by baseline amide proton transfer weighted imaging combined with DWI and clinical dataObjective:To investigate the prognostic value of baseline APT combined with DWI and clinical data in patients with AIS receiving conventional supportive treatment.Methods:There were 54 AIS patients diagnosed by MRI within 24-72 hours of onset and treated with conventional supportive care.According to the modified Rankin scale(mRS)score at 90 days,the patients were divided into good outcome group and poor outcome group.The general data of patients such as age,gender,living habits,physical examination,laboratory examination,DWI infarct volume,relative ADC,ΔAPTw(the relative APT signal of the lesion to the contralateral side)were collected.Independent sample t test was used to compare the measurement data between the two groups,and chi-square test was used to compare the count data.The differential parameters were analyzed by binary Logistic analysis and a regression model was established.The receiver operating characteristic(ROC)curve was used to test the predictive efficacy of different parameters and models for the clinical prognosis of patients.P<0.05 was considered statistically significant.Results:There were significant differences in DWI infarct volume,rADC value and ΔAPTw value between the two groups(P<0.05).DWI infarct volume and ΔAPTw value were independent factors for the clinical prognosis of AIS patients.ROC curve analysis showed that the area under the curve of infarct volume on DWI was 0.885,and the sensitivity and specificity were 90.9%and 75.0%,respectively.The area under the ΔAPTw curve was 0.917,and the sensitivity and specificity were 90.9%and 87.5%,respectively.The area under the curve of the combined model was 0.947,the sensitivity and specificity were 95.5%and 87.5%,respectively,and the accuracy was 90.7%.Conclusion:The prediction model of AIS patients’ clinical prognosis was established based on baseline APT combined with DWI sequence information,which could improve the sensitivity and specificity,and the accuracy rate was up to 90.7%.APT technique can be used as a supplement to conventional MRI sequence,and it is a promising non-invasive examination method for the prognosis evaluation of AIS patients.