Prediction of clinical outcome of patients with hypertensive intracerebral hem-orrhage based on CT radiomics
Objective To construct and validate a nomogram model for predicting 30 days clinical outcomes of patients with hypertensive intracerebral hemorrhage(HICH).Methods In a two-center retrospective cohort stud-y,patients in center 1(n=431 cases)were divided chronologically into a training cohort(n=305 cases)and an internal validation cohort(n=126 cases),and patients in center 2 formed an external validation cohort(n=108 cases).Clinical outcomes at 30 days were evaluated according to the modified Rankin score(mRS),with mRS ≤3 indicating a good prognosis and mRS＞3 indicating a poor prognosis.Clinical history,laboratory exami-nation and noncontrast computed tomography scan signs were selected by random forest(RF),and important factors were included in multivariate Logistic regression analysis to identify independent risk factors to construct clinical models.The features of the patients'noncontrast computed tomography images were extracted,the opti-mal selection was selected by LASSO regression,and Radscore was performed on the feature set to construct the radiomics model.Independent risk factors and Radscore were combined to establish clinical radiomic nomogram model.Results The nomogram model is composed of hematoma location,hematoma volume,ventricular rupture,midline shift,GCS score and Radscore,and its AUC is 0.89 in the training cohort,and 0.86 and 0.82 in the internal and external verification cohort,respectively.The calibration curve analysis shows that the nomogram is in the training cohort P=0.944,in the internal verification cohort P=0.540 and in the external verification co-hort P=0.171.DC A analysis shows that the threshold probability of nomogram is(0.10-0.90),which has high clinical application value,and the optimal truncation probability is 0.33.Conclusion The nomogram model can effectively predict the 30-day clinical outcome of patients with HICH,so it has a certain guiding value in clinical diagnosis and treatment.
万方数据
维普
