Objective To develop a new clinical radiomic nomogram model for predicting the one-year mortality risk of glioblastoma patients after surgery,to provide guidance for the prognosis of glioblastoma patients.Methods 100 patients with glioblastoma were enrolled and divided into training group(n =50)and verification group(n =50).A graph was de-veloped incorporating patient-relevant clinical factors and MRI radiomic features.The radiomic features of the enhanced re-gion of interest were extracted,and the features for modeling were screened by the minimum absolute convergence and selec-tion operator(LASSO regression),and the radiomic score was calculated.Univariate and multivariate analyses were conduc-ted to find meaningful clinical variables.A single model based solely on radiomic score and a combined model based on clinical variables and radiomic score were established.ROC curve,clinical decision curve was respectively used to evaluate the two models in the training group and the verification group,and the optimal model was selected for clinical guidance.Results Significant radiomic features were screened from the regions of interest in the enhanced images of glioblastoma pa-tients and radiomic scores were calculated.Two independent risk factors were screened from related clinical factors of pa-tients,namely the degree of edema in the lesion and the presence or absence of bleeding in the lesion.The AUC of the com-bined model was 0.890 in the training group and 0.879 in the verification group.The AUC of the single model was 0.842 in the training group and 0.798 in the verification group.Clinical decision curve showed that the model had high clinical prac-ticability.Conclusion The combined model is superior to the single model.The accuracy of the model combined with ra-diomic scores and relevant clinical variables was higher.The clinical radiomic nomogram is a non-invasive predictive tool to assist clinicians in assessing the one-year risk of death in patients with glioblastoma.
NETL
NSTL
万方数据
