To develop a decision tree model based on clinical information, molecular genetics information and pre-operative magnetic resonance imaging (MRI) radiomics-score (Rad-score) to investigate its predictive value for the risk of recurrence of glioblastoma (GBM) within one year after total resection. Patients with pathologically confirmed GBM at Huashan Hospital, Fudan University between November 2017 and June 2020 were retrospectively analyzed, and the enrolled patients were randomly divided into training and test sets according to the ratio of 3:1. The relevant clinical and MRI data of patients before, after surgery and follow-up were collected, and after feature extraction on preoperative MRI, the LASSO filter was used to filter the features and establish the Rad-score. Using the training set, a decision tree model for predicting recurrence of GBM within one year after total resection was established by the C5.0 algorithm, and scatter plots were generated to evaluate the prediction accuracy of the decision tree during model testing. The prediction performance of the model was also evaluated by calculating area under the receiver operating characteristic (ROC) curve (AUC), ACC, Sensitivity (SEN), Specificity (SPE) and other indicators. Besides, two external validation datasets from Wuhan union hospital and the second affiliated hospital of Xuzhou Medical University were used to verify the reliability and accuracy of the prediction model. According to the inclusion and exclusion criteria, 134 patients with GBM were finally identified for inclusion in the study, and 53 patients recurred within one year after total resection, with a mean recurrence time of 5.6 months. According to the importance of the predictor variables, a decision tree model for predicting recurrence based on five important factors, including patient age, Rad-score, O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, pre-operative Karnofsky Performance Status (KPS) and Telomerase reverse transcriptase (TERT) promoter mutation, was developed. The AUCs of the model in the training and test sets were 0.850 and 0.719, respectively, and the scatter plot showed excellent consistency. In addition, the prediction model achieved AUCs of 0.810 and 0.702 in two external validation datasets from Wuhan union hospital and the second affiliated hospital of Xuzhou Medical University, respectively. The decision tree model based on clinicopathological risk factors and preoperative MRI Rad-score can accurately predict the risk of recurrence of GBM within one year after total resection, which can further guide the clinical optimization of patient treatment decisions, as well as refine the clinical management of patients and improve their prognoses to a certain extent.
Keywords: Decision tree model; MRI; deep-learning; glioblastoma; radiomics; short-term recurrence.
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