To construct a predictive model based on the Delta radiomics and clinical baseline data of preoperative contrast-enhanced MRI imaging, and evaluate its predictive value for recurrence and survival in patients with brain glioma (BG).

A retrospective analysis was conducted on clinical data of 220 BG patients admitted to Oncology Department of Xiangyang No.1 People’s Hospital, Hubei University of Medicine from August 2015 to April 2023. Patients were divided into a normal group (n=125) and a recurrence group (n=95) according to their survival status. The cohort was split into a training set (155 cases, 88 normal cases and 67 recurrence cases) and a testing set (65 cases, 37 normal cases and 28 recurrence cases) at a ratio of 7∶3. Delta radiomics features were extracted from the peritumoral edema area (3-12 mm) and within the tumor, respectively, and the least absolute shrinkage and selection operator (LASSO) algorithm combined with ten-fold cross-validation was used to generate the peritumoral radiomics score (Radscore1) and intratumoral radiomics score (Radscore2). The clinical baseline data and imaging characteristics of normal and recurrent patients in the training and testing sets were compared. Based on the training set data, 3 postoperative recurrence prediction models were constructed using multiple Logistic regression: clinical model, imaging model, and combination model. DeLong test was used to compare the differences in area under the ROC curve (AUC) between the models, and decision curve analysis was used to evaluate the clinical net benefit of each model. XGboost machine learning algorithm was used to verify the prediction results of each model, and the predictive performance of the models was further validated in the testing set. Kaplan-Meier method was used to plot the overall survival (OS) curve after surgery, and the Log-rank test was used for inter group comparison. The median OS and 95%CI were calculated, and a survival column chart was constructed to evaluate its predictive value for postoperative survival of BG patients.

In the training set, there were statistically significant differences in gender, isocitrate dehydrogenase-1 (IDH1) mutation, 1p19q heterozygous deletion, MRI enhancement mode, and Radscore1 and Radscore2 between the two groups of patients (P<0.05). The comparison results of the above data in the testing set are consistent with those in the training set. Multivariate Logistic regression analysis showed that gender, IDH1 mutation status, 1p19q heterozygous deletion, Radscore1 and Radscore2 were independent influencing factors for BG recurrence (P<0.05). Based on the above factors, three prediction models were constructed: clinical model, imaging model, and combination model. ROC curve analysis showed that the predictive performance of the combination model (AUC=0.890, 95%CI: 0.830-0.935) was significantly better than that of the clinical model (AUC=0.748, 95%CI: 0.672-0.814) and the imaging model (AUC=0.832, 95%CI: 0.763-0.887), and the differences were statistically significant (P<0.05). The XGboost algorithm validated the prediction results of the combination model, and the decision curve showed that the clinical net benefit of the combination model was higher. This result was validated in the testing set, and ultimately the column chart based on the combination model simplified the prediction process of BG recurrence, with a sensitivity of 0.857 and a specificity of 0.756 in the testing set. Kaplan-Meier survival curve analysis showed that there were statistically significant differences in OS between subgroups based on gender, 1p19q heterozygous deletion, Radscore1, and Radscore2 (P<0.05). The AUC for predicting BG survival based on the survival column chart established based on these factors is 0.859 (95%CI: 0.802-0.933), with sensitivity and specificity of 0.872 and 0.705, respectively.

The predictive model based on Delta radiomics from preoperative contrast-enhanced MRI can improve the prediction of postoperative recurrence and survival in BG patients, providing a basis for individualized management and clinical decision-making after surgery.