## 3 Results
### Model Performance  
Two models were trained with two separate input types. The first input type was T1-weighted (T1), and on the training set, the model had accuracy, sensitivity, specificity, precision, and AUC of 85%, 65%, 95%, 86%, and 94%, respectively. On the test set, the model with the T1 input type had accuracy, sensitivity, specificity, precision, and AUC of 80%, 57%, 92%, 80%, and 73%, respectively. For the input type T2-weighted (T2), the model had accuracy, sensitivity, specificity, precision, and AUC of 79%, 100%, 73%, 68%, and 94%, respectively, on the training set. On the test set, the model with the T2 input type had accuracy, sensitivity, specificity, precision, and AUC of 75%, 100%, 62%, 58%, and 79%, respectively. The model performances are described in Table 2. Figure 2 depicts the ROC \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Variables** & **Training Set (n = 61)** & **Test Set (n = 20)** & \(p\) **Value** & **High Risk (n = 54)** & **Low Risk (n = 27)** & \(p\) **Value** \\ \hline Age at diagnosis & 45.2 (14.4) & 47.6 (11.6) & 0.454 & 44.6 (13.1) & 48.2 (14.8) & 0.291 \\ \hline Gender & & & & & & \\ \hline Male & 31 (50.8\%) & 12 (60\%) & 0.649 & 29 (53.7\%) & 14 (51.9\%) & 1.000 \\ Female & 30 (49.2\%) & 8 (40\%) & & 25 (47.3\%) & 13 (48.1\%) & \\ \hline Histologic Diagnosis & & & & & & \\ \hline Astrocytoma & 18 (29.5\%) & 6 (30\%) & 0.789 & 8 (14.8\%) & 16 (59.3\%) & 0.000 \\ Oligoastrocytoma & 17 (27.9\%) & 7 (35\%) & & 17 (31.5\%) & 7 (25.9\%) & 0.000 \\ Oligoaddroglioma & 26 (42.6\%) & 7 (35\%) & & 29 (53.8\%) & 4 (14.8\%) & \\ \hline Grade & & & & & & \\ \hline II & 24 (39.3\%) & 9 (45\%) & 0.854 & 29 (53.7\%) & 4 (14.8\%) & 0.002 \\ III & 37 (60.7\%) & 11 (55\%) & & 25 (47.3\%) & 23 (85.2\%) & \\ \hline Risk Status & & & & & & \\ \hline High & 41 (67.2\%) & 13 (65\%) & 1.000 & - & - & \\ Low & 20 (32.8\%) & 7 (35\%) & 1.000 & - & - & \\ \hline Gene expression & & & & & \\ \hline WEE1 & 7.8 (1.3) & 7.9 (1) & 0.5254 & 7.3 (0.9) & 9.0 (1) & 0.000 \\ CRTAC1 & 9.9 (9.3-10.7) & 9.7 (8.7-10.4) & 0.7425 & 10.3 (9.5-10.9) & 8.4 (7.5-9.4) & 0.000 \\ SEMA4G & 8.3 (8–8.7) & 8.2 (8–8.5) & 0.7633 & 8.5 (8.2–8.7) & 7.8 (7.0–8.2) & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Patient characteristics. prognosis of gliomas at present. Therefore, further efforts are needed to improve prognostic accuracy and facilitate therapeutic approaches. In addition to molecular markers, recent studies have identified gene expression profiles that make it possible to categorize patients into high- and low-risk groups regarding survival probabilities. Gene signatures have attracted much attention in recent years and have shown great potential for prognostic prediction in LGG patients. [PERSON] et al. constructed a five-gene signature that can predict the individual clinical outcome with high accuracy in LGG patients [42]. [PERSON] et al. developed a ten-gene signature that successfully classified LGGs into groups with different prognostic probabilities [43]. [PERSON] et al. constructed a robust six-immune-related gene signature and established a prognostic nomogram for risk stratification and prediction of overall survival in LGG patients [44]. The risk score calculation, based on the three-gene signature we adopted in our study, was a significant prognostic factor after adjusting for the effects of age, sex, tumor grade, and treatment modalities. [PERSON] et al. identified a three-gene model that showed satisfactory performance in predicting 1-, 3-, and 5-year survival of LGG patients [12]. The AUC value of classification into low- and high-risk groups in 1-, 3- and 5-year survival graphs was greater than 0.75, regardless of the training or validation set. In the current clinical context, determining molecular markers or gene signatures of gliomas requires invasive procedures, such as surgical resection or biopsy.