## 3 Results
### Model Performance
Given the considerable high intra-tumor heterogeneity of the lesions and the risk that a tissue sampling may not adequately represent the overall genomic profile, imaging, which is performed at the very beginning of the diagnostic process of glioma cases, may be useful in assessing heterogeneity since the imaging data provides a complete view of the tumor. Hence, noninvasive characterization of genomic profiles would better aid clinicians in making more precise treatment and follow-up plans before surgery or biopsy. Genomic analyses could easily be replaced by models predicting the genetic and molecular profiles of the tumors based on imaging if they are highly accurate. Even models with moderate accuracy could potentially be incorporated into diagnostic and therapeutic paradigms. In this study, we demonstrated the value of CNNs employed to post-process the radiomics information from conventional routine MRI from a publicly available multi-institutional dataset to predict a specific gene signature-based risk stratification status in LGG patients. To our knowledge, this is the first study that attempted to predict a gene signature-based risk stratification using imaging studies. The deep-learning model was developed and cross-validated for its performance. AUC values for the test sets were 0.80 and 0.75 for the T1 and T2 input models, respectively. We did not include any clinical parameters in our models, such as age or KPS. Because these alone have a significant impact on classifying the survival risk status of the patients, clinicians are already aware that high age or low KPS indicate likely poor survival outcomes. This method becomes very clinically useful when clinical parameters fail to inform providers in their decision-making process. We did not include WHO grade IV gliomas in our analysis either, since they have clinically distinct survival outcomes compared to LGGs. Identifying high-risk LGG patients early in the course of their disease may be important in clinical decision-making as it may guide clinicians through more aggressive treatment options and closer follow-ups. Despite the promising results, there are a few limitations to be aware of. First, our sample size was relatively small (80 patients), especially considering that neural networks necessitate a massive amount of input for high-performing models. To address this limitation, we used data augmentation and tailored an optimized CNN structure. Second, we applied our proposed model to LGGs only. In clinical scenarios, it would be of higher value if GBMs were also included. Our methodology requires prior knowledge of the tumor grade. Though GBMs have distinct features, some LGG-appearing tumors are actually GBMs. To differentiate LGGs from GBMs at the baseline, advanced MRI techniques, such as diffusion MRI, perfusion MRI or MR spectroscopy, can be used. Furthermore, models predicting survival outcomes may show over-optimistic results if they are implemented in a dataset including both GBMs and LGGs since they have different survival rates inherently. Third, our study includes only the T1- and T2-weighted MRI sequences. We did not utilize advanced MRI techniques, even though they have been shown to provide additional information and can improve prediction performance [45; 46]. Yet, including these sequences is also a potential limitation because these sequences are unfortunately not widely available, just as we did not have them available for our dataset. Lastly, due to the small sample size and the lack of an external dataset containing both the genomic information we used in our study and imaging data, there was no independent test set in our study. Instead, we utilized a commonly used cross-validation technique.