## 4 Discussion  
The DSC value was 0.839 for the tumor-region extraction, and the extraction was highly accurate even in a single sequence. Additionally, because the matrix sizes of the VOIs were defined as twice of the tumor diameter on the basis of this output result, the VOIs that sufficiently contained the tumor region and surrounding brain parenchyma could be created. Additionally, as shown in Figure 7, some cases with the ring enhancement effect, a typical characteristic of high-grade glioma, were correctly extracted. However, in the case without the ring enhancement effect, as shown in Figure 7, the peripheral edema region was incorrectly extracted as the tumor region, and thus, the similarity index tended to be low. Therefore, the 3D CNN used for tumor-region extraction was mainly trained to recognize the ring enhancement effect as the edge of the tumor region. The pretrained model of the [PERSON] project was a model that trained MR images of glioma patients, and parameter tuning had already been performed. By using this model, it was possible to eliminate the large amount of time required for training and parameter tuning. Regarding tumor grading, accuracy, sensitivity, and specificity were 91.3%, 100%, and 69.2%, respectively. Table 3 shows a comparison of our approach with other approaches in terms of the methods and results. From Table 3, it is observed that the method of [PERSON] et al. classified grade by processing one slice of a single sequence; however, the accuracy was 96.8%, which was higher than that of our proposed method. In addition to the fact that they used a different database from that of other studies, it made simple comparisons difficult. Furthermore, this was not a fully automated grading system because tumor detection and region extraction were performed manually. Subsequently, we focused on studies adapting fully automated 3D processing, including tumor region extraction and grading--similar to our study. The sensitivity of this method (100%) was higher than that of the method proposed by [PERSON] et al. (94.7%), and it is showed that all high-grade tumors were correctly classified. Therefore, it is considered that grade IV tumors can be classified preoperatively with 100% accuracy by this method, leading to early treatment of patients and prediction of prognosis. In addition, the accuracy of our method (91.3%) was lower than that of the method proposed by [PERSON] et al. (97.1%), but comparable to that of [PERSON] et al. (91.3%). While these two methods adopted multi-sequence processing, our method uses a single sequence. From these results, it was confirmed that a single sequence can be used for grading with high accuracy comparable to that of multi-sequences. Therefore, this method can create a highly accurate grade classification pipeline while preventing the occurrence of unusable cases and the increase in calculation cost due to the lack of a specific sequence. However, four VOIs (31%) of grades II and III were misclassified, and the cases with the ring enhancement effect tended to be misclassified as belonging to grade IV (see Figure 7). This may be because the ring enhancement effect is a typical  
Figure 7: Examples of input images and the corresponding grading result. The blue outlined frames are correctly classified, and the red outlined frames are incorrectly classified. characteristic of high-grade glioma. However, grade II and III tumors without the ring enhancement effect were also misclassified as grade IV, and therefore, some features other than the ring enhancement effect might also have contributed to the incorrect classification results. Furthermore, multi-sequences are used for performing preoperative grading in actual clinical settings; additionally, a definitive postoperative diagnosis is also performed by obtaining multilateral information based on the results of both MRI and pathological examinations. However, our evaluation results confirmed that one could perform grading with high accuracy using this method by only considering the T1-weighted images.