Identifying key factors for predicting O6-Methylguanine-DNA methyltransferase status in adult patients with diffuse glioma: a multimodal analysis of demographics, radiomics, and MRI by variable Vision Transformer

Glioma is one of the most common primary tumors of the central nervous system (CNS) [1, 2]. The 2021 World Health Organization (WHO) CNS tumors classification recommends performing CNS tumor grading by adding molecular parameters to histological features [3, 4] because certain molecular markers can provide prognostic information [3]. Over the past decade, the methylation status of O6-methylguanine-DNA methyl transferase (MGMT) promoter is associated with an overall survival rate as well as their diagnostic value [5‐7]. When patients with glioblastoma received chemotherapy, patients with methylation of MGMT promoter results in longer survival compared to patients with unmethylated MGMT promoter [8, 9]. MGMT promoter methylation was strongly associated with a superior progression-free rate and survival rate at 12 months [10]. A biopsy and histological examination need to be performed to determine MGMT promoter methylation. The brain tumor property is evaluated by radiological imaging when a biopsy cannot be performed due to reasons, such as tumor size, tumor location, patient comorbidity, and patient condition [10, 11]. Recent studies aimed to predict MGMT promoter methylation by analyzing radiomic features or images using machine learning algorithms, including a convolutional neural network (CNN) [12‐21]. However, the most dominant factors among patient characteristics, radiomic features, and magnetic resonance imaging (MRI) for predicting MGMT promoter methylation remain unclear. This is partly because machine and deep learning have a limitation in simultaneously analyzing these factors in one model [22]. Identifying the dominant factor to predict MGMT promoter methylation is important in the context of the global development of multimodal artificial intelligence solutions [23, 24].

A previous study has proposed a Vision Transformer (ViT)-inspired model, named variable ViT (vViT) that analyzes multiple sequences of different lengths [22, 25‐27]. The vViT simultaneously handles multimodal factors (patient characteristics, radiomic features, and MRI), calculating prediction accuracy for each factor and then integrating them into the overall performance. One strength of vViT is its ability to quantitatively evaluate, or identify, the most dominant factor by calculating the prediction accuracy for each factor in a single model [27]. This strength is attributed to that the vViT analyzes input factors separately. However, limited studies have applied vViT to predicting MGMT promoter methylation among adult patients with diffuse gliomas. This study aimed to investigate the performance of vViT and identify the dominant factor among patient characteristics, radiomic features, and MRI using vViT in predicting MGMT promoter methylation among adult patients with diffuse glioma.

Table 1 shows the characteristics of the patients in the training and test datasets.

The present study aimed to, first, examine the performance of vViT in predicting MGMT promoter methylation among adult patients with diffuse glioma, using demographics, radiomic features, and MRI itself and, second, identify the dominant factor among demographics, radiomic features, and MRI itself. To the best of our knowledge, this is the first study to apply vViT in predicting MGMT promoter methylation and to determine the dominant factor among demographics, radiomic features, and MRI itself. The vViT in the patient-base analysis demonstrated an accuracy of 0.833 and AUC-ROC of 0.840 for the test dataset. Robinet et al. applied EmbraceNet to predict MGMT promoter methylation using T1WI and fluid-attenuated inversion recovery (FLAIR) images [37]. Robinet et al. revealed slightly higher performance than random and concluded the poor performance of current deep learning methods in determining MGMT promoter methylation from MRI. Moreover, the BraTS21 competition, which was hosted by the Radiological Society of North America (RSNA) and the Medical Image Computing and Computer-Assisted Interventions, achieved first place with an accuracy of 0.67 and AUC-ROC of 0.62 [21]. The dataset we used was partly overlapped with the dataset used in this competition. Regarding this point, vViT achieved higher performance than previously reported. In this study, there was no CNN model that achieved statistically higher accuracy and AUC-ROC compared with vViT. On the other hand, Yogananda et al. and Korfiatis et al. reported 0.947 and 0.949 of accuracies using CNN in predicting MGMT promoter methylation, respectively [17; 38]. The inconsistency may be caused by differences in study population characteristics and the classification method. Hence, the reproducibility of each method should be continuously confirmed. In a recent study, Xu et al. achieved 0.952 of accuracy by ViT using CE-T1WI and T2WI [38]. Although the performance was overestimated due to the imbalance of the dataset, the result indicates that the multimodal approach by Transformer is promising in predicting MGMT status. In the present study, we revealed that the multi-modality analysis reached higher performance than CNNs in predicting MGMT promoter methylation although vViT could not achieve state-of-the-art performance. The development of effective multimodal fusion approaches is becoming increasingly important to capture features of complex diseases [39]. Predicting MGMT promoter methylation among adult patients with diffuse glioma is not an exception.

The dominance of radiomic features or MRI itself in predicting MGMT promoter methylation remains controversial. The radiomic features tend to be analyzed using a machine learning algorithm [12, 14‐16], while MRI tends to be analyzed using CNN [17‐21]. The present study compared the permutation importance of radiomic features and MRI by vViT. By evaluating the importance of each sector, our vViT overcomes a difficulty of CNN: convoluting the input values makes it difficult to estimate the importance of input factors in making predictions although conventional CNN can handle multiple factors. We revealed that the radiomic features of ED had the highest importance in both image-based and patient-based analysis. A previous study indicated that the heterogeneity of edema region may have key information on MGMT promoter methylation [2]. As Yang et al. mentioned in this study, radiomics may be a promising technique to evaluate the heterogeneity in the edema region. Other studies insisted that the edema region represents the aggressive degree of glioma [40, 41]. The result of the present study reconfirmed these previous studies.

The present study has some limitations. First, we used the retrospectively collected dataset in a single center. The generalizability of the present study should be validated using another dataset. If there are errors such as data duplication or misidentification, the errors may not be identified. Of course, there is another open dataset that collected data from patients with glioma [42]. However, predicting MGMT promoter methylation is a challenging task and there remains inconsistency in performance. This inconsistency can be explained by differences among datasets or methods of image preprocessing as well as the performance of the deep learning model. Another dataset collected under different criteria and imaging conditions may be inappropriate for validation [43]. It cannot be stated that vViT is applicable in a clinical setting based on the results of this study alone. A comparison of vViT with radiologists or diagnostic improvement by radiologists using vViT should be examined in a future study. Second, the patient-based analysis revealed inequality between the numbers of patients. The training and test datasets were constructed to have the same number of images to input as many as possible number of images into the vViT. By this equalization, the overestimation of performance was avoided. This development led to inequality and selection bias, with an overestimated performance of the patient-based analysis. However, the performance of image-based analysis was partly comparable with previously reported MGMT prediction. This effect may be limited. Third, the radiomic and image sectors demonstrated an imbalance. The vViT had six radiomic and two image sectors in predicting MGMT promoter methylation. The imbalance between the number of radiomic and image sectors in vViT may make findings of radiomic features dominant. Changing the implementation setting in split-sequence and linear projection may be a solution. When the parameters of vViT and the number of radiomics are changed, the performance can be improved. As far as we investigated, the best performance was obtained when 64 radiomic features were used. Fourth, the interpretability of each demographic and radiomic feature was insufficient. We were not able to determine the contribution of each radiomic feature included in the T2WI ED radiomic sector. This point leads to difficulty in clarifying how the edema region was evaluated by radiomics. However, we can speculate the dominant radiomic feature by the F-value shown in Appendix 3. In addition to this, the biological meaning of each feature can be checked by a document [31].

In conclusion, vViT can be a competent model for predicting MGMT promoter methylation among adult patients with diffuse glioma compared with conventional CNN models. The input factors can be ranked by combining vViT with permutation feature importance. The most dominant factor among demographics, radiomic features, and MRI in predicting MGMT promoter methylation was the radiomic features derived from the edema region in T2WI for both image- and patient-based analysis. The radiomic features derived from CE-T1WI and T2WI had statistically higher importance than CE-T1WI and T2WI itself in predicting MGMT promoter methylation. The present study demonstrates that radiomic features have higher permutation importance in predicting MGMT promoter methylation compared with MRI itself.