# BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification
## 2 Related Works  
In this section, we describe the methods that participated in the RSNA-ASNR-MICCAI BraTS 2021 Challenge [17] for classifying a tumor's MGMT promoter methylation status from the pre-operative baseline mpMRI data of 2040 patients. It should be noted that all methods (i) addressed the variable volume lengths by either sub-sampling or duplicating slices; (ii) addressed the annotations at the volume level using either 3D CNN networks or CNN-RNNs, in which the annotations were propagated to each slice within the volume; and (iii) developed either multimodal (utilizing all modalities of the mpMRI scan) or unimodal approaches (utilizing just the FLAIR modality). The winning method of the RSNA-ASNR-MICCAI BraTS 2021 Challenge (denoted hereafter as 3D-Resnet10-Trick) [8] employed a 3D CNN model utilizing the Resnet10 [18] architecture with the FLAIR modality from the mpMRI. The model processed slices with dimensions of \(256\times 256\). A technique known as the \"Best Central Image Trick\" was introduced to construct the 3D input volumes for the model. Initially, the 'best' slice was selected as the central image of the newly constructed 3D volume (where 'best' denotes the image containing the largest brain cutaway view). Then, the 20 slices that existed before the 'best' slice and the 20 slices that existed after the 'best' slice were selected (with 'before' and 'after' referring to the slices within the original FLAIR 3D volume). Finally, all these slices were concatenated in depth to form the input volume to the 3D-Resnet10. The runner-up solution in the competition (denoted hereafter as EfficientNet-LSTM-mpMRI) [10] leveraged a CNN-RNN architecture, where EfficientNet-B0 [19] was the selected CNN model and LSTM [20] was the selected RNN model. EfficientNet-B0 was pre-trained and used as a feature extractor, whereas the LSTM was trainable. This approach was multimodal and utilized all four modalities: FLAIR, T1w, T1 wCE, and T2. A fixed temporal subsampling was performed to produce ten slices from each MRI modality. These slices were concatenated in depth into a four-channel map, with absent MRI types replaced with zero-filled channels. A 2D convolution converted the four-channel image into a three-channel feature map, aligning with EfficientNet's requirement. Stratified fivefold cross-validation was employed, and ensemble predictions were derived by averaging the outputs from all models. The third-place solution (denoted hereafter as EfficientNet-Aggr) [11] was a multi-modal approach comprising four instances of the EfficientNet-B3 model, each dedicated to a specific modality. This approach utilized a stratified split based on patient IDs and classes within the training dataset to derive a validation set. The aggregation of the results was performed on a per-patient basis, emphasizing the divergence between the maximum predictions and the mean and subsequently comparing the average and minimum predictions. The prediction associated with the most pronounced difference was retained. The fourth-placed solution (denoted hereafter as YOLO-EfficientNet) [9] combined object detection using YOLOv5 [21] and classification using 2D and 3D EfficientNet variants. Focusing on the T1 wCE type, they employed a systematic seven-slice selection after resampling. The YOLOv5 object detection model, trained on hand-annotated images, identified tumor slices. For 2D classification, the best results were from the T1 wCE axials with the EfficientNet B3 model, using diverse augmentations (center cropping and adding noise to 3D data) and techniques like test-time augmentation (TTA) and power ensembling. In the absence of detectable tumors, all slices were globed together and passed on as 3D input. The model was trained with a stratified fivefold cross-validation. The fifth-place solution (denoted hereafter as stats-EfficientNet) [12] initially sampled ten images from each modality.