# BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification
## 1 Introduction  
Brain tumors are a complex and heterogeneous group of neoplasms that pose a significant health challenge worldwide. Despite considerable progress in our understanding of their molecular and genetic underpinnings, the diagnosis, prognosis, and treatment of brain tumors remain formidable tasks [1; 2]. In recent years, the integration of radiological imaging data with genomic information has emerged as a promising avenue in the field of neuro-oncology. This intersection of radiology and genomics, often referred to as \"radio-genomics\", has the potential to revolutionize the approach to brain tumor characterization and classification [3]. The inherent complexity and diversity of brain tumors, coupled with the limitations of traditional histopathological methods, have spurred the search for non-invasive, complementary approaches to improve the accuracy of diagnosis and prognosis. Radiogenomic analysis leverages advanced imaging techniques, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), to extract a wealth of quantitative and qualitative imaging features [4; 5]. These features are then correlated with the genetic and molecular characteristics of brain tumors, offering insights into their underlying biology and clinical behavior. Glioblastoma, the most aggressive and prevalent primary brain tumor in adults, remains a therapeutic challenge despite advances in neuro-oncology [6]. The genetic andepigenetic heterogeneity within glioblastoma underscores the urgent need for precise biomarkers to guide personalized treatment strategies. Among these, the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter has emerged as a critical determinant of the response to temozolomide, the standard chemotherapy for glioblastoma. The predictive power of the MGMT promoter methylation status in glioblastoma treatment outcomes underscores the significance of accurate and timely determination [6; 7]. Traditionally, the determination of the MGMT promoter methylation status has relied on labor-intensive and time-consuming laboratory techniques, such as methylation-specific polymerase chain reaction (MSP) and pyrosequencing. The emergence of AI-driven methods promises to revolutionize this process by analyzing high-dimensional genomic and epigenomic data. In this work, we utilize multi-parametric MRI (mpMRI) scans to address the prediction of the MGMT promoter methylation status at the pre-operative baseline MRI scans. The mpMRI scans consist of different types of 3D volumes (which constitute different modalities for the developed methodology): (a) fluid-attenuated inversion recovery (FLAIR); (b) native (i.e., T1-weighted pre-contrast or, in other words, T1w); (c) T1-weighted post-contrast (T1 wCE); and (d) T2-weighted (T2) 3D volumes. The challenges in using such 3D volumes of data for predicting the MGMT promoter methylation status are twofold: (i) the annotations are at the volume level rather than at the slice level, i.e., there exists one annotation for the whole volume; and (ii) the volumes have variable lengths, i.e., the volumes are 3D signals consisting of series of slices, i.e., 2D images, and each volume consists of a different number of images. The main contribution of this work is that our proposed method effectively addresses and overcomes these two challenges. Traditional approaches handle 3D signals using 3D CNN architectures that generate one prediction per signal [8; 9]; however, such architectures are very complex with a large number of parameters and require pre-training with other large 3D databases. In the healthcare domain and the medical imaging world, large 3D databases, annotated for the purpose of interest, are not easy to find, and in most cases, they are not publicly available due to privacy issues, regulatory frameworks, and policies. Other traditional approaches posit a hypothesis and assign the volume-level label to each slice of the volume, then employ CNN-RNN networks to train with the annotated slices.