# Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction

arXiv: https://arxiv.org/abs/2104.01149 · HTML: https://mlsanity.com/paper/2104.01149/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 63 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 25 of 25)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Adversarial Feature Learning](https://mlsanity.com/paper/1605.09782/) (2016)
- [Sparsity Invariant CNNs](https://mlsanity.com/paper/1708.06500/) (2017)
- [Toward Multimodal Image-to-Image Translation](https://mlsanity.com/paper/1711.11586/) (2017)
- [Adversarially Learned Inference](https://mlsanity.com/paper/1606.00704/) (2016)
- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://mlsanity.com/paper/1703.02719/) (2017)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution](https://mlsanity.com/paper/1904.05049/) (2019)
- [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018)
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://mlsanity.com/paper/1803.02579/) (2018)
- [In-Place Activated BatchNorm for Memory-Optimized Training of DNNs](https://mlsanity.com/paper/1712.02616/) (2017)
- [Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation](https://mlsanity.com/paper/1711.01468/) (2017)
- [Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge](https://mlsanity.com/paper/1802.10508/) (2018)
- [Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features](https://mlsanity.com/paper/1812.01049/) (2018)
- [Real-Time Instrument Segmentation in Robotic Surgery using Auxiliary Supervised Deep Adversarial Learning](https://mlsanity.com/paper/2007.11319/) (2020)
- [Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation](https://mlsanity.com/paper/1704.07754/) (2017)
- [MRI Cross-Modality NeuroImage-to-NeuroImage Translation](https://mlsanity.com/paper/1801.06940/) (2018)
- [Competitive Inner-Imaging Squeeze and Excitation for Residual Network](https://mlsanity.com/paper/1807.08920/) (2018)
- [Radiogenomics of Glioblastoma: Identification of Radiomics associated with Molecular Subtypes](https://mlsanity.com/paper/2010.14068/) (2020)
- [Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN](https://mlsanity.com/paper/1905.04105/) (2019)

## Built on by (most cited first, 5 of 5)

- [Medical Image Segmentation on MRI Images with Missing Modalities: A Review](https://mlsanity.com/paper/2203.06217/) (2022)
- [Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation](https://mlsanity.com/paper/2302.04308/) (2023)
- [Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation](https://mlsanity.com/paper/2405.18511/) (2024)
- [Spatio-Temporal Mixture-of-Modality-Experts Diffusion for Quantitative DCE-MRI Synthesis from Incomplete MR Sequences](https://mlsanity.com/paper/2606.25535/) (2026)
- [BrainIAC: Interactive 3D Brain Lesion Segmentation across Heterogeneous MRI Modalities with Online Adaptation](https://mlsanity.com/paper/2609.23026/) (2026)

## Abstract (arXiv)

The accurate prognosis of Glioblastoma Multiforme (GBM) plays an essential role in planning correlated surgeries and treatments. The conventional models of survival prediction rely on radiomic features using magnetic resonance imaging (MRI). In this paper, we propose a radiogenomic overall survival (OS) prediction approach by incorporating gene expression data with radiomic features such as shape, geometry, and clinical information. We exploit TCGA (The Cancer Genomic Atlas) dataset and synthesize the missing MRI modalities using a fully convolutional network (FCN) in a conditional Generative Adversarial Network (cGAN). Meanwhile, the same FCN architecture enables the tumor segmentation from the available and the synthesized MRI modalities. The proposed FCN architecture comprises octave convolution (OctConv) and a novel decoder, with skip connections in spatial and channel squeeze & excitation (skip-scSE) block. The OctConv can process low and high-frequency features individually and improve model efficiency by reducing channel-wise redundancy. Skip-scSE applies spatial and channel-wise excitation to signify the essential features and reduces the sparsity in deeper layers learning parameters using skip connections. The proposed approaches are evaluated by comparative experiments with state-of-the-art models in synthesis, segmentation, and overall survival (OS) prediction. We observe that adding missing MRI modality improves the segmentation prediction, and expression levels of gene markers have a high contribution in the GBM prognosis prediction, and fused radiogenomic features boost the OS estimation.