# Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma

arXiv: https://arxiv.org/abs/2201.05530 · HTML: https://mlsanity.com/paper/2201.05530/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 14 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)

## Built on by (most cited first, 4 of 4)

- [Multi-modal learning for predicting the genotype of glioma](https://mlsanity.com/paper/2203.10852/) (2022)
- [Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image Representations for Glioma Grading](https://mlsanity.com/paper/2203.04013/) (2022)
- [How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images](https://mlsanity.com/paper/2206.07599/) (2022)
- [Unified Modeling Enhanced Multimodal Learning for Precision Neuro-Oncology](https://mlsanity.com/paper/2406.07078/) (2024)

## Abstract (arXiv)

The isocitrate dehydrogenase (IDH) gene mutation status is an important biomarker for glioma patients. The gold standard of IDH mutation detection requires tumour tissue obtained via invasive approaches and is usually expensive. Recent advancement in radiogenomics provides a non-invasive approach for predicting IDH mutation based on MRI. Meanwhile, tumor geometrics encompass crucial information for tumour phenotyping. Here we propose a collaborative learning framework that learns both tumor images and tumor geometrics using convolutional neural networks (CNN) and graph neural networks (GNN), respectively. Our results show that the proposed model outperforms the baseline model of 3D-DenseNet121. Further, the collaborative learning model achieves better performance than either the CNN or the GNN alone. The model interpretation shows that the CNN and GNN could identify common and unique regions of interest for IDH mutation prediction. In conclusion, collaborating image and geometric learners provides a novel approach for predicting genotype and characterising glioma.