# Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma

arXiv: https://arxiv.org/abs/1911.06687 · HTML: https://mlsanity.com/paper/1911.06687/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2019.

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

- [Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine](https://mlsanity.com/paper/2211.09317/) (2022)
- [Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Images](https://mlsanity.com/paper/2206.01903/) (2022)
- [Modeling of Textures to Predict Immune Cell Status and Survival of Brain Tumour Patients](https://mlsanity.com/paper/2206.01897/) (2022)
- [Radiomics in Medical Imaging: Methods, Applications, and Challenges](https://mlsanity.com/paper/2602.00102/) (2026)
- [Future Artificial Intelligence tools and perspectives in medicine](https://mlsanity.com/paper/2206.03289/) (2022)

## Abstract (arXiv)

This paper proposes to use deep radiomic features (DRFs) from a convolutional neural network (CNN) to model fine-grained texture signatures in the radiomic analysis of recurrent glioblastoma (rGBM). We use DRFs to predict survival of rGBM patients with preoperative T1-weighted post-contrast MR images (n=100). DRFs are extracted from regions of interest labelled by a radiation oncologist and used to compare between short-term and long-term survival patient groups. Random forest (RF) classification is employed to predict survival outcome (i.e., short or long survival), as well as to identify highly group-informative descriptors. Classification using DRFs results in an area under the ROC curve (AUC) of 89.15% (p<0.01) in predicting rGBM patient survival, compared to 78.07% (p<0.01) when using standard radiomic features (SRF). These results indicate the potential of DRFs as a prognostic marker for patients with rGBM.