# Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment

arXiv: https://arxiv.org/abs/1809.09468 · HTML: https://mlsanity.com/paper/1809.09468/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 11 of 11)

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- [Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning](https://mlsanity.com/paper/2111.14959/) (2021)
- [CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification](https://mlsanity.com/paper/2203.10690/) (2022)
- [An Efficient Ensemble Explainable AI (XAI) Approach for Morphed Face Detection](https://mlsanity.com/paper/2304.14509/) (2023)
- [Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems](https://mlsanity.com/paper/2307.03853/) (2023)
- [A latent space for unsupervised MR image quality control via artifact assessment](https://mlsanity.com/paper/2302.00528/) (2023)

## Abstract (arXiv)

Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with patients having a poor prognosis. Lower grade gliomas are less aggressive, but they can evolve into higher grade tumors over time. Patient management and treatment can vary considerably with tumor grade, ranging from tumor resection followed by a combined radio- and chemotherapy to a "wait and see" approach. Hence, tumor grading is important for adequate treatment planning and monitoring. The gold standard for tumor grading relies on histopathological diagnosis of biopsy specimens. However, this procedure is invasive, time consuming, and prone to sampling error. Given these disadvantages, automatic tumor grading from widely used MRI protocols would be clinically important, as a way to expedite treatment planning and assessment of tumor evolution. In this paper, we propose to use Convolutional Neural Networks for predicting tumor grade directly from imaging data. In this way, we overcome the need for expert annotations of regions of interest. We evaluate two prediction approaches: from the whole brain, and from an automatically defined tumor region. Finally, we employ interpretability methodologies as a quality assurance stage to check if the method is using image regions indicative of tumor grade for classification.