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

TL;DR

This paper presents a CNN-based method for automatic brain tumor grading from MRI data, aiming to provide a non-invasive, rapid alternative to traditional biopsy-based diagnosis, with interpretability checks for reliability.

Contribution

It introduces a CNN approach for tumor grading directly from MRI scans, eliminating the need for expert annotations and incorporating interpretability for quality assurance.

Findings

Achieved accurate tumor grade predictions from MRI data.

Compared whole brain and tumor region-based approaches.

Implemented interpretability methods for model validation.

Abstract

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.