Exploring Radiologic Criteria for Glioma Grade Classification on the BraTS Dataset |
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| Affiliation: | 1. Department of Electronics and Telecommunications, Symbiosis Institute of Technology, Symbiosis International University, Pune India;2. Department of Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India;3. Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, India;1. Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California;2. Department of Radiation Oncology, Stanford University, Stanford, California;3. Department of Radiation Oncology, University of California, San Francisco, San Francisco, California;4. Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas;5. Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California |
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| Abstract: | ObjectivesGlioma grading using maching learning on magnetic resonance data is a growing topic. According to the World Health Organization (WHO), the classification of glioma discriminates between low grade gliomas (LGG), grades I, II; and high grade gliomas (HGG), grades III, IV, leading to major issues in oncology for therapeutic management of patients. A well-known dataset for machine-based grade prediction is the MICCAI Brain Tumor Segmentation (BraTS) dataset. However this dataset is not divided into WHO-defined LGG and HGG, since it combines grades I, II and III as “lower grades gliomas”, while its HGG category only presents grade IV glioblastoma multiform. In this paper we want to train a binary grade classifier and investigate the consistency of the original BraTS labels with radiologic criteria using machine-aided predictions.Material and methodsUsing WHO-based radiomic features, we trained a SVM classifier on the BraTS dataset, and used the prediction score histogram to investigate the behaviour of our classifier on the lower grade population. We also asked 5 expert radiologists to annotate BraTS images between low (as opposed to lower) grade and high grade glioma classes, resulting in a new groundtruth.ResultsOur first training reached 84.1% accuracy. The prediction score histogram allows us to identify the radiologically high grade patients among the original lower grade population of the BraTS dataset. Training another SVM on our new radiologically WHO-aligned groundtruth shows robust performances despite important class imbalance, reaching 82.4% accuracy.ConclusionOur results highlight the coherence of radiologic criteria for low grade versus high grade classification under WHO terms. We also show how the histogram of prediction scores and crossed prediction scores can be used as tools for data exploration and performance evaluation. Therefore, we propose to use our radiological groundtruth for future development on binary glioma grading. |
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| Keywords: | Glioma grading Machine learning Automatic classification Prediction score Virtual biopsy Radiomics |
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