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Introduction:
Glioblastoma (GB) is one of the most aggressive tumors of the brain. Despite intensive treatment, the average overall survival (OS) is 15–18 months. Therefore, it is helpful to be able to assess a patient’s OS to tailor treatment more specifically to the course of the disease. Automated analysis of routinely generated MRI sequences (FLAIR, T1, T1CE, and T2) using deep learning-based image classification has the potential to enable accurate OS predictions.
Methods:
In this work, a method was developed and evaluated that classifies the OS into three classes – “short”, “medium” and “long”. For this purpose, the four MRI sequences of a person were corrected using bias-field correction and merged into one image. The pipeline was realized by a bagging model using 5-fold cross-validation and the ResNet50 architecture.
Results:
The best model was able to achieve an F1-score of 0.51 and an accuracy of 0.67. In addition, this work enabled a largely clear differentiation of the “short” and “long” classes, which offers high clinical significance as decision support.
Conclusion:
Automated analysis of MRI scans using deep learning-based image classification has the potential to enable accurate OS prediction in glioblastomas.
