Predicting Overall Survival of Glioblastoma Patients Using Deep Learning Classification Based on MRIs
Katharina Ott1, Santiago Cepeda2, Dennis Hartmann1
1IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany.
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.
