UCSF

Current methods of glioma pathology assessment using the tumor score metric rely on the extraction of a biopsy sample for evaluation by a pathologist. This method is limited by the fact that tumor score can vary within a glioma and that it only gives information regarding glioma pathology at one time point. An approach in which allows for the assessment of glioma pathology at various timepoints and in the entire brain is thus desirable. We explored such a method of glioma pathology prediction with machine learning, using both traditional and deep learning approaches. Using a dataset of patient information (MRI images and corresponding tumor scores, we performed several experiments with traditional machine learning models to explore the potential benefits of a deep learning based approach. We then developed, trained, and tuned a deep learning model that predicted tumor score from MRI data, and experimented with various forms of transfer learning to evaluate the impact of loading weights from different autoencoders. We determined the results of our traditional machine learning experiments showed a potential for a deep learning model’s ability to predict tumor score from MRI data. When evaluating our deep learning model we found that domain shift played a significant role in affecting our results in terms of testing accuracy, and we explored several methods to alleviate this issue. That said, our deep learning approach did outperform our traditional machine learning models, indicating the effectiveness of this approach.