A Novel MKL Method for GBM Prognosis Prediction by Integrating Histopathological Image and Multi-omics Data

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors with very short prognosis expectation. To improve patients' clinical treatment and their life quality after surgery. Researches have developed tremendous in silico models and tools for predicting GBM prognosis based on molecular datasets and have earned great success. However, pathology still plays the most critical role in cancer diagnosis and prognosis in the clinic at present. Recent advancement of storing and processing histopathological images have drawn attention from researches. Models based on histopathological images are devel- oped which shows great potential for computer-aided pathological diagnoses. But models based on both molecular and histopatho- logical images that could predict GBM prognosis with high accuracy is not present yet. In our previous research, we used the simpleMKL method to integrate multi-omics data to improve GBM prognosis prediction successfully. In this research, we have developed a novel MKL method, named HI-MKL, that could integrate both histopathological images and multi-omics data efficiently. By using datasets from The Cancer Genome Atlas (TCGA) project, we have built a system that could predict the GBM prognosis with high accuracy. Our research shows that HI-MKL is an accurate, robust and generalized MKL method which performs well in GBM prognosis task.