Challenges in predicting glioma survival time in multi-modal deep networks
| Autor: | Yunzhe Xue, Abdulrhman Aljouie, Meiyan Xie, Usman Roshan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Artificial neural network Tumor region Computer science business.industry Pattern recognition medicine.disease Data modeling 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Modal Margin (machine learning) 030220 oncology & carcinogenesis Glioma medicine Artificial intelligence business Dropout (neural networks) Glioblastoma |
| Zdroj: | BIBM |
| Popis: | Prediction of cancer survival time is of considerable interest in medicine as it leads to better patient care and reduces health care costs. In this study, we propose a multi-path multimodal neural network that predicts Glioblastoma Multiforme (GBM) survival time at the 14 months threshold. We obtained image, gene expression, and SNP variants from whole-exome sequences all from the The Cancer Genome Atlas portal for a total of 126 patients. We perform a 10-fold cross-validation experiment on each of the data sources separately as well as the model with all data combined. From post-contrast Tl MRI data, we used 3D scans and 2D slices that we selected manually to show the tumor region. We find that the model with 2D MRI slices and genomic data combined gives the highest accuracies over individual sources but by a modest margin. We see considerable variation in accuracies across the 10 folds and that our model achieves 100% accuracy on the training data but lags behind in test accuracy. With dropout our training accuracy falls considerably. This shows that predicting glioma survival time is a challenging task but it is unclear if this is also a symptom of insufficient data. A clear direction here is to augment our data that we plan to explore with generative models. Overall we present a novel multi-modal network that incorporates SNP, gene expression, and MRI image data for glioma survival time prediction. |
| Databáze: | OpenAIRE |
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