title: Multimodal survival analysis on glioblastoma
Creators: name: Wang, Tongjie
Publisher: name: The University of Edinburgh
Year: 2025
Subjects: subject: Multimodal
subject: Survival analysis
subject: Glioblastoma
subject: Genomic variants
subject: Deep learning

description: Survival analysis is a statistical method to estimate disease severity. The traditional standard survival analysis and data engineering have enhanced our understanding of factors affecting patient survival analysis in tabular formats using clinical records or gene expression levels. This approach has led to increasing survival rates and improved treatment outcomes for cancers, as well as the identification of many factors likely correlated with cancer formation and response to treatment. Concurrently, advancements in genomic sequencing and medical image inspection allow us to gain multi-dimensional insights into cancer properties. However, standard survival tools struggle to effectively model the impact of these multi-factors beyond the traditional multi-omics analysis on patients' prognosis, calling for novel and feasible methods to handle complex biological data and adapting survival analysis on diverse biological modalities, such as pathological images and genomic variants. To enhance the survival prediction and improve treatment outcomes, we developed a survival analysis method tested on glioma, a type of malignant brain tumor. Our method is capable of extracting clinically crucial features from multiple modalities and integrating them, thereby optimizing the accuracy of survival predictions, a step beyond what traditional survival tools using tabular data can provide. We specifically focus on analyzing and extracting features from pathological images, genomic variants, and tabular data. Our contributions include the development of a novel algorithm by unsupervised learning, with the capability of semantic segmentation of histopathological slides to extract clinically significant features, thus optimizing survival predictions. It is also noted that our algorithm surpasses both supervised and unsupervised models in segmenting nuclei while also identifying two other features (microvasculature/blood vessels and cytoplasm/extracellular matrix) that are currently untouched by existing research. Secondly, this research introduces a brand new method using a natural language processing model to interpret transcriptomic variants. This algorithm shows better performance compared to using pathological scores, offering a novel method to understand the prognostic impact of genomic variants without prior knowledge from laboratory work. The thesis also presents a comparative analysis between traditional survival analysis methods with data engineering versus deep learning-based survival analysis for tabular data. Our novel approach applies explainable models - LIME and SHAP, to deep learning-based survival analysis. This overcomes the concern concerns about the clinical applicability of deep-learning-based survival models due to their lack of interpretability. Finally, we show a novel approach with the capability of integrating cancer multimodalities including image, sequential and tabular data to predict survival and harvest a higher accuracy in survival prediction. The thesis paves the path to bridging the multiple clinically crucial modalities for survival prediction, offering a potential way to improve patients' prognostic, personalizing treatment and knowledge of cancer.