Survival Analysis of Glioblastoma Patients: An Overview Empowered by Data Engineering

Introduction

Glioblastoma is a type of tumor that occurs in glial cells and accounts for 26.7% of all primary brain and central nervous system tumors. Survival analysis of glioblastoma patients is a key task in clinical management due to the heterogeneity of the tumor. Over the past decades, researchers have proposed various survival analysis methods combining different types of data, such as imaging and genetic information. Especially in recent years, the rise of machine learning and deep learning technologies has transformed traditional statistical analysis-based survival analysis methods. This paper reviews prognostic parameters obtained from diagnostic imaging techniques and genomic platforms, as well as techniques, learning, and statistical analysis algorithms used for prognosis prediction. It highlights the challenges of existing survival prediction studies and proposes future directions for research in this field.

Authors and Publication Information

• Author: Navodini Wijethilake (Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka)
• Co-authors: Dulani Meedeniya, Charith Chitraranjan, Indika Perera, Mobarakol Islam, Hongliang Ren
• Affiliated Institutions:
  1. Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka
  2. Biomedical Image Analysis Group, Imperial College London, UK
  3. Department of Biomedical Engineering, National University of Singapore
  4. Department of Electronic Engineering, Chinese University of Hong Kong
• Publication Date: March 15, 2021
• Journal/Conference: IEEE Access
• DOI: 10.1109/ACCESS.2021.3065965

Research Methods and Process

• Image Data Preprocessing and Segmentation: Includes steps such as skull stripping, registration, normalization, etc., used to obtain image features of the tumor region from MRI sequences.
• Image Feature Extraction: Extracting image features related to morphology and texture from the segmented tumor region, such as the gray-level co-occurrence matrix, gray-level run-length matrix, etc.
• Genetic Data Analysis: Includes gene expression profiles, methylation profiles, and mutation profiles, which reflect the genetic and epigenetic changes in glioblastoma patients.
• Radiomics: Combining imaging features and genomic data to non-invasively predict imaging biomarkers related to genomic behavior.

Research Results and Conclusions

• Survival Analysis Methods: Explored the application of various machine learning and deep learning algorithms in glioblastoma survival analysis and the advantages and limitations of these methods in prognosis prediction.
• Gene Expression Profiles: Confirmed that the expression levels of certain specific genes are closely related to the prognosis of glioblastoma patients, for example, IDH1 mutation is a positive prognostic marker for prolonged survival of glioblastoma patients.
• Combination of Image Features and Genomic Features: Combining information such as gene expression, mutation status, and imaging biomarkers improved the accuracy of predicting the survival time of glioblastoma patients.

Scientific and Practical Value of the Research

This study provides new insights into the survival analysis of glioblastoma patients, emphasizing the critical role of data engineering in improving the accuracy of prognosis estimation. It can not only better guide clinical treatment but also help in the optimal allocation of resources. Future research should continue to explore more efficient data processing methods and combine the use of multiple types of data and technologies to further improve the accuracy of survival prediction models for glioblastoma patients.

Highlights and Innovations of the Research

• Combined imaging features and genomic features for glioblastoma survival analysis, emphasizing the importance of interdisciplinary data sharing.
• Utilized various machine learning and deep learning methods and conducted an in-depth discussion on the advantages and challenges of different methods in prognosis prediction.
• Proposed future directions and development trends for survival prediction applicable to clinical management of glioblastoma.

Other Valuable Content

• Conducted an in-depth analysis of the limitations of existing survival prediction research and proposed corresponding improvement strategies.
• The research indicates the tremendous potential of machine learning and deep learning technologies in developing individualized treatment plans and clinical pathway planning.
• Emphasized the importance of interdisciplinary collaboration in enhancing research and clinical application of survival analysis, pointing the way for individualized treatment of glioblastoma.