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Radiomics for precision medicine in glioblastoma
Kiran Aftab1, Faiqa Binte Aamir2, Saad Mallick2
1Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan.
Introduction:
Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients.
Methods:
We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma.
Results:
Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice.
Conclusion:
Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.
Insights
Radiomics, using machine learning on brain imaging, shows promise for personalized glioblastoma treatment. While still developing, it can aid in diagnosis and predicting survival, offering hope for improved patient outcomes.
Area of Science:
- Neuro-oncology
- Medical imaging analysis
- Machine learning applications
Background:
- Glioblastoma is the most common primary brain tumor with poor treatment outcomes.
- Tumor heterogeneity and molecular variability hinder current treatment approaches.
- Radiomics offers a data-driven approach to analyze tumor imaging for personalized management.
Purpose of the Study:
- To review the literature on radiomics and radiogenomics in glioblastoma.
- To assess their role in diagnosis, stratification, prognostication, and treatment monitoring.
- To understand the potential for personalized glioblastoma management.
Main Methods:
- Comprehensive literature review of radiomics and radiogenomics studies in glioblastoma.
- Analysis of models for diagnosis, survival prediction, and treatment response.
- Evaluation of current limitations and future directions for clinical translation.
Main Results:
- Radiomics classifiers integrating imaging, genetic, and clinical data predict tumor diagnosis, survival, and treatment response with moderate accuracy.
- Current applications of radiomics in glioblastoma treatment are nascent.
- Development of robust machine learning models requires larger datasets and standardized methodologies.
Conclusions:
- Radiomics holds significant potential to revolutionize glioblastoma management.
- Personalized medicine approaches driven by radiomics can improve patient care.
- Further research and standardization are crucial for clinical implementation.
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