| Optimizing Glioblastoma, IDH-wildtype Treatment Outcomes : A Radiomics and Support Vector Machine -Based Approach to Overall Survival Estimation |
Jiunn-Kai Chong1
, Priyanka Jain2
, Shivani Prasad2
, Navneet Kumar Dubey3
, Sanjay Saxena2
, Wen-Cheng Lo1,4,5
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1Department of Neurosurgery, Taipei Medical University Hospital, Taipei, Taiwan
2Department of Computer Science & Engineering, International Institute of Information Technology, Bhubaneswar, Odisha, India
3Executive Programme in Healthcare Management, Indian Institute of Management, Lucknow, India
4Division of Neurosurgery, Department of Surgery, College of Medicine, Taipei Medical University, Taipei, Taiwan
5Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan |
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Received: May 18, 2024; Revised: June 20, 2024 Accepted: August 1, 2024. Published online: August 5, 2024. |
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| ABSTRACT |
Objective
: Glioblastoma multiforme (GBM), particularly the IDH-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches.
Methods
: This study utilizes a Support Vector Machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (< 12 Months) and long (>=12 Months) survivors. A dataset comprising multi-parametric MRI (mpMRI) scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, FLAIR, and T1-Gd sequences. Low variance features were removed, and Recursive Feature Elimination (RFE) was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT promoter methylation status were integrated to enhance prediction accuracy.
Results
: The model showed reasonable results in terms of cross-validated AUC of 0.84 (95% CI: 0.80-0.90) with (p-value < 0.001) effectively categorizing patients into short and long survivors. Log-rank test (Chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value<0.0001.
Conclusion
: The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS. |
| Key Words:
GBM · IDH-wildtype · Overall survival · Radiomics & radiogenomics |
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