Machine Learning-Based Prediction of Glioma IDH Gene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study)
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Patients
2.2. MRI Data Acquisition
2.2.1. The MRI Study Protocol at the University Clinic St. Pölten
2.2.2. The MRI Study Protocol at the FAU Erlangen-Nürnberg
2.3. MRI Data Processing and Calculation of MRI Biomarker Maps
- Four cMRI data sets: FLAIR and ceT1w MRI data as well as the ADC maps for microstructural density and the CBV maps for macrovascular perfusion.
- Four biomarker maps for oxygen metabolism: MRI-based tissue oxygen metabolism (OEF and CMRO2) as well as MRI-based capillary oxygen tension (capiPO2) and mitochondrial (tissue) oxygen tension (mitoPO2).
- Four biomarker maps for microvascular architecture and neovascularization activity: microvascular density (MVD), microvascular diameter (VSI), microvascular perfusion (µCBV), and microvascular type (MTI).
2.4. Radiomic Feature Extraction
2.5. Traditional Machine Learning
- A multilayer perceptron (MLP) with one hidden layer and number of neurons = number of features + number of classes;
- Adaptive boosting (ABoost) using decision tree “J48” as classifier; and
- Random forest (RF).
- cMRI (ceT1w, FLAIR, ADC, CBV);
- MRI-based oxygen metabolism (CMRO2, OEF, capiPO2, mitoPO2);
- MRI-based vascular architecture (µCBV, MVD, MTI, VSI); and
- The combination of MRI-based oxygen metabolism and vascular architecture.
2.6. Deep Learning
2.7. Model Performance Testing
3. Results
3.1. The Selected Radiomic Features
3.2. Testing with an Independent Internal Cohort
3.3. Testing with an Independent External Cohort
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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ABoost | MLP | RF | CNN | LSTM | |
---|---|---|---|---|---|
Clinical MRI | 0.858 | 0.866 | 0.907 | 0.891 | 0.94 |
Oxygen Metabolism | 0.87 | 0.907 | 0.902 | 0.855 | 0.88 |
Vascular Architecture Mapping | 0.85 | 0.902 | 0.907 | 0.891 | 0.88 |
Oxy Met & VAM | 0.829 | 0.902 | 0.898 | 0.873 | 0.86 |
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Stadlbauer, A.; Nikolic, K.; Oberndorfer, S.; Marhold, F.; Kinfe, T.M.; Meyer-Bäse, A.; Bistrian, D.A.; Schnell, O.; Doerfler, A. Machine Learning-Based Prediction of Glioma IDH Gene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study). Cancers 2024, 16, 1102. https://doi.org/10.3390/cancers16061102
Stadlbauer A, Nikolic K, Oberndorfer S, Marhold F, Kinfe TM, Meyer-Bäse A, Bistrian DA, Schnell O, Doerfler A. Machine Learning-Based Prediction of Glioma IDH Gene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study). Cancers. 2024; 16(6):1102. https://doi.org/10.3390/cancers16061102
Chicago/Turabian StyleStadlbauer, Andreas, Katarina Nikolic, Stefan Oberndorfer, Franz Marhold, Thomas M. Kinfe, Anke Meyer-Bäse, Diana Alina Bistrian, Oliver Schnell, and Arnd Doerfler. 2024. "Machine Learning-Based Prediction of Glioma IDH Gene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study)" Cancers 16, no. 6: 1102. https://doi.org/10.3390/cancers16061102