Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI
Abstract
:1. Introduction
2. Methods
2.1. Literature Review
- (“Machine Learning” OR “artificial intelligence” OR “Deep Learning”) AND brain AND (tumor OR tumour) AND (survival OR “life expectancy”) AND (pediatric OR paediatric OR adults) AND (MRI OR “magnetic resonance”)
- no full-text available;
- no AI application or non-pertinent application;
- conference proceedings;
- books or book chapters;
- non-English manuscripts.
2.2. Metrics
3. Results
3.1. Years: 2012–2016
3.2. Years: 2016–2018
3.3. Years: 2019–2020
3.4. Years: 2021–2022
3.5. Overall Considerations
3.6. Performance
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Overall Survival (OS) | |||||
---|---|---|---|---|---|
Reference | AI Method | Evaluation Procedure | Number of Cases | Analysed Features | Performance on Test Set (C-Index) |
Lu et al. [87] (2020) | Random Survival Forest (RSF) | 70–30% + 10-fold CV | 181 | Radiomic, Clinical, Semantic (VASARI) features | 0.91 |
Kim et al. [68] (2019) | Generalised Linear Model | 70–30% + 10-fold CV + LASSO | 83 | Radiomic, Clinical, PWI, DTI features | 0.87 |
Chen et al. [89] (2021) | Random Survival Forest (RSF) | 60–40% | 95 | Clinical, DVH features | 0.85 |
Rathore et al. [90] (2021) | Cox Proportional Hazard Regression (CPH) | 60–40% | 171 | Radiomic, histopathological features + Multimodal imaging | 0.79 |
Survival Classification (SC) | |||||
---|---|---|---|---|---|
Reference | AI Method | Evaluation Procedure | Number of Cases | Analysed Features | Performance on Test Set (Accuracy) |
Grist et al. [93] (2021) | Single Layer Neural Network | Stratified 10-fold CV | 69 | Clinical, Bayesian, PWI, DWI features | 98.0% |
Sanghani et al. [64] (2018) | Support Vector Machine (SVM) | Stratified 5-fold CV | 163 | Radiomic, Volumetric features + Multimodal imaging | 97.5% |
Nematollahi et al. [71] (2018) | Decision Trees | 10-fold CV | 55 | Clinical, Imaging (MRI) features + Multimodal imaging | 90.9% |
Nie et al. [75] (2019) | 3D − Convolutional Neural Network (CNN) + SVM | 75–25% + 3-fold CV | 68 | Deeply learned (DTI, fMRI) features + Multimodal imaging | 90.7% |
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di Noia, C.; Grist, J.T.; Riemer, F.; Lyasheva, M.; Fabozzi, M.; Castelli, M.; Lodi, R.; Tonon, C.; Rundo, L.; Zaccagna, F. Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics 2022, 12, 2125. https://doi.org/10.3390/diagnostics12092125
di Noia C, Grist JT, Riemer F, Lyasheva M, Fabozzi M, Castelli M, Lodi R, Tonon C, Rundo L, Zaccagna F. Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics. 2022; 12(9):2125. https://doi.org/10.3390/diagnostics12092125
Chicago/Turabian Styledi Noia, Christian, James T. Grist, Frank Riemer, Maria Lyasheva, Miriana Fabozzi, Mauro Castelli, Raffaele Lodi, Caterina Tonon, Leonardo Rundo, and Fulvio Zaccagna. 2022. "Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI" Diagnostics 12, no. 9: 2125. https://doi.org/10.3390/diagnostics12092125