Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Search Strategy and Selection Criteria
2.2. Planning and Performance of the Review
2.3. Studies Corresponding Publication Year
2.4. Characteristics of Studies
Reference | Application Field | Diseases | NP (Type) | MRI Sequence | Region for Feature Extraction | Software Used | NF | FS | CM | VM | Performance | RQS |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Prasanna et al., 2017 [10] | Prognosis | GBM | 65(36 long-term survival, 29 short-term survival) | T2W, Gd-T1W, FLAIR | ET, NCR, PTR | MATLAB | 134 | 12 (mRMR) | RF | 3-Fold CV | CI = 0.68~0.78 | 56% |
Shofty et al., 2018 [25] | Diagnosis | LGG | 47 (26 oligodendroglia, 21 astrocytomas) | T1WGd, T2W, FLAIR | Pre-defined Lesion area of tumor | MATLAB | 152 | PCA | SVM, KNN, Ensemble classifier | 5-Fold CV | AUC = 0.87 | 69% |
Akbari et al., 2018 [26] | Diagnosis and prognosis | GBM | 129 (74 male, 55 female) | T1WGd, T1W, T2W, T2-FLAIR, DTI | ET, non-ET, ED | CaPTk | 436 | Yes | SVM | 10-Fold CV | AUC = 0.92 | 75% |
Cho et al., 2018 [27] | Prognosis and survival | Glioma | 285 (210 HGG, 75 LGG) | T1W, T2W, T1ce, FLAIR | ET, non-ET, ED | PyRadiomics, MATLAB | 468 (3 Types) | yes (Top 5) | SVM, RF | 5-Fold CV | AUC = 0.903 | 75% |
Rathore et al. 2018 [28] | Prognosis | GBM | 31 | T1W, T2W, T1ce, FLAIR, DTI | ED, ET, NET | CaPTk | n/a | n/a | SVM | LOO CV | AUC =0.91 | 72% |
Binder et al., 2018 [29] | Survival | GBM | 260 | T1W, T2W, T1ce, FLAIR | ET, non-ET, ED | CaPTk | 1650 | yes (p > 0.05) | Multivariate classification framework | 5-Fold CV | ------- | 78% |
Abidin et al., 2019 [30] | Diagnosis | METs and Glioma | 52 | T1ce, T2 FLAIR | Tumor | Amira | 630 | no | AdaBoost | 10-Fold CV | AUC = 0.84 | 64% |
Talamonti et al., 2019 [31] | Survival | Medulloblastoma | 70 | T1W TSE MDC, T2W TSE, T2W FLAIR | Necrosis, solid tumor, and oedema | PyRadiomics | ---- | yes | SVM | LOO-CV | ------ | 72% |
Hajianfar et al., 2019 [32] | Diagnosis | GBM | 82 | T1W, T2W, T1ce, FLAIR | NCR, WT, ET, ED | R, Python | 7000 | Top 20 | Ada-Boost, DT | 10-Fold CV | AUC = 0.74 | 81% |
Hamerla et al., 2019 [33] | Diagnosis | Meningioma | 147 | T1W, T2W, T1ce, FLAIR | Peritumoral ED | PyRadiomics | 12,733 | 16 | SVM, RF, NLP, XGBoost | 10-Fold CV | AUC = 0.97 | 81% |
Kniep et al., 2019 [34] | Diagnosis | METs | 189 | T1ce, T1W, FLAIR | Multiple metastases | Python | 1423 | 59 | RF | 5-Fold CV | AUC = 0.90 | 72% |
Jeong et al., 2019 [35] | Diagnosis | GBM | 25 (13 HGG, 12 LGG) | T2W FLAIR, T1W | Solid tumor | MATLAB | 1689 | 7 (types) of delta- and radiomic features | RF | LOO CV | AUC = 0.938 | 69% |
Wei et al., 2019 [36] | Diagnosis | GBM | 105 | T1ce, T2-FLAIR & ADC | Tumor & PED | R | 3051 | 100 | LR | No CV | AUC = 0.926 | 78% |
Wening et al., 2019 [37] | Survival | GBM | 211 | Multimodal | ED, ET, NEC | PyRadiomics | 9871 | 95 | LR | No CV | ACC = 0.56 (long, mid, and short-term survival) | 67% |
Kim et al., 2019 [38] | Prognosis | GBM | 83 | T1W, T2W, T1ce, FLAIR, DTI, DSC | NER | ANTsR | 6472 | Top 10 (LASSO) | GLM | 10-Fold CV | CI = 0.87 | 72% |
Prasanna et al., 2019 [39] | Survival | Glioma | 241 | T1c, T2w, and FLAIR | ET, WT, TC | MATLAB | 234 | yes (Top 2) | CNN, RF | 3-Fold CV | ST- 0.57 MT-0.63 LT-0.43 | 56% |
Qian et al., 2019 [40] | Diagnosis | GBM & METs | 412 (GBM 242, 170 METs) | T1W, T1c, T2w | Tumor & peritumoral region | PyRadiomics | 1303 | 12 | SVM, LASSO, MLP, ADaBoost | 5-Fold CV | AUC = 0.95 | 86% |
Carré et al., 2019 [41] | Diagnosis | GBM | 243 (108 grade II and III gliomas, 135 grade IV GBM) | T1w-gd and T2w-flair | OED, NCR, ET | PyRadiomics | 1462 | 91 (18 first-order and 73 second- order) | RF, NB, LR, SVM, NN | 5-Fold CV | ACC = 0.82 (95% CI 0.80–0.8 5, p = 0.005) | 72% |
Shofty et al., 2020 [42] | Diagnosis | METs | 53 | Multi-modal | Brain lesions | MATLAB | 195 | 50 (PCA) | SVM | 5-Fold CV | AUC = 0.78 | 75% |
Sudre et al., 2020 [43] | Diagnosis | Glioma | 333 (101 LGG, 232 HGG | T2 W, FLAIR | Tumor | NiftyReg | Several | 29 (Shape, histogram, Haralick) | RF | 2-Fold CV | AUC = 0.80 | 67% |
Crisi et al., 2020 [44] | Prognosis | GBM | 59 | T1-GRE, T2- GRE, T2FLAIR | ET, NEC | LIFEx | 92 | 14 | NB, DT, MLP | 10-Fold CV | AUC = 0.84 | 47% |
Wei et al. 2020, [45] | Diagnosis | IHPC, meningioma | 292 (IHPC = 155 meningiomas = 137) | T1WI, CE- T1WI, and T2WI | TC and PED | PyRadiomics | 473 | 64 | Recursive feature elimination, RF | 3-Fold CV | AUC = 0.913 (Tr), 0.914 (val) | 86% |
Beig et al., 2020 [46] | Survival | GBM | 203 | Gd-T1W, T2W, FLAIR | NCR, PED, ET | MATLAB | 936 | 25 | Cox regression | 5-Fold CV | ----- | 81% |
Lohmann et al., 2020 [47] | Early progression | GBM | 34 | PET | Tumor | PyRadiomics | 944 | 4 (shape, Histogram, GLSZM) | RF | 5-Fold CV | AUC = 0.79 | 58% |
Correa et al., 2020 [48] | Diagnosis | METs | 37 | post-Gd T1w, T2w, and FLAIR | Lesion and lesion habitat | -------- | 4740 (Haralick, Gabor, Laws, CoLlAGe) | top 3 (Laws) | RF | 3-Fold CV | AUC = 0.97 | 67% |
Kumar et al., 2020 [49] | Prognosis | Glioma | 285 (210 HGG, 75 LGG) | T1, T1c, T2 FLAIR | NET, NCR, ED, ET | PyRadiomics | 1158 | 580 | RF | 5-fold CV | AUC = 0.97 | 58% |
Verma et al., 2020 [50] | Survival | GBM | 156 | Gd-T1W, T2W, FLAIR | ET, NET, NCR | R studio | 3024 (Haralick, Laws, CoLlAGe) | ------ | LASSO | 10-fold CV | CI = 0.80 | 56% |
Choi et al., 2020 [51] | Survival | GBM | 144 | T1W, T2W, T1ce, FLAIR | PED | PyRadiomics | 478 | 7 | Cox-Lasso | 10-fold CV | --- | 75% |
Yousaf et al., 2020 [52] | Survival | GBM | 335 (259 HGG, 76 LGG) | T1W, T2W, T1ce and FLAIR | Tumor | MATLAB | 30,632 | 14 | RF | 10-fold CV | ---- | 53% |
Zhang et al., 2020 [53] | Diagnosis and prognosis | GBM | 104 | T1C, T1, T2, FLAIR | ET, NCR, ED | MATLAB | 180 | ------ | SVM | No CV | ACC = 87.88% | 56% |
Choi et al., 2020 [54] | Diagnosis | GBM | 136 | T2W | Tumor & PED | PyRadiomics | 107 | 9 | Random Forest | No CV | AUC = 0.758 | 83% |
Sakai et al., 2020 [55] | Diagnosis | Glioma | 100 (22 IDH1 mutant, 78 wildtypes | FLAIR, DWI | Tumor | Olea sphere | 92 | ---- | XGBoost | 5-fold CV | AUC = 0.97 | 67% |
Demire et al. 2021 [56] | Diagnosis | GBM & METs | 60 (35 GBM, 25 METs) | T1WI, T2WI, FLAIR, postcontrast T1WI | NEC, NET, ET, Oedema | Third- party | 856 | ----- | SVM, RF, NB | 5-Fold CV | AUC = 0.97 | 50% |
Tixier et al., 2021 [57] | Survival | GBM | 234 | T1W | Gd -ET, NEC, NET, TC | Python | 88 | 57 | Lasso | 5-Fold CV | AUC = 0.75 | 61% |
Russo et al., 2021 [58] | Diagnosis | Glioma | 56 | PET | Tumor | LIFEx | 44 | ----- | NN, RF, SVM | 5-Fold CV | AUC = 0.78 | 50% |
Yan et al., 2021 [59] | Diagnosis | GBM | 41 | T1ce, T1W, T2W, FLAIR | Tumor | CaPTk | 841 | 153 | RF | No CV | ACC = 81% | 64% |
Ye et al., 2021 [60] | Diagnosis and prognosis | GBM | 285 (210 HGG, 75 LGG) | T1W, T2W, T2 FLAIR | GD-ET, PED | PyRadiomics | 94 | Top 30 | RF, KNN, SVM, MLP, CNN | No CV | AUC = 0.65 (short-, mid-, and long-term survival) | 67% |
Joo et al., 2021 [61] | Diagnosis | Meningioma | 454 | T2W, T1ce | Tumor & PED | MATLAB | 3222 | Top 6 | RF | 10-Fold CV | AUC = 0.76 | 56% |
Pasquini et al., 2021 [62] | Diagnosis | High-grade glioma | 156 | T1W, T2W, FLAIR, PWI, DWI | WT, CET, NEC, NET | MATLAB | 1871 | Top 15 | RF | 10-Fold CV | AUC = 74.2% | 56% |
Cao et al., 2021 [63] | Prognosis | Lower-grade glioma | 102 (60 men, 42 women) | T1W, T2W, FLAIR, DWI | WT, NEC | MATLAB | 56 | Top 10 | RF | No CV | AUC = 0.879 | 53% |
Patel et al., 2021 [64] | Prognosis | GBM | 76 | CE-T1W, T2W, DWI | Whole Brain | PyRadiomics | 307 | 6 | RF, NB | 10-Fold CV | AUC = 0.8 | 70% |
Soltani et al., 2021 [65] | Diagnosis and prognosis | GBM | 211 | T1, T1CE, T2, and T2- FLAIR | ED, ET, NEC | PyRadiomics | 3910 | 67 | ANN, KNN, RF | No CV | ACC = 0.57 (short-, mid-, and long-term survival) | 56% |
Wagner et at., 2021 [66] | Prognosis | LGG | 115 | T2-FLAIR, Gd-T1W | Segmented tumor | PyRadiomics | 851 | 10 | RF | 4-fold CV | AUC = 0.75 | 58% |
Le et al., 2021 [67] | Diagnosis and prognosis | Glioma | 120 | T2-FLAIR, Gd-T1W | ET, NET, ED | CaPTk | 704 | 13 | XGBoost | LOO-CV | AUC = 0.85 | 61% |
Kumar et al., 2021, [68] | Diagnosis | Glioma | 369 (293 HGG, 76 LGG) | T2 FLAIR, T1W, postcontrast T1W and | NET, NCR, ED, ET | Python | 428 | ---- | LR, SVM, KNN, ERT | 5-fold CV | AUC = 0.95 | 67% |
Cepeda et al., 2021 [69] | Survival | GBM | 203 | T1CE, T1, T2, FLAIR | Tumor, peritumoral | MATLAB | 15,720 | ---- | Naive Bayes | No CV | AUC = 0.769 | 61% |
Maliket al., 2021 [70] | Diagnosis (clinical study) | LGG & GBM | 78 (42 GBM, 36 LGG) | T1ce, T2-FLAIR, DWI | PED, TC | PyRadiomics | 3822 | 9 (RFE) | SVM, KNN, LDA, AdaBoost | LOO CV | AUC = 0.96 | 67% |
Samani et al., 2021 [71] | Diagnosis | GBM & METs | 106 (66 GBM, 40 METs) | DTI | PTR | PyRadiomics | All first-order features | Top 2% (PCA) | SVM, CNN | 5-fold CV | ACC = 85% | 61% |
Xiao et al., 2021 [72] | Diagnosis | GBM & Brain Abscess | 118 (86 GBM, 32 brain abscess) | T1W, T2W, T1ce, FLAIR | NCR, PED, TC | PyRadiomics | 1004 | 43 (PCA) | RF, LR | 5-fold CV | AUC = 0.89 | 56% |
Gutta et al., 2021 [73] | Diagnosis | Glioma | 237 | T1CE, T1W, T2W, T2- FLAIR | ET, NET & ED | PyRadiomics | 1284 | 45 | SVM, RF | No CV | ACC = 87% | 67% |
Zhang et al., 2021 [74] | Diagnosis | Glioma | 162 | Gd-T1W, T1W, T2W, T2-FLAIR | TC, ED | PyRadiomics | 1102 | Top 10 | autoML | 4-fold CV | AUC = 0.951 | 58% |
Xu et al., 2021 [75] | Prognosis | GBM | 236 | T1, T1-Gd, T2W, T2- FLAIR | ET, ED, NET, NCR | PyRadiomics | 1320 | 45 | Cox regression | 5-fold CV | C-index = 0.64 | 61% |
Meißner et al., 2022 [76] | Survival | METs | 59 | T1CE, T2W | Tumor | PyRadiomics | 1316 | 100 | SVM | 10-fold CV | AUC = 0.92 | 67% |
Shaheen et al., 2022 [77] | Survival | Glioma | 178 | T1W, T2W, T1ce, FLAIR | PTE, NEC, ENC | PyRadiomics | 89 | 50 | SVM | --- | AUC = 0.73 | 61% |
Deng et al., 2022 [78] | Survival | Glioma | 84 | T2W, T1ce, FLAIR | Tumor, NCR, ED | PyRadiomics | 1316 | 12 | RF | ---- | AUC = 0.879 | 61% |
Liu et al., 2022 [79] | Prognosis | GBM | 200 | T1CE, T2 | Tumor and peritumoral region | PyRadiomics | 8412 | Top 20 | RF, SVM | 10-fold CV | AUC = 0.91 | 61% |
Do et al., 2022 [80] | Prognosis | GBM | 53 | T1W, T1Gd, T2, T2-FLAIR | NCR, PED, ET | Python | 704 | 22 | RF, SVM, XGBoost | 5-fold CV | AUC = 0.93 | 50% |
Chiu et al., 2022 [81] | Diagnosis | GBM | 54 | T1Gd, T2W, T2-FLAIR, T1CE | NCR, ET, PED | Python | 1316 | ---- | RF | No CV | AUC = 0.96 | 53% |
Chen et al., 2022 [82] | Diagnosis | Meningioma | 819 | T1W, T2W, T1CE | Solid tumor, NCR | Python | 2942 | top 9 | RF | No CV | AUC = 0.95 | 56% |
Xu et al., 2022 [83] | Prognosis | Glioma | 74 | T1W, T2W- FLAIR, T1CE | Solid tumor | PyRadiomics | 112 | 7 | Stack, KNN, LR, RF, SVM, NB | 5-fold CV | AUC = 0.76 | 67% |
Kumar et al., 2022 [84] | Diagnosis | Glioma | 285 (210 HGG, 75 LGG) | T2W, T1ce, FLAIR | NCR, ET, NET, PED | PyRadiomics | 321 | 42 | RF, DT, SVM, LR | 5-fold CV | AUC = 0.975 | 86% |
Verma et al., 2022 [85] | Survival | GBM | 150 | Gadolinium—T1w, T2w, FLAIR | ET, NCR | MATLAB | 3792 | 316 | ---- | 5-fold CV | AUC = 0.78 | 75% |
Wang et al., 2022 [86] | Diagnosis | METs | 228 | T1ce | Solid tumor and NCR | Python | 960 | 548 (LASSO) | SVM | 5-fold CV | AUC = 0.928 | 53% |
Yang et al., 2022 [87] | Diagnosis | GBM | 187 | T1W, T2W, T1ce, FLAIR | Tumor and PED | PyRadiomics | 190 | Yes (LASSO) | Cox regression | 10-fold CV | CI = 0.658 | 69% |
Liu et al., 2022 [88] | Diagnosis | GBM, MET, and lymphoma | 324 (134 GBM 82 Lymphoma 108 MET) | T2W, T1ce | WT, PED | PyRadiomics | 8412 | Top 20 (LASSO) | RF, linear, AdaBoost | 10-fold CV | AUC = 0.91 | 62% |
2.5. Quality Assessment
3. Results
3.1. Radiomics for Glioma Grading and Differential Diagnosis
3.2. Radiomics for Non-Glial Tumors
3.3. Radiomics for Survival Prediction
3.4. Radiomics for Brain-Habitat Analysis
3.5. Radiomics for Genetic-Mutation-Status Prediction
3.6. Frequently Selected Radiomics Features
3.7. Evaluation Metrics
4. Discussion
4.1. Promises of Radiomics and Machine Learning for Brain Tumor Analysis
4.2. Research Gaps and Future Challenges
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviation
AdaBoost | Adaptive Boosting |
CI | Concordance Index |
CNN | Convolutional Neural Network |
DT | Decision Tree |
DTI | Diffusion Tensor Imaging |
DWI | Diffusion-Weighted Imaging |
ED | Edema |
ERT | Extremely Randomized Trees |
ET | Enhancing Tumor |
GBM | Glioblastoma |
GLM | Generalized Linear Model |
HGG | High-Grade Glioma |
LR | Logistic Regression |
KNN | K- Nearest Neighbour Algorithm |
LASSO | Least Absolute Shrinkage and Selection Operator |
LDA | Linear Discriminant Analysis |
LGG | Low-Grade Glioma |
LOO | Leave One Out |
METs | Metastasis |
MLP | Multi-Layer Perceptron |
mRMR | Minimum Redundancy Maximum Relevance |
NB | Naïve Bayes |
IHPC | Intracranial hemangiopericytoma |
NCR | Necrosis |
NER | Non-Enhancing Region |
NET | Non-Enhancing Tumor |
NN | Neural Network |
PCA | Principal Component Analysis |
PED | Peritumoral Edema |
PTR | Peritumoral Region |
RF | Random Forest |
RFE | Recursive Feature Elimination |
SVM | Support Vector Machine |
T1ce | Contrast-Enhanced T1 Imaging |
XGBoost | eXtreme Gradient Boosting |
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Tabassum, M.; Suman, A.A.; Suero Molina, E.; Pan, E.; Di Ieva, A.; Liu, S. Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review. Cancers 2023, 15, 3845. https://doi.org/10.3390/cancers15153845
Tabassum M, Suman AA, Suero Molina E, Pan E, Di Ieva A, Liu S. Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review. Cancers. 2023; 15(15):3845. https://doi.org/10.3390/cancers15153845
Chicago/Turabian StyleTabassum, Mehnaz, Abdulla Al Suman, Eric Suero Molina, Elizabeth Pan, Antonio Di Ieva, and Sidong Liu. 2023. "Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review" Cancers 15, no. 15: 3845. https://doi.org/10.3390/cancers15153845