Table 4:
Studies investigating the use of ML to differentiate between types of brain tumors
Study | Purpose | Number of Patients | Findings |
---|---|---|---|
Kim et al.56 (2018) | Differentiate GBM vs. primary central nervous system lymphoma (PCNSL) using multiparametric MRI-based radiomics | 143 patients (n = 86 training; n = 57 validation) |
|
Shrot et al.57 (2019) | Differentiating different brain tumors using basic and advanced MRI-based radiomics | 141 patients (41 GBM, 38 METS, 50 meningioma & 12 PCNSL) |
|
Niu et al.58 (2019) | Differentiating between different meningioma subtypes using basic MRI-based radiomics | 241 patients (n = 80 meningiothelial meningioma, n = 80 fibrous meningioma, n = 81 transitional meningioma) |
|
Nakagawa et al.59 (2018) | Differentiating GBM vs PCNSL using ML method based on texture features in multiparametric MRI | 70 patients |
|
Dong et al.60 (2019) | Differentiating between pilocytic astrocytoma (PA) and GBM using MRI quantitative radiomic features by a decision tree model | 66 patients (PA n = 31; GBM n = 35) |
|
Zhang et al.61 (2018) | Using MRI-based radiomics to differentiate between non-functioning pituitary adenoma subtypes | 112 patients (training set n = 75; test set n = 37) |
|
Chakrabarty et al.62 (2021) | Train a CNN to differentiate between tumor types (HGG, LGG, metastases, meningioma, pituitary adenoma, acoustic neuroma & healthy tissue) | 1373 (BraTs, TCGA, LGG-1p19q dataset, internal and external dataset) |
|
Qian et al.53 (2019) | To identify the optimal radiomic ML classifier for differentiating GBM vs METS | 412 |
|
Artzi et al.54 (2019) | To differentiate between GBM and METS using CE-T1WI MRI-based radiomics | 439 |
|
Kniep et al.55 (2018) | Using multiparametric MRI-based radiomics to predict tumor type in brain metastasis (SCLC, BC, MM, GC and NSCLC) | 189 |
|