Table 1 Normalization methods and grey level discretization applied in recent radiomics studies dedicated to brain tumors.

Su et al.¹⁵

No

100

T2w-flair

–

–

Pyradiomics

18 first-order, 13 shape, 54 texture

Investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning approach to the MR radiomics features of patients with midline gliomas

Liu et al.¹⁶

Yes

130

T1w, T2w-fl1air

ComBat

–

Artificial Intelligence Kit (GE)

First-order, texture

Develop and validate a model that can be used to predict the individualized treatment response in children with cerebral palsy

Bologna et al.¹⁷

–

Phantom

T1w, T2w

Z-Score

32 FBN

Pyradiomics

18 first-order, 14 shape, 75 texture

Analysis of virtual phantom for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain

Elsheikh et al.¹⁸

Yes

135

T1w, T1w-gd, T2w, T2w-flair

–

–

–

First-order, texture

Analysis of multi-stage association of glioblastoma gene expressions with texture and spatial patterns

Tixier et al.¹⁹

Yes

90

T1w-gd, T2w-flair

–

128 FBN

CERR

72 features (first-order, texture, shape)

Study the impact of tumor segmentation variability on the robustness of MRI radiomics features

Ortiz-Ramón et al.²⁰

No

200

T1w, T2w, T2w-flair

–

32 FBN

MATLAB

114 textures

Identify the presence of ischaemic stroke lesions by means of texture analysis on brain MRI

Vamvakas et al.²¹

No

40

T1w, T1w-gd, T2w, T2w-flair

–

–

MATLAB

11 first-order, 16 texture

Investigate the value of advanced multiparametric MRI biomarker analysis based on radiomics features and machine learning classification for glioma grading

Tixier et al.²²

Yes

159

T1w, T1w-gd, T2w-flair

–

128 FBN

CERR

286 features (first-order, shape, texture)

Evaluate the capacity of radiomics features to add complementary information to MGMT status, to improve the ability to predict prognosis

Wu et al.²³

Yes

126

T1w, T1w-gd, T2w, T2w-flair

–

–

–

704 features (first-order, shape, texture)

Identify the optimal radiomics-based machine learning method for isocitrate dehydrogenase genotype prediction in diffuse gliomas

Artzi et al.²⁴

No

439

T1w-gd

WhiteStripe

–

MATLAB

757 features (first-order, shape, texture)

Differentiate between glioblastoma and brain metastasis subtypes using radiomics analysis

Kniep et al.²⁵

No

189

T1w, T1w-gd, T2w-flair

WhiteStripe

–

Pyradiomics

18 first-order, 17 shape, 56 texture

Investigate the feasibility of tumor type prediction with MRI radiomics image features of different brain metastases in a multiclass machine learning approach for patients with unknown primary lesion at the time of diagnosis

Sanghani et al.²⁶

Yes

163

T1w, T1w-gd, T2w, T2w-flair

–

–

Pyradiomics

2200 features (first-order, shape, texture)

Predict overall survival in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning

Liu et al.²⁷

Yes

84

T2w

Z-Score

–

MATLAB

131 features (first-order, shape, texture)

Develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and investigate the genetic background behind the radiomics signature

Peng et al.²⁸

No

66

T1w-gd, T2w-flair

–

64 FBN

MATLAB

51 features (first-order, shape, texture)

Distinguish true progression from radionecrosis after stereotactic radiation therapy for brain metastases with machine learning and radiomics

Bae et al.²⁹

No

217

T1w-gd, T2w-flair

WhiteStripe

–

Pyradiomics

796 features (first-order, shape, texture)

Investigate whether radiomics features based on MRI improve survival prediction in patients with glioblastoma multiforme (GBM) when they are integrated with clinical and genetic profiles

Chen et al.³⁰

Yes

220

T1w, T1w-gd, T2w, T2w-flair

Nyul

–

Pyradiomics

420 features (first-order, shape, texture)

Classify gliomas combining automatic segmentation and radiomics