Table 4 Current precision diagnosis and treatment approaches using radiomics on standard of care MRI sequences in patients with glioblastoma

2008, Diehn

T1, T1+

T2

T, 22 pts

V, 110 pts

-10 binary imaging traits (enhancement, necrosis, mass effect, T2 edema, cortical involvement, SVZ involvement, C:N ratio, contrast/T2 ratio, T2 edema, T2 heterogeneity).

- Unsupervised hierarchical clustering, Spearman rank-correlation coefficient.

- Associations between angiogenesis, tumor hypoxia, and the contrast enhancement imaging phenotype; proliferation gene-expression signature and mass effect phenotype; EGFR protein overexpression and contrast:necrosis imaging trait.

2011, Zinn

FLAIR

T, 26 pts

V, 26 pts

- Quantitative models of edema/invasion, enhancing tumor, necrosis.

- Comparative marker selection, ingenuity pathway analysis.

- Imaging traits associated with upregulation of mRNA involved in cellular migration/invasion (PERIOSTIN),which was seen to correlate with decreased survival.

2014, Rahman

ADC-/+

T2/FLAIR

T, 91 pts

- 6 variables extracted from histograms of apparent diffusion coefficient were measured at three times (baseline, post-treatment and change).

- Cox proportional hazards model adjusted for clinical variables.

- ADC histogram analysis within both enhancing and nonenhancing components of tumor can be used to stratify for PFS and OS in patients with recurrent glioblastoma treated with Bevacizumab.

2014, Jamshidi

T1, T1+

T2

Flas

T, 23 pts

- (1) infiltrative versus edematous T2 abnormality, (2) degree of contrast enhancement, (3) necrosis, (4) supraventricular zone (SVZ) involvement, (5) mass effect, and (6) contrast-to-necrosis ratio.

- Resampling statistics, analysis of variance, Pearson correlation coefficient.

- Gene-to-trait associations were found such as contrast-to-necrosis ratio with KLK3 and RUNX3, SVZ involvement with the Ras oncogene family and the metabolic enzyme TYMS, and vasogenic edema with the oncogene FOXP1 and PIK3IP1.

2015, Lee

T1+Flair

T, 65 pts

- 36 spatial habitat diversity (regions with distinctly different intensity characteristics) features based on pixel abundances w/in ROIs.

- Overall coefficient of variation, symbolic regression method.

- Association with OS and EGFR+ status

- Could be a useful prognostic tool for MRIs of patients with glioblastomas.

2016, Kickingereder

T1, T1+

Flair

T, 112 pts

V, 60 pts

- 4842 total

- 17 first-order features, 9 volume and shape features, 162 texture features.

- Supervised principal component analysis, Cox proportional hazard models, integrated Brier scores.

- An 72-feature radiomics-based classification of recurrent glioblastoma permits the prediction of treatment outcome to antiangiogenic therapy through PFS and OS.

2016, Kickingereder

T1+

Flair

T, 79 pts

V, 40 pts

- 12,190 indexes

- Supervised principal component analysis.

- An 11-feature radiomic signature allowed prediction of PFS and OS, stratification of patients with newly diagnosed glioblastoma, and improved performance compared with that of established clinical and radiologic risk models.

2016, Grossmann

T1+

FLAIR

T, 144 pts

(gene, 91 pts)

- Volumetric features such as the necrotic core, contrast enhancement, abnormal tumor volume, tumor-associated edema, and total tumor volume (TV), as well as ratios of these tumor components.

- Spearman rho, C-index, Noether test.

- Association of imaging features with immune response pathways and apoptosis, signal transduction and protein folding processes, homeostasis and cell cycling pathways, as well as OS.

2016, McGarry

T1, T1+

ADC

FLAIR

T, 81 pts

- Map containing 81 (3⁴) potential voxel-wise codes. A 4-digit code was assigned to each voxel. The digit order chosen was T1, ADC, T1+, and FLAIR. Codes ranged from 1111 (dark voxels on all images) to 3333.

- Log-rank Kaplan-Meier survival analysis, Cox proportional hazards model, combined classifier.

- Radiomic signature predicted poorer prognosis at tumor diagnosis in newly diagnosed glioblastoma

2017, Prasanna

T1

FLAIR

T2

T, 65 pts

- 402 radiomic features were obtained for each region: enhancing lesion, peritumoral brain zone and tumor necrosis.

- Redundancy maximum relevance feature selection , random forest (RF) classifier, threefold cross-validation.

- Ten radiomic “peritumoral” MRI features, suggestive of intensity heterogeneity and textural patterns, were predictive of survival on treatment-naïve pre-operative glioblastoma.

2017, Yu

FLAIR

T, 110 pts

V, 30 pts

- 671 high-throughput features were extracted from grade II glioma.

- Classification by support vector machine and AdaBoost, leave-one-out cross-validation.

- 110 features were selected for the noninvasive IDH1 status estimation of grade II glioma.

2017, Xi

T1, T1+

T2

T, 98 pts

V, 20 pts

- 1665 imaging features

- Reduced using LASSO regularization, classification by support vector machine.

- The best classification system for predicting MGMT promoter methylation status in preoperative gliobastoma originated from the combination of 36 T1, T2, and enhanced T1 images features.

2017, Kickingereder

T1, T1+

FLAIR

T2

T, 120 pts

V, 60 pts

- 1043 imaging features

- Penalized Cox model with 10-fold cross-validation.

- The 8-feature radiomic signature increased the prediction accuracy for PFS and OS beyond the assessed molecular, clinical, and standard imaging parameters in newly diagnosed glioblastoma prior to standard-of-care treatment.

2017, Li

T1+

T, 96 pts

- 555 imaging features

- Student’s tests (t test)

- Glioblastoma in different age groups (< 45 and ≥ 45 years old) present different radiomics-feature patterns, suggesting different pathologic, protein, or genic origins.

- 101 features showing the consistency with the age groups, and unsupervised clustering results of those features also show coherence with the age difference.

2017, Grossmann

T1+

FLAIR

T, 126 pts

V, 165 pts

- 65 imaging features from T1 and FLAIR scans at baseline (pretreatment) and follow-up after 6 weeks (post treatment initiation)

- Unbiased unsupervised feature selection (PCA), selection of variant features (coefficient of variation).

- Minimal redundancy maximal relevance algorithm, Cox proportional hazards model for PFS or OS.

- Multivariable analysis of features derived at baseline imaging resulted in significant stratification of OS and PFS.

- These stratifications were stronger compared with clinical or volumetric covariates prognostic value for survival and progression in patients with recurrent glioblastoma receiving bevacizumab treatment.

2017, Kanas

T1+

FLAIR

T, 86 pts

- 10 quantitative variables and 24 qualitative variables were calculated from the volumes of three distinct regions: edema/invasion, tumor enhancement (tumor), and necrosis.

- Isometric feature mapping, locally linear embedding, Laplacian eigenmaps, linear discriminant analysis, factor analysis, principal components analysis, stochastic proximity embedding, random forest, k-nearest neighbors, Gaussian naive Bayes, and the J48 tree.

- The status of MGMT promoter methylation was predicted with an accuracy of up to 73.6%.

- Experimental analysis showed that the edema/necrosis volume ratio, tumor/necrosis volume ratio, edema volume, and tumor location and enhancement characteristics were the most significant variables in respect to the status of MGMT promoter methylation in glioblastoma.

2010, Drabycz

T1+

T2

FLAIR

T, 59 pts

- 4 visual qualitative texture features (cysts, ring/nodular enhancement, margins, homogeneity), volume, 11 regions/sectors features and space–frequency texture analysis based on the S-transform.

- Two-way repeated-measures analysis of variance (ANOVA) tests.

- Ring enhancement assessed visually is significantly associated with unmethylated MGMT promoter status.

-Texture features on T2 images assessed by the space–frequency analysis were significantly different between methylated and unmethylated cases.