|Year, Author||Sequence||Training and Validation set||Extracted radiomics features, selection, and statistical learning||Biologic correlation and relevance|
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.|
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.|
|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.|
|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.
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.|
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.|
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.|
|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|
|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.|
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.|
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.|
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.
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.
|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.
|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.