Table 1.
Studies using machine learning in the development of glioblastoma monitoring biomarkers.
| Author | Target condition | Reference standard | Dataset(s) | Available demographic information | Methodology | Features selected | Test set performance |
|---|---|---|---|---|---|---|---|
| a Kim J.Y. et al. (34) | Early true progression or Early pseudoprogression | Mixture of histopathology and imaging follow up | Training = 61 Testing = 34 T 1 C, FLAIR, DWI, DSC |
Training = age mean ± SD (range) 58 ± 11 (34–83) male 38 (62%) Testing = age mean ± SD 62 ± 12 male 25 (74%) Data from Korea |
Retrospective 2 centers: 1 train & 1 external test set. LASSO feature selection with 10-fold CV Linear generalized model |
First-order, volume/shape, Second-order (texture), wavelet. ADC & CBV parameters included. |
Recall 0.71 Specificity 0.90 Precision 0.83 BA 0.81 F1 0.77 AUC 0.85 (CI 0.71 – 0.99) |
| Kim J.Y. et al. (35) | Early true progression or Early pseudoprogression | Mixture of histopathology and imaging follow up | Training = 59 Testing = 24 T 1 C, FLAIR, DTI, DSC |
Training = age mean ± SD 61 ± 11 male 37 (63%) Testing = age mean ± SD 59 ± 12 male 9 (38%) Data from Korea |
Retrospective 1 center LASSO feature selection with 10-fold CV Linear generalized model |
First-order, Second-order (texture), wavelet. FA & CBV parameters included. |
Recall 0.80 Specificity 0.63 Precision 0.36 BA 0.72 F1 0.50 AUC 0.67 (0.40 – 0.94) |
| Bacchi S. et al. (36) | True progression or PTRE (HGG) | Histopathology for progression and imaging follow up for pseudoprogression | Training = 44 Testing = 11 T 1 C, FLAIR, DWI |
Combined = age mean ± SD 56 ± 10 male 26 (47%) Data from Australia |
Retrospective 1 center 3D CNN & 5-fold CV |
CNN. FLAIR & DWI parameters |
Recall 1.00 Specificity 0.60 Precision 0.75 BA 0.80 F1 0.86 AUC 0.80 |
| Elshafeey N. et al. (37) | True progression or b PTRE | Histopathology | Training = 98 Testing = 7 DSC, DCE |
Training = age mean ± SD 50 ± 13 male 14 (58%) No testing demographic information Data from USA |
Retrospective 3 centers mRMR feature selection. 1 test. 1) decision tree algorithm C5.0 2) SVM including LOO and 10-fold CV |
Ktrans & CBV parameters | Insufficient published data to determine diagnostic performance (CV training results available recall 0.91; specificity 0.88) |
| Verma G. et al. (38) | True progression or Pseudoprogression | Mixture of histopathology and imaging follow up | Training = 27 3D-EPSI |
Training = age mean ± SD 64 ± 10 male 14 (52%) Data from USA |
Retrospective 1 center Multivariate logistic regression LOOCV |
Cho/NAA & Cho/Cr | No test set (CV training results available recall 0.94; specificity 0.87) |
| Ismail M. et al. (39) | True progression or Pseudoprogression | Mixture of histopathology and imaging follow up | Training = 59 Testing = 46 T 1 C, T 2/ FLAIR |
Training = age mean(range) 61 (26–74) male 39 (66%) Testing = age mean (range) 56 (25–76) male 30 (65%) Data from USA |
Retrospective 2 centers: 1 train & 1 external test set. SVM & 4-fold CV |
Global & curvature shape | Recall 1.00 Specificity 0.67 Precision 0.88 BA 0.83 F1 0.94 |
| a Bani-Sadr A. et al. (40) | True progression or Pseudoprogression | Mixture of histopathology and imaging follow up | Training = 52 Testing = 24 T 1 C, FLAIR MGMT promoter status |
Combined = age mean ± SD 58 ± 11 male 45 (59%) Data from France |
Retrospective 1 center Random Forest. |
Second-order features +/- MGMT promoter status |
Recall 0.94 (0.71 - 1.00) Specificity 0.38 (0.09 - 0.76) Precision 0.36 BA 0.66 F1 0.84 AUC 0.77 & non-MRI: Recall 0.80 (0.56 - 0.94) Specificity 0.75 (0.19 - 0.99) Precision 0.86 BA 0.74 F1 0.83 AUC 0.85 |
| Gao X.Y. et al. (41) | True progression or PTRE (HGG) | Mixture of histopathology and imaging follow up | Training = 34 Testing = 15 (per lesion) T 1 C, FLAIR |
Combined = age mean ± SD 51 ± 11 male 14 (36%) (per patient) Data from China |
Retrospective 2 centers SVM & 5-fold CV |
T
1 C, FLAIR subtraction map parameters |
Recall 1.00 Specificity 0.90 Precision 0.83 BA 0.95 F1 0.91 AUC 0.94 (0.78 – 1.00) |
| Jang B-S. et al. (42) | True progression or Pseudoprogression | Mixture of histopathology and imaging follow up | Training = 59 Testing = 19 T 1 C & clinical features & IDH/MGMT promoter status |
Training = age median (range) 56 (22–77) male 41 (70%) Testing = age mean ± SD 53 (28–75) male 10 (53%) Data from Korea |
Retrospective 2 centers 1 train & 1 external test set. CNN LSTM & 10-fold CV (compared to Random Forest) |
CNN T
1 C parameters +/- Age; Gender; MGMT status; IDH mutation; radiotherapy dose and fractions; follow-up interval |
Recall 0.64 Specificity 0.50 Precision 0.64 BA 0.57 F1 0.63 AUC 0.69 & non-MRI: Recall 0.72 Specificity 0.75 Precision 0.80 BA 0.74 F1 0.76 AUC 0.83 |
| Li M. et al. (43) | True progression or b PTRE | Imaging follow up | Training = 84 DTI |
No demographic information Data from USA |
Retrospective. 1 center DC-AL GAN CNN with SVM including 5 and 10 and 20-fold CV (compared to DCGAN, VGG, ResNet, and DenseNet) |
CNN. DTI | No test set (CV training results only available: Recall 0.98 Specificity 0.88 AUC 0.95) |
| Akbari H. et al. (44) | True progression or Pseudoprogression | Histopathology | Training = 40 Testing = 23 Testing = 20 T 1 C, T 2/FLAIR, DTI, DSC, DCE |
Combined internal = age mean (range) 57 (33–82) male 38 (60%) No external demographic information Data from USA |
Retrospective 2 centers. 1 train & test. 1 external test set. imagenet_vgg_f CNN SVM & LOOCV |
First-order, second-order (texture). CBV, PH, TR, T 1 C, T 2/FLAIR parameters included. |
Recall 0.70 Specificity 0.80 Precision 0.78 BA 0.75 F1 0.74 AUC 0.80 |
| Li X. et al. (45) | Early True progression or early pseudoprogression (HGG) | Mixture of histopathology and imaging follow up | Training = 362 T 1 C, T 2, multi-voxel & single-voxel 1H-MRS, ASL |
Training = age mean (range) 50 (19–70) male 218 (60%) Data from China |
Retrospective Gabor dictionary and sparse representation classifier (SRC) |
Sparse representations | No test set (CV training results only available: Recall 0.97 Specificity 0.83) |
| Manning P et al. (46) | True progression or pseudoprogression | Mixture of histopathology and imaging follow up | Training = 32 DSC, ASL |
Training = age mean ± SD 56 ± 13 male 22 (69%) Data from USA |
Retrospective 1 center Linear discriminant analysis & LOOCV |
CBF and CBV parameters included. | No test set (CV training results only available: Recall 0.92 Specificity 0.86 AUC 0.95) |
| Park J.E. et al., 2020 (47) | Early True progression or early pseudoprogression | Mixture of histopathology and imaging follow up | Training = 53 Testing = 33 T 1 C |
Training = age mean ± SD 56 ± 11 male 31 (59%) Testing = age mean ± SD 62 ± 12 male 25 (76%) Data from Korea |
Retrospective 2 centers. 1 train & test. 1 external test set. Random Forest feature selection with 10-fold CV (Automated segmentation) |
First-order, volume/shape, Second-order (texture), wavelet parameters included. | Recall 0.61 Specificity 0.47 Precision 0.58 BA 0.54 F1 0.59 AUC 0.65 (0.46 – 0.84) |
| Lee J. et al. (48) | True progression or b PTRE (HGG) | Histopathology | Training = 43 T 1, T 1 C, T 2, FLAIR, (subtractions: T 1 C - T 1, T 2- FLAIR) ADC parameters. |
Training =age mean ± SD (range) 52 ± 13 (16–74) male 24 (56%) Data from USA |
Retrospective 1 center CNN-LSTM. 3-fold CV |
CNN-LSTM parameters. | No test set (CV training results only available: AUC 0.81 (0.72 - 0.88)) |
| Kebir S. et al. (49) | True progression or b PTRE | Imaging follow up | Training = 30 Testing = 14 O-(2[18F]-fluoroethyl)-L-tyrosine (FET) |
Combined = age mean ± SD (range) 57 ± 11 (34-79) male 34 (77%) Data from Germany |
Retrospective 1 center Linear discriminant analysis. 3-fold CV |
TBRmean
TBRmax TTPmin parameters. |
Recall 1.00 Specificity 0.80 Precision 0.90 BA 0.92 F1 0.95 AUC 0.93 (0.78 - 1.00) |
| Cluceru J. et al. (50) | Early True progression or early pseudoprogression (HGG) | Histopathology | Training = 139 DSC, MRSI, DWI, DTI |
Training = age median (range) 52 (21–84) Male 83 (60%) Data from USA Ethnicity: White 112 (80%) American Indian 1 (1%) Asian 6 (4%( Pacific Islander 2 (1%) Other 18 (13%) |
Retrospective 1 center Multivariate logistic regression. 5-fold CV |
Cho, Cho/Cr, Cho/NAA & CBV parameters. | No test set (CV training results only available: Recall 0.65 (0.33 - 0.96); Specificity 0.62 (0.21 - 1.00) AUC 0.69 (0.51 - 0.87)) |
| Jang B.S. et al. (51) | True progression or b PTRE | Mixture of histopathology and imaging follow up (including PET) | (i) (trained model = 78) testing = 104 (ii) all training = 182 T 1 C & clinical, molecular, timings, radiotherapy data |
Testing = age median (range) 55 (25-76) male 59 (67%) Data from Korea |
Retrospective (i) 6 centers 1 external test set. CNN LSTM (ii) 7 centers 1 training set CNN LSTM & 10-fold CV |
CNN T 1 C parameters and Age; Gender; MGMT status; IDH mutation; radiotherapy dose and fractions; follow-up interval | (i) Insufficient published data to determine diagnostic performance (ii) No test set (CV training results available AUPRC 0.87) |
a
Within publication some data appears mathematically discrepant.
b
Within publication discrepant or unclear information (e.g. interval after radiotherapy).
Unless otherwise stated, glioblastoma alone was analyzed.
PTRE, post-treatment related effects; HGG, high-grade glioma.
MRI sequences: T1 C, postcontrast T1-weighted; T2, T2-weighted; FLAIR, fluid-attenuated inversion recovery; DSC, dynamic susceptibility-weighted; DCE, dynamic contrast-enhanced; DWI, diffusion-weighted imaging; DTI, diffusor tensor imaging; ASL, arterial spin labelling; MRI parameters: ADC, apparent diffusion coefficient; FA, fractional anisotropy; TR, trace (DTI); CBV, cerebral blood volume; PH, peak height; Ktrans, volume transfer constant.
Magnetic resonance spectroscopy: 1H-MRS, 1H-magnetic resonance spectroscopy; 3D-EPSI, 3D echo planar spectroscopic imaging.
1H-MRS parameters: Cr, creatine; Cho, choline; NAA, N-acetyl aspartate.
Nuclear medicine: TBR, tumor-to-brain ratio; TTP, time-to-peak.
Molecular markers: MGMT, O6-methylguanine-DNA methyltransferase; IDH, isocitrate dehydrogenase.
Machine learning methodology: CV, cross validation; LOOCV, leave-one-out cross validation; SVM, support vector machine; CNN, convolutional neural network; LASSO, least absolute shrinkage and selection operator; LSTM, long short-term memory; mRMR, minimum redundancy and maximum relevance; VGG, Visual Geometry Group (algorithm); DCGAN, deep convolutional generative adversarial network; DC-AL GAN, DCGAN with AlexNet.
Statistical measures: CI, confidence intervals; BA, balanced accuracy; AUC, area under the receiver operator characteristic curve; AUPRC, area under the precision-recall curve.