Due to a production error, there was an error in the published Table 1 . The 4th row of the table started at the second column instead of the first column, causing the contents of the last column to move to the next row, resulting in a formatting error. The corrected Table 1 appears below.
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) |
Within publication some data appears mathematically discrepant.
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.
The publisher apologizes for this error. The original version of this article has been updated.