Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology
Abstract
:1. Introduction
2. Radiogenomics Workflow
2.1. Image Acquisition
2.2. Pre-Processing of Data
2.3. Segmentation and Identification of Regions of Interest (ROI)
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- Thresholding method: starting from a grayscale image, thresholding returns a binary image [48];
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- Clustering: a more elaborate procedure that allows the determination, starting from a set of data, of groups with “similar” characteristics;
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- Edge-based method: emphasizes areas of abrupt change within a digital image (for example, discontinuity in the physical properties of tissues), which, generally, reflect changes in the physical status of the tissues [49];
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- Region growing: a simple region-based segmentation method, based on the selection of pixels that are similar and, therefore, can be classified as appertaining to the same tumoral subregion [50];
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- Watershed algorithm: a unique segmentation tool where gray levels and voxels are classified by their intensity or gradient in a topographical map, with ridges and valleys;
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- Atlas method: a tumor-free reference MRI is used to contour the MRI image containing the tumor volume [51].
- The first step consists of BET (brain extraction tool). It is a procedure where a first segmentation, which includes brain tissue and beyond, is performed. All structures which do not contain only brain tissue and which can cause biases (eyes, muscle, base of neck, scalp, fat, cerebrospinal fluid) are eliminated with a completely automatic algorithm;
- The second step consists of FAST (FMRIB’s automated segmentation tool), that is, the segmentation of the brain volume previously extracted with the BET. FAST is a package, included in the FSL software, for segmentation of the brain volume into the three different tissues (gray matter, white matter and CSF, the latter exclusively contained within the volume extracted with the BET), including algorithms for spatial intensity corrections (also called bias fields).
- SPM software (current version: SPM12) [52] uses a tool named optimized voxel-based morphometry which was developed at the Institute of Neurology at University College of London (UCL Queen’s Square Institute of Neurology, Queen’s Square House, Queen’s Square, London, WC1N 3BG, UK) and is available from the web.
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- Tools for brain surface extraction, bias field correction, voxel classification, cerebrum labeling, and surface generation;
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- Tools for processing of diffusion data including tensor fitting and tractography;
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- Sophisticated tools for visualizing and exploring MRI data, diffusion data, tractography and connectivity.
2.4. Feature Extraction
- (a)
- Texture features, describing the variation of gray level values within the tumor;
- (b)
- Shape features, describing form and geometrical properties of the region of interest, such as surface, volume, compactness, diameter and sphericity;
- (c)
- Histogram-based features, calculated starting from the histogram that describes the distribution of pixels in the ROI, the mean, median, maximum, minimum values of the voxel intensities on the image, asymmetry, kurtosis (flatness), uniformity, and entropy;
- (d)
- Second-order features derived from the gray-level co-occurrence matrix, quantifying the incidence of voxels with same intensity;
- (e)
- Higher order features: features that describe the relationships between two or more pixels of the ROI, obtained after applying filters (e.g., wavelet transform, Laplacian transform, Gaussian filter, etc.) or mathematical transform to the pictures [61].
2.5. Methods for Dimensionality Reduction and Feature Selection
2.6. Classification of Radiomic Features and Informatic Analysis: Machine Learning and Deep Learning
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- It is not necessary to segment the tumor;
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- It is not necessary to explicitly define the features to be calculated;
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- It is not necessary to select the features.
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- Larger input data are needed;
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- Problems of interpretability.
2.7. ROC Curve and Model Validation
3. Radiogenomics of Glioblastoma
3.1. Prediction of IDH Mutational Status
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- Gliomas harboring IDH mutations occurred, more frequently, in the frontal lobe, adjacent to the rostral extension of the lateral ventricles;
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- A larger tumor volume in T2 sequences and a higher volume ratio between T2 and T1 sequences with contrast agents were observed in IDH mutant tumors, together with the presence of a high portion of non-enhancing tumor and a central necrotic cystic area with low T1 and FLAIR suppression. A larger portion of enhancing tumor with peripheral enhancement and an infiltrative pattern of edema, instead, was strongly associated with IDH wild-type genotype;
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- In diffusion imaging, a higher mean ADC value was observed in IDH mutated tumors;
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- Tumor vascularity, neoangiogenesis and vessels distribution, reflected by the parameter rCBV, were much less represented in IDH-mutated tumors than in the wild-type counterpart. Consequently, IDH-mutated gliomas exhibited significantly lower rCBV values relative to their wild-type counterpart;
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- Higher skewness and kurtosis were associated with IDH mutational status;
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- Approaches based on multimodal combination of CBV and ADC seemed to lead to better results for predicting IDH status and GBM aggressiveness [76];
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- Radiomics models combining data from multiple tumor regions, for example, core, whole tumor and peritumoral edema region, were more accurate in IDH prediction, especially if the analysis was integrated with clinical data (age, performance status, surgery);
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- ML-based approaches integrating clinical data (mainly age, significantly lower in IDH-mutated tumors) with the most predictive radiological features (frontal tumor location, ADC andT2/FLAIR volume) achieved the best accuracy in the prediction of IDH genotype;
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- DL approaches using DSC perfusion MRI images accurately predicted the IDH mutational status [76];
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- Approaches based on 2-HG MRS techniques also achieved adequate accuracy, sensitivity, and specificity in the prediction of the IDH status.
3.2. Prediction of MGMT Promoter Methylation Status
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- MGMT methylated tumors were localized in the left hemisphere, especially in the left temporal lobe. In contrast, MGMT unmethylated tumors tended to be localized in the right hemisphere, in the right frontal lobe or in proximity to the SVZ;
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- MGMT unmethylated tumors tended to exhibit more homogenous contrast enhancement, while MGMT methylated tumors were characterized by ring contrast enhancement, with central necrosis and decreased peritumoral edema;
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- In T2/FLAIR images, MGMT methylated tumors had a lower hyperintense tumor volume, in contrast with unmethylated tumors;
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- In diffusion imaging, increased minimum ADC values and higher ADC ratio were associated with MGMT promoter methylation;
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- In perfusion imaging, higher rCBV was associated with MGMT promoter methylation;
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- Multi-habitat MRI and comprehensive multi-omics models integrating radiomic features (possibly from both the tumor and the edema areas), clinical variables, and genetic data achieved the best accuracy for determining MGMT methylation status [128].
3.3. Discrimination of Pseudoprogression from Early Progression
3.4. Survival Prognostication
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Study | MRI Technique | IDH1 Mutant MRI Phenotype/Predicitive Features | Number of Patients | Performance AUC/Accuracy Value |
---|---|---|---|---|
Tejadaa Nejra et al., 2018 [91] | VLSM analysis of MRI images | Frontal lobe location, adjacent to the rostral extension of the lateral ventricles | 237 | Permutation-adjusted p-value = 0.021 |
Chang et al., 2018 [92] | T2, FLAIR, and T1 pre and postcontrast | Absent or minimal areas of enhancement, central areas of cysts with low T1 and FLAIR suppression, and well-defined tumor margins | 259 | 94% accuracy |
Hong et al., 2018 [93] | T2 and T1CE and DWI | Larger volume on T2 and a higher volume ratio between T2 and T1CE; higher mean ADC | 176 | AUC was 0.48 for T2 volume; 0.73 for T2-T1 volume ratio; 0.65 for ADC mean |
Li et al., 2018 [94] | T1, T1CE, T2 and FLAIR | The multiregional model built with all-region features performed better than the single-region models, while combining age with all-region features achieved the best performance | 225 | AUC 0.96 |
Xing et al., 2017 [95] | DWI, DSC-PWI and conventional MRI imaging | Minimum ADC and relative ADC significantly higher; relative maximum CBV <2.35 predictive of IDH mutation | 42 | AUC was 0.87 for minimum ADC, 0.84 for relative ADC and 0.82 for relative maximum CBV |
Wu et al., 2018 [96] | Conventional MRI imaging | Higher enhancement, necrosis and edema, and a higher mean relative ADC | 131 | AUC 0.79 |
Zhang et al., 2016 [97] | Machine learning algorithm to generate a model predictive of IDH genotype based on the integration of clinical features and conventional MRI features (Statistics and Machine Learning Toolbox MATLAB 2015a) | Top features resulted were age and MRI parametric intensity, texture, and shape features | 120 | AUC 0.92 |
Choi et al., 2019 [98] | T1, T2, T2-FLAIR, T1CE, DSC perfusion MRI | The recurrent neural network model (RNN) accurately predicted the IDH status using DSC perfusion MRI | 463 | AUC 0.96 for GBM patients |
Kickingereder et al., 2015 [99] | T1 images both before and after administration of gadoterate meglumine (Dotarem, Guerbet) as well as axial FLAIR and axial T2 images | Lower rCBV | 181 | 92.2% accuracy |
Yamashita et al., 2015 [100] | T1CE, precontrast T1 spin-echo, T2-turbo spin-echo, FLAIR and DWI | Higher absolute tumor blood flow, relative tumor blood flow, necrosis area, and percentage of cross-sectional necrosis area inside the enhancing lesion. No significant difference in the ADC minimum and ADC mean | 66 | AUC for absolute tumor blood flow, relative tumor blood flow, percentage of cross-sectional necrosis area inside the enhancing lesion, and necrosis area were 0.850, 0.873, 0.739, and 0.772, respectively |
Sudre et al., 2020 [101] | Machine learning assisted DSC-MRI using random forest classifier | Lower tumor surface to volume ratio (SAV) and measure of non-compactness; higher skewness and kurtosis; higher correlation and sum entropy | 333 | Overall specificity of 77% and sensitivity of 65% |
Bangalore Yogananda et al., 2019 [102] | MRI-based deep learning 3D-Dense-UNets | High IDH classification accuracy of T2w image-only network (T2-net) | 214 | T2-net demonstrated AUC of 0.98 ± 0.01 |
Study | MRI Technique | MGMT Methylated Tumors MRI Phenotype/Predicitive Features | Number of Patients | Performance AUC/Accuracy Value |
---|---|---|---|---|
Chang et al., 2018 [92] | T1, T1CE, T2, T2 FLAIR | Heterogeneous, nodular enhancement; presence of an eccentric cyst; edema with cortical involvement; frontal and superficial temporal predominance | 259 patients | Accuracy 83% |
Korfiatis et al., 2016 [124] | T2-fast spin-echo, axial T1 and T1CE. Two supervised machine-learning classifiers were used to predict MGMT methylation status: SVM-based classifier and random forest | The best-performing classification system resulted from SVM with features extracted from T2 images | 155 | AUC 0.85 |
Moon et al., 2012 [125] | Axial T1, axial T2-fast spin-echo sequence, axial FLAIR, axial T2-gradient-echo sequence | Higher ADC value and higher ADC ratio in the methylated group; rCBV ratio did not differ between the two groups | 38 | ADC values tended to be higher in the methylated group. ADC ratio was significantly higher in the methylated group. rCBV ratio did not differ between the two groups (p = 0.380) |
Wei et al., 2019 [126] | T1CE, T2 FLAIR and DWI | A fusion radiomics signature combining four single radiomics signatures (T1-WI-tumor, T1-WI-edema, T2-FLAIR-tumor, and T2-FLAIR-edema) showed optimal performance in predicting the MGMT methylation status | 105 | AUC of 0.925 in the training cohort and 0.902 in the validation cohort |
Han et al., 2018 [127] | Diffusion-weighted (DWI) and 3-diminsional pseudo-continuous arterial spin labeling (3D pCASL) imaging | MGMT promoter methylation was associated with tumor location and necrosis (p < 0.05). Increased ADC value (p < 0.001) and decreased rCBF (p < 0.001) were associated with MGMT promoter methylation. ADC achieved better predicting efficacy than rCBF (ADC: AUC, 0.860; vs. rCBF: AUC, 0.835) The combination of tumor location, necrosis, ADC and rCBF resulted in the highest performance in predicting the MGMT promoter methylation | 92 | The combination of tumor location, necrosis, ADC and rCBF resulted in the highest AUC of 0.914 |
Chen et al., 2020 [128] | Deep learning model analyzing contrast-enhanced T1images, FLAIR images | FLAIR images showed the better tumor segmentation performance and the better MGMT status prediction | 106 patients | Accuracy = 0.827 ± 0.056 |
Study | MRI Technique | Survival Prognostication | Number of Patients | Performance AUC/Accuracy Value |
Beig et al. [145] | T1, T2, T2 FLAIR | Use of 25 radiomic features from the tumor habitat predicted PFS | 203 | p < 0.0001 on the training set and p = 0.03 on the test set |
Jain et al. [146] | Dynamic susceptibility contrast-enhanced T2-weighted perfusion MR imaging | Worsening OS and PFS were associated with increasing relative cerebral blood volume obtained from the non-enhancing region of GBM | 45 | OS (p = 0.0103); PFS (p = 0.0223) |
Choi et al. [147] | T2, T2 FLAIR, T1CE | Radiomics added to the clinical model achieved the best performance in PFS and OS prognostication | 120 | AUC = 0.66 for PFS AUC = 0.73 for OS |
Kazerooni et al. [148] | Pre-operative MRI acquisition on a 3 Tesla scanner | Multi-omics data (clinical, radiomic and genetic data) achieved better performance in predicting OS | 516 | AUC = 0.78 in the discovery cohort AUC = 0.75 in the replication cohort |
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Gatto, L.; Franceschi, E.; Tosoni, A.; Di Nunno, V.; Tonon, C.; Lodi, R.; Agati, R.; Bartolini, S.; Brandes, A.A. Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology. Biomedicines 2022, 10, 3205. https://doi.org/10.3390/biomedicines10123205
Gatto L, Franceschi E, Tosoni A, Di Nunno V, Tonon C, Lodi R, Agati R, Bartolini S, Brandes AA. Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology. Biomedicines. 2022; 10(12):3205. https://doi.org/10.3390/biomedicines10123205
Chicago/Turabian StyleGatto, Lidia, Enrico Franceschi, Alicia Tosoni, Vincenzo Di Nunno, Caterina Tonon, Raffaele Lodi, Raffaele Agati, Stefania Bartolini, and Alba Ariela Brandes. 2022. "Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology" Biomedicines 10, no. 12: 3205. https://doi.org/10.3390/biomedicines10123205
APA StyleGatto, L., Franceschi, E., Tosoni, A., Di Nunno, V., Tonon, C., Lodi, R., Agati, R., Bartolini, S., & Brandes, A. A. (2022). Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology. Biomedicines, 10(12), 3205. https://doi.org/10.3390/biomedicines10123205