Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.
Machine-accessible metadata file describing the reported data (ISA-tab format)
Background & Summary
Gliomas are the most common primary central nervous system malignancies. These tumors, which exhibit highly variable clinical prognosis, usually contain various heterogeneous sub-regions (i.e., edema, enhancing and non-enhancing core), with variable histologic and genomic phenotypes. This intrinsic heterogeneity of gliomas is also portrayed in their radiographic phenotypes, as their sub-regions are depicted by different intensity profiles disseminated across multimodal MRI (mMRI) scans, reflecting differences in tumor biology. There is increasing evidence that quantitative analysis of imaging features1,
Both clinical and computational studies focusing on such research require the availability of ample data to yield significant associations. Considering the value of big data and the potential of publicly available datasets for increased reproducibility of scientific findings, the National Cancer Institute (NCI) of the National Institutes of Health (NIH) created TCGA (cancergenome.nih.gov) and TCIA39 (www.cancerimagingarchive.net). TCGA is a multi-institutional comprehensive collection of various molecularly characterized tumor types, and its data are available in NCI’s Genomic Data Commons portal (gdc-portal.nci.nih.gov). Building upon NIH’s investment in TCGA, the NCI’s Cancer Imaging Program approached sites that contributed tissue samples, to obtain corresponding de-identified routine clinically-acquired radiological data and store them in TCIA. These repositories make available multi-institutional, high-dimensional, multi-parametric data of cancer patients, allowing for radiogenomic analysis. However, the data available in TCIA lack accompanying annotations allowing to fully exploit their potential in clinical and computational studies.
Towards addressing this limitation, this study provides segmentation labels and a panel of radiomic features for the glioma datasets included in the TCGA/TCIA repositories. The main goal is to enable imaging and non-imaging researchers to conduct their analyses and extract measurements in a reproducible and repeatable manner, while eventually allowing for comparison across studies. Specifically, the resources of this study provide i) imaging experts with benchmarks to debate their algorithms, and ii) non-imaging experts (e.g., bioinformaticians, clinicians), who do not have the background to interpret and/or appropriately process the raw images, with data helpful to conduct correlative genomic/clinical studies. Following radiological assessment of both the Glioblastoma Multiforme (TCGA-GBM39, n=262 [Data Citation 1: The Cancer Imaging Archive http://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9]) and the Low-Grade-Glioma (TCGA-LGG39, n=199 [Data Citation 2: The Cancer Imaging Archive http://doi.org/10.7937/K9/TCIA.2016.L4LTD3TK]) collections, we identified 135 and 108 pre-operative mMRI scans, respectively. These scans include at least pre- and post-contrast T1-weighted, T2-weighted, and T2 Fluid-Attenuated Inversion Recovery (FLAIR) volumes. The segmentation labels provided for these scans are divided into two categories: a) computer-aided segmentation labels that could be mainly used for computational comparative studies, and b) manually corrected segmentation labels (approved by an expert board-certified neuroradiologist—M.B.) for use in clinically-oriented analyses, as well as for performance evaluation and training of computational models. The method employed to produce the computer-aided labels is named GLISTRboost36,38, which was awarded the 1st prize during the International Multimodal Brain Tumor Image Segmentation challenge 2015 (BraTS’15)36,38,40,
The generated data describe two independent datasets [Data Citation 3: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q and Data Citation 4: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF], one for each glioma collection, and include the computer-aided and manually-revised segmentation labels, coupled with the corresponding co-registered and skull-stripped TCIA scans, in the Neuroimaging Informatics Technology Initiative (NIfTI57) format, allowing for direct analysis. Furthermore, a panel of radiomic features is included entailing intensity, volumetric, morphologic, histogram-based, and textural parameters, as well as spatial information and parameters extracted from glioma growth models58,
The complete radiological data of the TCGA-GBM and TCGA-LGG collections consist of 262 [Data Citation 1: The Cancer Imaging Archive http://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9] and 199 [Data Citation 2: The Cancer Imaging Archive http://doi.org/10.7937/K9/TCIA.2016.L4LTD3TK] mMRI scans provided from 8 and 5 institutions, respectively (Table 1). The data included in this study describe the subset of the pre-operative baseline scans of these collections, with available MRI modalities of at least T1-weighted pre-contrast (T1), T1-weighted post-contrast (T1-Gd), T2, and T2-FLAIR (Fig. 1a). Specifically, we considered 135 and 108 pre-operative baseline scans from the TCIA-GBM and TCIA-LGG collections, respectively. Further detailed information on the diversity of the imaging sequences used for this study is included in Table 2 (available online only). This table covers the TCIA institutional identifier, patient information (i.e., age, sex, weight), scanner information (i.e., manufacturer, model, magnetic field strength, station name), as well as specific imaging volume information extracted from the dicom headers (i.e., modality name, series number, accession number, acquisition/study/series date, scan sequence, type, slice thickness, slice spacing, repetition time, echo time, inversion time, imaging frequency, flip angle, specific absorption rate, numbers of slices, pixel dimensions, acquisition matrix rows/columns).
It should be noted that the diversity of the available scans in NCI/NIH/TCIA is driven by the fact that TCIA collected all available scans for subjects whose tissue specimens had passed the quality evaluation of the NCI/NIH/TCGA program. Due to this collection being retrospective all the MRI scans are considered ‘standard-of-care’, without following any uniform imaging protocol.
All pre-operative mMRI volumes were re-oriented to the LPS (left-posterior-superior) coordinate system (which is a requirement for GLISTRboost), co-registered to the same T1 anatomic template61 using affine registration through the Oxford center for Functional MRI of the Brain (FMRIB) Linear Image Registration Tool (FLIRT)62,63 of FMRIB Software Library (FSL)64,
For producing the computer-aided segmentation labels, further preprocessing steps included the smoothing of all volumes using a low-level image processing method, namely Smallest Univalue Segment Assimilating Nucleus (SUSAN)71, in order to reduce high frequency intensity variations (i.e., noise) in regions of uniform intensity profile while preserving the underlying structure (Fig. 1d). The intensity histograms of all modalities of all patients were then matched72 to the corresponding modality of a single reference patient, using the implemented version in ITK (HistogramMatchingImageFilter).
It should be noted that we did not use any non-parametric, non-uniform intensity normalization algorithm73,
Segmentation labels of glioma sub-regions
Consistent with the BraTS challenge56 the segmentation labels that we consider in the present study, and make available through TCIA [Data Citation 3: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q and Data Citation 4: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF], delineate the enhancing part of the tumor core (ET), the non-enhancing part of the tumor core (NET), and the peritumoral edema (ED) (Fig. 2). The ET is described by areas that show hyper-intensity in T1-Gd when compared to T1, but also when compared to normal/healthy white matter (WM) in T1-Gd. Biologically, ET is felt to represent regions where there is leakage of contrast through a disrupted blood-brain barrier that is commonly seen in high grade gliomas. The NET represents non-enhancing tumor regions, as well as transitional/pre-necrotic and necrotic regions that belong to the non-enhancing part of the tumor core (TC), and are typically resected in addition to the ET. The appearance of the NET is typically hypo-intense in T1-Gd when compared to T1, but also when compared to normal/healthy WM in T1-Gd. Finally, the ED is described by hyper-intense signal on the T2-FLAIR volumes.
Computer-aided segmentation approach
The method used in this study to produce the computer-aided segmentation labels for all pre-operative scans of both TCGA-GBM and TCGA-LGG collections is named GLISTRboost36,38 and it is based on a hybrid generative-discriminative model. The generative part incorporates a glioma growth model58,
GLISTRboost36,38 is based on a modified version of the GLioma Image SegmenTation and Registration (GLISTR)79 software. GLISTR jointly performs a) the registration of a healthy population probabilistic atlas to brain scans of patients with gliomas using a tumor growth model to account for mass effects, and b) the segmentation of such scans into healthy and tumor tissues. The whole framework of GLISTR is based on a probabilistic generative model that relies on EM, to recursively refine the estimates of the posteriors for all tissue labels, the deformable mapping to the atlas, and the parameters of the incorporated brain tumor growth model58,
This ‘initial’ segmentation is then refined by taking into account information from multiple patients via a discriminative machine-learning algorithm. Specifically, we used the gradient boosting algorithm76 to perform voxel-level multi-label classification. Gradient boosting produces a prediction model by combining weak learners in an ensemble. We used decision trees of maximum depth 3 as ‘weak learners’, which were trained in a sub-sample of the training set, in order to introduce randomness77. The sampling rate was set equal to 0.6, while additional randomness was introduced by sampling stochastically a subset of imaging (i.e., radiomic) features at each node. The number of sampled features was set equal to the square root of the total number of features. The algorithm was terminated after 100 iterations.
The set of features used for training our model was extracted volumetrically and consists of i) intensity information, ii) image derivative, iii) geodesic information, iv) texture features, and v) the GLISTR posterior probability maps. The intensity information is summarized by the raw intensity value, I, of each image voxel, vi, at each modality, m, (i.e., I(vim)), as well as by the respective differences among all four modalities, i.e., I(viT1)- I(viT1Gd), I(viT1)- I(viT2), I(viT1)- I(viT2FLAIR), I(viT1Gd)- I(viT1), I(viT1Gd)- I(viT2), I(viT1Gd)- I(viT2FLAIR), I(viT2)- I(viT1), I(viT2)- I(viT1Gd), I(viT2)- I(viT2FLAIR), I(viT2FLAIR)- I(viT1), I(viT2FLAIR)- I(viT1Gd), I(viT2FLAIR)- I(viT2). The image derivative component consists of the Laplacian of Gaussians and the image gradient magnitude. Note that in order to ensure that the intensity-based features are comparable, intensity normalization was performed across subjects based on the median intensity value of the cerebrospinal fluid label, as provided by GLISTR. Geodesic information was used to introduce spatial context information. At any voxel vi we calculated the geodesic distance from the seed-point at voxel vs, which was used in GLISTR as the tumor center. The geodesic distance between vi and vs was estimated using the fast marching method84,85 and by taking into account local image gradient magnitude86. Furthermore, we used texture features computed from a gray-level co-occurrence matrix (GLCM)87. Specifically, these texture features describe first-order statistics (i.e., mean and variance of each modality’s intensities within a radius of 2 voxels for each voxel), as well as second-order statistics. To obtain the latter, the image volumes were firstly normalized to 64 different gray levels, and then a bounding box of 5-by-5-by-5 voxels was used for all the voxels of each image as a sliding window. Then, a GLCM was populated by taking into account the intensity values within a radius of 2 pixels and for the 26 main 3D directions to extract the energy, entropy, dissimilarity, homogeneity (i.e., inverse difference moment of order 2), and inverse difference moment of order 1. These features were computed for each direction and their average was used. To avoid overfitting, the gradient boosting machine was trained using simultaneously both LGG and GBM training data of BraTS’15, in a 54-fold cross-validation setting (allowing for using a one out of the 54 available LGGs of the BraTS’15 training data, within each fold).
Finally, the segmentation results were further refined for each patient separately, by assessing the local intensity distribution of the segmentation labels and updating their spatial configuration based on a probabilistic model78. The intensity distributions of the WM, ED, NET and ET, were populated separately using the corresponding voxels of posterior probability equal to 1, as given by GLISTR. Histogram normalization was then performed for the 3 pair-wise distributions considered; ED versus WM in T2-FLAIR, ET versus ED in T1-Gd, and ET versus NET in T1-Gd. Maximum likelihood estimation was used to model the class-conditional probability densities (Pr(I(vi)|Class) by a distinct Gaussian model for each class. In all pair-wise comparisons described before, the former tissue is expected to be brighter than the latter. Voxels of each class with spatial proximity smaller than 4 voxels to the voxels of the paired class, were evaluated by assessing their intensity I(vi) and comparing the (‘Pr(I(vi)|Class1) with Pr(I(vi)|Class2). The voxel vi was then classified into the tissue class with the larger conditional probability. This is equivalent to a classification based on Bayes' Theorem with equal priors for the two classes, i.e., Pr(Class1)=Pr(Class2)=0.5.
The output of GLISTRboost segmentation is expected to yield labels for ET, NET, and ED. However, some gliomas, especially LGG, do not exhibit much contrast enhancement, or ED. Biologically, LGGs may have less blood-brain barrier disruption (leading to less leak of contrast during the scan), and may grow at a rate slow enough to avoid significant edema formation, which results from rapid disruption, irritation, and infiltration of normal brain parenchyma by tumor cells. As such, manual revision of the segmentation labels was performed, particularly for LGG cases lacking ET or ED regions. Specifically, after taking all the above into consideration, in scans of LGGs without an apparent ET area we consider only the NET and ED labels (Fig. 3a,d), whereas in LGG scans without ET and without obvious texture differences across modalities we consider only the NET label, allowing for distinguishing between normal and abnormal brain tissue (Fig. 3e). The difficulty in calculating the accurate boundaries between tumor and healthy tissue in the operating room is reflected in the segmentation labels as well; there is high uncertainty among neurosurgeons, neuroradiologists, and imaging scientists in delineating these boundaries. Therefore, small regions within the segmented labels that were ambiguous of their exact classification, were left as segmented by GLISTRboost.
Manual revisions/corrections applied in the computer-aided segmentation labels include: i) obvious under- or over-segmented ED/ET/NET regions (Fig. 3d–g), ii) voxels classified as ED within the tumor core (Fig. 3b,c,g), iii) unclassified voxels within the tumor core (Fig. 3c–g), iv) voxels classified as NET outside the tumor core. Contralateral and periventricular regions of T2-FLAIR hyper-intensity were excluded from the ED region (Fig. 3c,f), unless they were contiguous with peritumoral ED (Fig. 3g—addition of apparent contralateral ED), as these areas are generally considered to represent chronic microvascular changes, or age-associated demyelination, rather than tumor infiltration88.
Radiomic features panel
An extensive panel of more than 700 radiomic features is extracted volumetrically (in 3D), based on the manually-revised labels of each tumor sub-region that comprised i) intensity, ii) volumetric89, iii) morphologic90,
These radiomic features are provided on an ‘as-is’ basis, and are distinct from the panel of features used in GLISTRboost. The biological significance of these individual radiomic features remains unknown, but we include them here to facilitate research on their association with molecular markers, clinical outcomes, treatment responses, and other endpoints, by researchers without sufficient computational background to extract such features. Although researchers can derive their own radiomic features from our segmentation labels, and the corresponding images we included a collection of features that have been shown in various studies to relate to clinical outcome31 and underlying tumor molecular characteristics30,32. Note that the radiomic features we provide are extracted from the denoised images, and the users might also want to consider extracting features from the unsmoothed images provided in [Data Citation 3: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q and Data Citation 4: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF].
All software tools used for pre-processing, initialization, and generation of the hereby described segmentation labels are based on publicly available tools. Specifically, the tools used for the pre-processing steps of skull-stripping (BET)67,68 and co-registration (FLIRT)62,63 are publicly available from the FMRIB Software Library (FSL)64,
We developed CaPTk83 as a toolkit to facilitate translation of complex research algorithms into clinical practice, by enabling operators to conduct quantitative analyses without requiring substantial computational background. Towards this end CaPTk is a dynamically growing software platform, with various integrated applications, allowing 1) interactive definition of coordinates and regions, 2) generic image analysis (e.g., registration, feature extraction), and 3) specialized analysis algorithms (e.g., identification of genetic mutation imaging markers12). Specifically for this study, CaPTk was used to 1) manually initialize seed-points required for the initialization of GLISTRboost36,38, 2) apply the de-noising approach (SUSAN)71 used for smoothing images before their input to GLISTRboost, as well as 3) to extract the radiomic features released in TCIA [Data Citation 3: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q and Data Citation 4: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF]. The exact version used for initializing the required seed-points in this study was released on the 14th of October 2016 and the code source, as well as executable installers, are available in: www.med.upenn.edu/sbia/captk.html.
Finally, our segmentation approach, GLISTRboost36,38, has been made available for public use through the Online Image Processing Portal (IPP—ipp.cbica.upenn.edu) of the CBICA. CBICA's IPP allows users to perform their data analysis using integrated algorithms, without any software installation, whilst also using CBICA's High Performance Computing resources. It should be noted that we used the Python package scikit-learn104 for the implementation of the gradient boosting algorithm.
We selected only the pre-operative multimodal scans of the TCGA-GBM [Data Citation 1: The Cancer Imaging Archive http://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9] and TCGA-LGG [Data Citation 2: The Cancer Imaging Archive http://doi.org/10.7937/K9/TCIA.2016.L4LTD3TK] glioma collections, from the publicly available TCIA repository. The generated data, which is made publicly available through TCIA’s Analysis Results Directory (wiki.cancerimagingarchive.net/x/sgH1) [Data Citation 3: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q and Data Citation 4: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF], comprise pre-operative baseline re-oriented, co-registered and skull-stripped mMRI scans together with their corresponding computer-aided and manually-revised segmentation labels in NIfTI57 format. We have further enriched the file containers to include an extensive panel of radiomic features, which we hope may facilitate radiogenomic research using the TCGA portal, as well as comparison of segmentation methods, even among those scientists without image analysis resources.
A subset of the pre-operative scans included in the generated data [Data Citation 3: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q and Data Citation 4: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF] was also part of the BraTS’15 dataset (Table 4 (available online only)), which were skull-stripped, co-registered to the same anatomical template and resampled to 1 mm3 voxel resolution by the challenge organizers. For this subset, we provide the identical MRI volumes as provided by the BraTS’15 challenge, allowing other researchers to compare their segmentation labels to the leaderboard of the BraTS’15 challenge. Furthermore, the manually-revised segmentation labels provided in [Data Citation 3: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q and Data Citation 4: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF] are included in the datasets of the BraTS’17 challenge, for benchmarking computational segmentation algorithms against tumor delineation validated by expert neuroradiologists, allowing for repeatable research.
Our expert board-certified neuroradiologist (M.B.) identified 135 and 108 pre-operative baseline scans of the TCGA-GBM and the TCGA-LGG glioma collections, via radiological assessment and while blinded to the glioma grade. Since it is not always easy to determine if a scan is pre-operative or post-operative only by visually assessing MRI volumes, and the radiological reports were not available through the TCGA/TCIA repositories, whenever we mention ‘pre-operative scans’ in this study, we refer to those that radiographically do not have clear evidence of prior instrumentation. Specifically, the main evaluation criterion for classifying scans as pre-operative, was absence of obvious skull defect and of operative cavity through either biopsy or resection.
We note that a mixed (pre- and post-operative) subset of 223 and 59 scans from the TCIA-GBM and TCIA-LGG datasets, respectively, were included in the BraTS’15 challenge, as part of their training (nGBM=200, nLGG=44) and testing (nGBM=23, nLGG=15) datasets, via the Virtual Skeleton Database (VSD) platform56,105 (www.virtualskeleton.ch). Since an explicit distinction as pre- or post-operative was not provided for the BraTS’15 dataset, we conducted the radiological assessment of the complete TCIA collections, blind to whether a scan was part of the BraTS challenge, and only included the BraTS’15 volumes identified as pre-operative (Fig. 1f) (Table 4 (available online only)).
The segmentation method we developed to produce the segmentation labels, GLISTRboost36,38, was ranked as the best performing method and awarded the 1st prize during the International Multimodal Brain Tumor Image Segmentation challenge 2015 (BraTS’15)36,38,40,
Furthermore, we used the Jaccard coefficient, in order to quantify the difference between the computer-aided segmentation labels produced for all the scans identified as pre-operative and all the manually-corrected labels that we provide in [Data Citation 3: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q and Data Citation 4: The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF]. The median (mean±std.dev) Jaccard values for the three regions of interest i.e., WT, TC, ET, were equal to 0.96 (0.93±0.1), 0.87 (0.78±0.23), and 0.86 (0.73±0.29), respectively.
The classification scheme of segmentation labels considered for the manual corrections of the GBM and LGG cases describe all three segmentation labels (i.e., ET, NET, and ED) for both GBMs and LGGs with an apparent ET area. However, whenever we note LGG scans without an apparent ET area and not obvious texture differences, we considered only the NET label, allowing for distinguishing between normal and abnormal brain tissue, as slowly growing tumors are not expected to induce ED. Furthermore, due to high uncertainty (reported by neurosurgeons, neuroradiologists, and imaging scientists) on the exact boundaries between the various tumor labels, particularly between NET and ED, small regions that visual assessment was ambiguous of their exact classification, were left as segmented by GLISTRboost.
Manual revisions/corrections applied in the computer-aided segmentation labels comprise: i) obvious under- or over-segmented ED/ET/NET regions (Fig. 3d–g), ii) voxels classified as ED within the tumor core (Fig. 3b,c,g), iii) unclassified voxels within the tumor core (Fig. 3c–g), iv) voxels classified as NET outside the tumor core. Note that during the manual corrections only peritumoral ED was considered, and both contralateral, and periventricular ED was deleted (Fig. 3c,f), unless it was a clear continuation of the peritumoral ED, in which cases was added (Fig. 3g). The rationale for this is that contralateral and periventricular white matter hyper-intensities regions might be considered pre-existing conditions, related to small vessel ischemic disease, especially in older patients.
The scheme followed for the manual correction included two computational imaging scientists (S.B., A.S.) and a medical doctor (H.A.) working in medical image computing and analysis for 10, 12 and 8 years, respectively. These operators corrected mislabeled voxels following the rules set by our expert board-certified neuroradiologist (M.B.) with 14 years of experience. The corrected labels were then iteratively re-evaluated by the latter and re-iterated until they were satisfactory segmented.
How to cite this article: Bakas, S. et al. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4:170117 doi: 10.1038/sdata.2017.117 (2017).
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Bakas, S. The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF (2017)
Martin Rozycki (who did the final QC of the submitted data), Michel Bilello (who validated all manually-revised segmentation labels), Spyridon Bakas, and Christos Davatzikos had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. The authors would like to thank Dr Gaurav Shukla (Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA) for assisting in the extraction of the radiomic features. Furthermore, the authors would like to acknowledge the effort of Ke Zeng, Saima Rathore, Bilwaj Gaonkar, and Sarthak Pati, who all contributed in successfully developing GLISTRboost. This work was supported in part by the National Institutes of Health (NIH) R01 grant on ‘Predicting brain tumor progression via multiparametric image analysis and modeling’ (R01-NS042645), and in part by the NIH U24 grant of ‘Cancer imaging phenomics software suite: application to brain and breast cancer’ (U24-CA189523).