Summary
The dataset contains T1-weighted (T1w), T2-weighted (T2w), Fluid Attenuated Inversion Recovery (FLAIR), T1w contrast-enhanced (T1ce) sequences, and diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) maps. It also includes clinical and demographic data, IDH status, treatment information, and volumetric assessment of the extent of the resection. Moreover, the dataset comprises expert-validated segmentations of tumor subregions (e.g., enhancing tumor, necrosis, peritumoral region), generated through computer-aided methods from preoperative, postoperative, and follow-up scans.
This dataset is unique in its inclusion of patients who underwent extensive resection of > 95% of the enhancing tumor. It also stands out from other publicly available datasets by providing early postoperative studies and segmentations, filling the gap in preoperative-focused datasets. By making these data publicly available, the scientific community can analyze recurrence patterns in patients who underwent total or near-total resection and develop new registration and segmentation algorithms focused on post-surgical and follow-up studies.
Acknowledgements
This work was partially funded by a grant awarded by the "Instituto Carlos III, Proyectos I-D-i, Acción Estratégica en Salud 2022" under the project titled "Prediction of tumor recurrence in glioblastomas using magnetic resonance imaging, machine learning, and transcriptomic analysis: A supratotal resection guided by artificial intelligence," reference PI22/01680.
Data Access
| Data Type | Download all or Query/Filter | License |
|---|---|---|
Images (37425 files, DICOM, 16 GB) | (Download requires NBIA Data Retriever) | |
| Brain-extracted Images, Segmentations (720 images , NIfTI, 2.9 GB) | ||
| Clinical data (CSV, 7 kB) |
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Additional Resources for this Dataset
The following external resources have been made available by the data submitters. These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.
- The source code used for image preprocessing in this collection can be found at https://github.com/smcch/RHUH-GBM-dataset-MRI-preprocessing
Detailed Description
| Image Statistics | |
|---|---|
Modalities | MR |
Number of Patients | 40 |
Number of Studies | 120 |
Number of Series | 600 |
Number of Images | 38145 |
| Images Size (GB) | 18.9 |
The inclusion criteria were:
Primary newly diagnosed WHO grade 4 astrocytoma adult patients (age over 18 years) who underwent surgery. Gross total resections (GTR) and Near Total Resection (NTR) were defined as no residual tumor enhancement and an extent of resection of more than 95% of the initial enhancing volume, respectively. Patients were treated with systemic temozolomide according to the Stupp protocol. Tumor progression was defined according to the modified Response assessment in neuro-oncology criteria (RANO). All the patients in our collection had primary glioblastomas. They were all newly diagnosed, with the exception of two patients who had undergone previous surgery and chemo/radiotherapy.
The exclusion criteria were:
Other histopathological diagnoses, patients in which it was impossible to establish the diagnosis of progression vs. pseudo-progression, missing MRI sequences, and poor-quality MRI scans due to the presence of artifacts.
The dataset includes clinical and pathological information:
Age, Sex, preoperative and postoperative Karnofsky performance score, Overall survival, Progression-free survival, percentage of the extent of resection of enhancing tumor, systemic therapy received, details of RT received (dose, technique, number of fractions, isodose), IDH status, ATRX mutation, and Ki-67 index, size of enhancing tumor recurrence.
Note: in the Clinical data file, In this dataset, some patients were still alive at the end of the data collection period, and their survival times are not yet known. These patients are considered right-censored = yes. In survival analysis, 'right censoring' occurs when the event of interest has not occurred for some study participants by the end of the study period or by the time the data was collected. This means that the survival time of censored participants is not fully observed or known.
The dataset includes the segmentations of the enhancing tumor, necrosis, and peritumoral region from the pre-postoperative and follow-up studies that experts have manually corrected. The dataset represents a sample of unique characteristics by including patients with an extent of resection of > 95 % of the enhancing tumor.
MRI acquisition protocol:
The dataset includes T1-weighted (T1w), T2-weighted (T2w), FLAIR (Fluid attenuated inversion recovery), T1w contrast-enhanced (T1ce) sequences, and diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) maps from 3 scanners at 2 centers. Please see the manuscript, Supplementary Table 1: MRI acquisition parameters, for detail.
MRI scans underwent preprocessing using a specific pipeline.
- This process involved converting DICOM to NiFTI using the dicom2niix tool (https://github.com/rordenlab/dcm2niix/releases/tag/v1.0.20220720),
- registering T1ce scans for each subject to the SRI24 anatomical atlas space using FLIRT (FMRIB’s Linear Image Registration Tool) (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL),
- coregistering T1w, T2w, FLAIR scans, and ADC maps to the transformed T1ce scan,
- extracting the brain from all co-registered scans with Synthstrip (https://surfer.nmr.mgh.harvard.edu/docs/synthstrip/), and
- normalizing intensity using the normalization tools included in the Cancer Imaging Phenomics Toolkit (CaPTk) (https://www.nitrc.org/projects/captk/).
- Computer-assisted segmentations were created using Deep-Medic (https://github.com/deepmedic/deepmedic) from the preprocessed images at each time point, resulting in three labels: 1) necrosis, 2) peritumoral signal alteration (including edema and non-enhancing tumor), and 3) enhancing tumor. Two expert neurosurgeons reviewed and manually corrected all segmentations.
Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Data Citation
Cepeda, S., García-García, S., Arrese, I., Herrero, F., Escudero, T., Zamora, T., & Sarabia, R. (2023) The Río Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM) [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/4545-c905
Publication Citation
Cepeda, S., García-García, S., Arrese, I., Herrero, F., Escudero, T., Zamora, T., & Sarabia, R. (2023). The Río Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM). In Data in Brief (Vol. 50, p. 109617). Elsevier BV. https://doi.org/10.1016/j.dib.2023.109617
TCIA Citation
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7
Other Publications Using This Data
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Version 1 (Current): Updated 2023/06/09
| Data Type | Download all or Query/Filter | License |
|---|---|---|
Images (DICOM, 16 GB) | (Download requires NBIA Data Retriever) | |
| Brain-extracted Images, Segmentations (NIfTI, 2.9 GB) | ||
| Clinical data (CSV) |