Notice: Function Pods Templates was called incorrectly. Pod Template PHP code is no longer actively supported and will be completely removed in Pods 3.3 Please see Debugging in WordPress for more information. (This message was added in version 3.0.) in /var/www/html/wp-includes/functions.php on line 6085
Deprecated: Pod Template PHP code has been deprecated, please use WP Templates instead of embedding PHP. has been deprecated since Pods version 2.3 with no alternative available. in /var/www/html/wp-content/plugins/pods/includes/general.php on line 1037
RHUH-GBM | The Río Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans
DOI: 10.7937/4545-c905 | Data Citation Required | Image Collection
| Location | Species | Subjects | Data Types | Cancer Types | Size | Status | Updated | |
|---|---|---|---|---|---|---|---|---|
| Head | Human | 40 | MR, Segmentation, Treatment, Demographic, Molecular Test, Measurement | Astrocytoma | Clinical, Image Analyses, Software/Source Code | Limited, Complete | 2023/06/09 |
Summary
This data collection consists of multiparametric MRI scans of 40 adult patients with histopathologically confirmed WHO grade 4 astrocytoma, who underwent surgery at the Río Hortega University Hospital in Valladolid, Spain, between January 2018 and December 2022. The dataset encompasses 600 MRI series, covering three time points: preoperative, early post-operative (less than 72 hours after surgery), and the follow-up scan, at which recurrence is diagnosed. Patients included in the sample underwent gross total resection (GTR) or near total resection (NTR), defined as having no residual tumor enhancement and an extent of resection of more than 95% of the initial enhancing volume, respectively. The modified Response Assessment in Neuro-Oncology criteria (RANO) were used to define tumor progression. 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.
Data Access
Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement to help@cancerimagingarchive.net before accessing the data.
Version 1: Updated 2023/06/09
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Images | MR | DICOM | Download requires NBIA Data Retriever |
40 | 120 | 600 | 37,425 | TCIA Restricted |
| Brain-extracted Images, Segmentations | Segmentation, MR | NIFTI | Download requires IBM-Aspera-Connect plugin |
720 | CC BY 4.0 | |||
| Clinical data | Treatment, Demographic, Molecular Test, Measurement | CSV | CC BY 4.0 |
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
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
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 |
Detailed Description
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.
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.
Related Publications
Publications by the Dataset Authors
The authors recommended this paper as the best source of additional information about this dataset:
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
No publications by dataset authors were found.
Research Community Publications
TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.