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 resections (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.
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
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| Brain-extracted Images (NIfTI, 2.9 GB) | (Download requires Aspera plugin) | |
| Clinical data (CSV) |
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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
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Note: in the Clinical data file, “Right Censored” value = “yes” indicate that the person died during the data-gathering period. “No” indicates they were “still alive” when the data-gathering period concluded.
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 according to the Stupp protocol. Tumor progression was defined according to the modified Response assessment in neuro-oncology criteria (RANO).
The exclusion criteria were: Other histopathological diagnoses, tumor recurrence cases (second surgery), 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 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. The acquisition protocols were: Scanner manufacturer and field strength: General Electric, Signa HDxT, 1.5 T. T1ce: TR/TE, 7.98 ms/2.57 ms; FOV, 220 x 220mm; matrix, 512 x 512; slice thickness, 1mm. T1w: TR/TE, 5.98ms/1.83ms; FOV, 220 x 220mm; matrix, 512 x 512; slice thickness, 1.6mm. T2w: TR/TE, 5220 ms/96.12 ms; FOV, 220 x 220mm; matrix, 512 x 512; slice thickness, 5mm. FLAIR: TR/TE, 11000ms/142.43ms; FOV, 220 x 220mm; matrix, 512 x 512; slice thickness, 4mm. DWI: TR/TE, 8000ms/111.7ms; FOV, 256 x 256mm; matrix, 128 x 160; slice thickness, 5mm; b-values, 0 and 1000 s/mm2.
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.
The images have been preprocessed using the CaPTk software (https://cbica.github.io/CaPTk/), according to the BraTS Challenge pipeline (http://braintumorsegmentation.org). 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.
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
DRAFT MODE PER APR 21 2023
Publication Citation
Cepeda, S., Garcia-Garcia, S., Arrese, I., Herrero, F., Escudero, T., Zamora, T., & Sarabia, R. (2023). The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM) (Version 2). arXiv. https://doi.org/10.48550/arXiv.2305.00005
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
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