Methods
Patient Population
Data collection was performed in accordance with relevant guidelines and regulations and was approved by the University of California San Francisco institutional review board with a waiver for consent. The dataset population consisted of 501 adult patients with histopathologically confirmed grade II-IV diffuse gliomas who underwent preoperative MRI, initial tumor resection, and tumor genetic testing at a single medical center between 2015 and 2021. Patients with any prior history of brain tumor treatment were excluded; however, history of tumor biopsy was not considered an exclusion criterion.
Genetic Biomarker Testing
All subjects’ tumors were tested for IDH mutations by genetic sequencing of tissue acquired during biopsy or resection. All grade III and IV tumors were tested for MGMT methylation status using a methylation sensitive quantitative PCR assay.
Study participant demographic data
The 501 cases included in the UCSF-PDGM include 55 (11%) grade II, 42 (9%) grade III, and 403 (80%) grade IV tumors. There was a male predominance for all tumor grades (56%, 60%, and 60%, respectively for grades II-IV). IDH mutations were identified in a majority of grade II (83%) and grade III (67%) tumors and a small minority of grade IV tumors (8%). MGMT promoter hypermethylation was detected in 63% of grade IV gliomas and was not tested for in a majority of lower grade gliomas. 1p/19q codeletion was detected in 20% of grade II tumors and a small minority of grade III (5%) and IV (<1%) tumors. Tabulated details and glossary are available in the Data Access and Detailed Description tabs below.
Image Acquisition
All preoperative MRI was performed on a 3.0 tesla scanner (Discovery 750, GE Healthcare, Waukesha, Wisconsin, USA) and a dedicated 8-channel head coil (Invivo, Gainesville, Florida, USA). The imaging protocol included 3D T2-weighted, T2/FLAIR-weighted, susceptibility-weighted (SWI), diffusion-weighted (DWI), pre- and post-contrast T1-weighted images, 3D arterial spin labeling (ASL) perfusion images, and 2D 55-direction high angular resolution diffusion imaging (HARDI). Over the study period, two gadolinium-based contrast agents were used: gadobutrol (Gadovist, Bayer, LOC) at a dose of 0.1 mL/kg and gadoterate (Dotarem, Guerbet, Aulnay-sous-Bois, France) at a dose of 0.2 mL/kg.
Image Pre-Processing
HARDI data were eddy current corrected and processed using the Eddy and DTIFIT modules from FSL 6.0.2 yielding isotropic diffusion weighted images (DWI) and several quantitative diffusivity maps: mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and fractional anisotropy (FA). Eddy correction was performed with outlier replacement on and topup correction off. DTIFIT was performed with simple least squares regression. Each image contrast was registered and resampled to the 3D space defined by the T2/FLAIR image (1 mm isotropic resolution) using automated non-linear registration (Advanced Normalization Tools). Resampled co-registered data were then skull stripped using a previously described and publicly available deep-learning algorithm: https://www.github.com/ecalabr/brain_mask/.
Tumor Segmentation
Multicompartment tumor segmentation of study data was undertaken as part of the 2021 BraTS challenge. Briefly, image data first underwent automated segmentation using an ensemble model consisting of prior BraTS challenge winning segmentation algorithms. Images were then manually corrected by trained radiologists and approved by 2 expert reviewers. Segmentation included three major tumor compartments: enhancing tumor, non-enhancing/necrotic tumor, and surrounding FLAIR abnormality (sometimes referred to as edema).
The UCSF-PDGM adds to on an existing body of publicly available diffuse glioma MRI datasets that are commonly used in AI research applications. As MRI-based AI research applications continue to grow, new data are needed to foster development of new techniques and increase the generalizability of existing algorithms. The UCSF-PDGM not only significantly increases the total number of publicly available diffuse glioma MRI cases, but also provides a unique contribution in terms of MRI technique. The inclusion of 3D sequences and advanced MRI techniques like ASL and HARDI provides a new opportunity for researchers to explore the potential utility of cutting-edge clinical diagnostics for AI applications. In addition, these advanced imaging techniques may prove useful for radiogenomic studies focused on identification of IDH mutations or MGMT promoter methylation.
The UCSF-PDGM dataset, particularly when combined with existing publicly available datasets, has the potential to fuel the next phase of radiologic AI research on diffuse gliomas. However, the UCSF-PDGM dataset’s potential will only be realized if the radiology AI research community takes advantage of this new data resource. We hope that this dataset sparks inspiration in the next generation of AI researchers, and we look forward to the new techniques and discoveries that the UCSF-PDGM will generate.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
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title | Data Access |
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| Data AccessData Type | Download all or Query/Filter | License |
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Images and Annotations (NIfTI format , 156 GB) |
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url | https://faspex.cancerimagingarchive.net/aspera/faspex/external_deliveries/ |
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url | https://wiki.cancerimagingarchive.net/download/attachments/119705830/UCSF-PDGM-metadata_v2.csv?api=v2 |
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title | Detailed Description |
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| Detailed DescriptionImage Statistics |
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Modalities | MR | Number of Patients | 501 | Number of Studies | 501 | Number of Files | 11,523 | Images Size (GB) | 156.5 GB |
All image data have been "skull stripped", deidentified, pre-processed per the methods section of our abstract, and converted to NIfTI format. We cannot provide original DICOM data, however, these pre-processed files have been prepared to facilitate the type of research that this dataset is intended for. Publicly available deep-learning algorithm for performing this process is here: https://www.github.com/ecalabr/brain_mask/. Glossary of abbreviations: UCSF-PDGM-metadata_glossary.csv Term | Represents | Values |
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ID | DICOM (0010,0020) PatientID |
| Sex | DICOM (0010,0040) Patient Sex | M,F | Age at MRI | Age in years at time of MR imaging |
| WHO CNS Grade | Grade per the 2021 World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS 2021) (https://doi.org/10.1093/neuonc/noab106 ) | 2,3,4 | Final pathologic diagnosis (WHO 2021) | Final (integrated) pathologic diagnosis per the 2021 World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS 2021) ( https://doi.org/10.1093/neuonc/noab106 ) | - Glioblastoma, Isocitrate dehydrogenase (IDH) -wildtype,
- Astrocytoma, IDH-mutant,
- Astrocytoma, IDH-wildtype,
- Oligodendroglioma, IDH-mutant, 1p/19q-codeleted
| MGMT status | O6-methylguanine-DNA methyltransferase status - clinical interpretation of the MGMT index described below. | negative, positive, indeterminate | MGMT index | O6-methylguanine-DNA methyltransferase methylation index (in house method developed by UCSF clinical labs, https://genomics.ucsf.edu/content/mgmt-promoter-methylation-assay ) where numeric values 0-17 indicate the number of promoter methylation sites. | 0-17, blank | 1p/19q | presence of codeletion of 1p and 19q genes, assayed by fluorescent in-situ hybridization | intact, co-deletion, relative co-deletion, unknown | IDH | isocitrate dehydrogenase mutation subtype characterized with a capture-based targeted next-generation DNA sequencing panel (UCSF500) as described in (https://doi.org/10.1093/neuonc/now254 ) |
| 1-dead 0-alive | Survival status of the patient at last clinical follow up. |
| OS | Overall survival in days from initial diagnosis to last clinical follow up. |
| EOR | Extent of resection determined by review of operative reports and immediate postoperative imaging | biopsy (only biopsy) Subtotal resection (STR) gross total resection (GTR) | Biopsy prior to imaging | Was a burr-hole biopsy performed prior to imaging | yes, no, blank |
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title | Citations & Data Usage Policy |
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| Calabrese, E., Villanueva-Meyer, J., Rudie, J., Rauschecker, A., Baid, U., Bakas, S., Cha, S., Mongan, J., & Hess, C. (2022). The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) [Dataset]. The Cancer Imaging Archive. DOI: 10.7937/tcia.bdgf-8v37 |
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title | Publication Citation |
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| Evan Calabrese, Javier E. Villanueva-Meyer, Jeffrey D. Rudie, Andreas M. Rauschecker, Ujjwal Baid, Spyridon Bakas, Soonmee Cha, John T. Mongan, Christopher P. Hess. The UCSF Preoperative Diffuse Glioma MRI (UCSF-PDGM) Dataset. Radiology: Artificial Intelligence. DOI: https://doi.org/10.1148/ryai.220058 (in press) |
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| Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7 |
Other Publications Using This DataCalabrese E, Rudie JD, Rauschecker AM, Villanueva-Meyer JE, Cha S. Feasibility of Simulated Postcontrast MRI of Glioblastomas and Lower Grade Gliomas Using 3D Fully Convolutional Neural Networks. Radiology: Artificial Intelligence. 2021 May 19;e200276. - Calabrese E, Villanueva-Meyer JE, Cha S. A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas. Scientific Reports. 2020 Jul 16;10(1):11852.
TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. |
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| Version 1 (Current)2 (Current): Updated 2022/11/30Data Type | Download all or Query/Filter | License |
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url | https://faspex.cancerimagingarchive.net/aspera/faspex/external_deliveries/355?passcode=74fa8e9291ff65e220c994599c6db0c7582f475d |
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(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) | | Clinical data (CSV)
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url | https://wiki.cancerimagingarchive.net/download/attachments/119705830/UCSF-PDGM-metadata_v2.csv?api=v2 |
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Changes to this version: - Fixes to integer rounding errors in *tumor_segmentation.nii.gz files by the Collection's investigators.
- TCIA users have asked for a mapping to the 2021 BraTS data to prevent any data leak when both datasets are used. Updated metadata spreadsheet attached which includes the BraTS IDs for all relevant cases. This data was confirmed by BraTS Challenge organizers.
- Correction of name suffix for bias corrected T1 postcontrast images from "T1gad_bias" to "T1c_bias" for consistency.
Version 1: Updated 2022/09/26Data Type | Download all or Query/Filter | License |
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Images and Annotations (NIfTI zip, 156 GB) |
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url | https://faspex.cancerimagingarchive.net/aspera/faspex/external_deliveries/333?passcode=492e0b4256ec1b978f5c980960c156c3baad9d21# |
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(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) | | Clinical data (CSV) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/119705830/UCSF-PDGM-metadata.csv?api=v2 |
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