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 500 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 500 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.
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:
Localtab Group |
---|
Localtab |
---|
active | true |
---|
title | Data Access |
---|
| Data Access
Data Type | Download all or Query/Filter | License |
---|
Images and Annotations (NIfTI format , 156 GB) |
Tcia button generator |
---|
url | https://faspex.cancerimagingarchive.net/aspera/faspex/external_deliveries/333?passcode=492e0b4256ec1b978f5c980960c156c3baad9d21# |
---|
|
|
(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) | | Clinical data (CSV) |
Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/download/attachments/119705830/UCSF-PDGM-metadata.csv?api=v2 |
---|
|
|
| |
Click the Versions tab for more info about data releases. Additional Resources for this DatasetThe 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. - Software / Code on Github
- Genomics data in DbGAP
- Genomics data in Gene Expression Omnibus
Click the Versions tab for more info about data releases. Please contact help@cancerimagingarchive.net with any questions regarding usage. |
Localtab |
---|
title | Detailed Description |
---|
| Detailed DescriptionImage Statistics |
|
---|
Modalities | MR | Number of Patients | 500 | Number of Studies | 500 | Number of Series | 11,523 | Number of Images | 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. |
Localtab |
---|
title | Citations & Data Usage Policy |
---|
| Citations & Data Usage Policy Tcia license 4 international |
---|
Info |
---|
| Evan Calabrese, Javier Villanueva-Meyer, Jeffrey Rudie, Andreas Rauschecker, Ujjwal Baid, Spyridon Bakas, Soonmee Cha, |
|
|