# The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) Dataset

arXiv: https://arxiv.org/abs/2109.00356 · HTML: https://mlsanity.com/paper/2109.00356/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)

## Built on by (most cited first, 30 of 43)

- [Multi-modal Vision Pre-training for Medical Image Analysis](https://mlsanity.com/paper/2410.10604/) (2024)
- [Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report](https://mlsanity.com/paper/2210.12113/) (2022)
- [The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)](https://mlsanity.com/paper/2305.00005/) (2023)
- [A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI](https://mlsanity.com/paper/2311.11383/) (2023)
- [Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma](https://mlsanity.com/paper/2405.12963/) (2024)
- [Towards Generalisable Foundation Models for Brain MRI](https://mlsanity.com/paper/2510.23415/) (2025)
- [MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis](https://mlsanity.com/paper/2509.06617/) (2025)
- [Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging](https://mlsanity.com/paper/2508.21580/) (2025)
- [MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows](https://mlsanity.com/paper/2603.24649/) (2026)
- [A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration](https://mlsanity.com/paper/2309.17192/) (2023)
- [A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastoma](https://mlsanity.com/paper/2502.03999/) (2025)
- [Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis](https://mlsanity.com/paper/2604.16729/) (2026)
- [Provable Multi-instance Deep AUC Maximization with Stochastic Pooling](https://mlsanity.com/paper/2305.08040/) (2023)
- [Deep Learning Based Apparent Diffusion Coefficient Map Generation from Multi-parametric MR Images for Patients with Diffuse Gliomas](https://mlsanity.com/paper/2407.02616/) (2024)
- [T1-contrast Enhanced MRI Generation from Multi-parametric MRI for Glioma Patients with Latent Tumor Conditioning](https://mlsanity.com/paper/2409.01622/) (2024)
- [Revisiting Medical Image Retrieval via Knowledge Consolidation](https://mlsanity.com/paper/2503.09370/) (2025)
- [Reliable Multi-modal Medical Image-to-image Translation Independent of Pixel-wise Aligned Data](https://mlsanity.com/paper/2408.14270/) (2024)
- [Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence: a multi-cohort retrospective diagnostic accuracy study](https://mlsanity.com/paper/2508.16650/) (2025)
- [TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth](https://mlsanity.com/paper/2603.04058/) (2026)
- [VASARI-auto: equitable, efficient, and economical featurisation of glioma MRI](https://mlsanity.com/paper/2404.15318/) (2024)
- [Synergistic Vision-Language Reinforcement Enables Scalable On-Demand Analysis across Diverse Clinical Tasks](https://mlsanity.com/paper/2505.03380/) (2025)
- [Hierarchical Brain Structure Modeling for Predicting Genotype of Glioma](https://mlsanity.com/paper/2508.09593/) (2025)
- [MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencoder](https://mlsanity.com/paper/2509.11442/) (2025)
- [TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks](https://mlsanity.com/paper/2511.09605/) (2025)
- [VariViT: A Vision Transformer for Variable Image Sizes](https://mlsanity.com/paper/2602.14615/) (2026)
- [Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction](https://mlsanity.com/paper/2607.16802/) (2026)
- [Interpretable 2D Vision Models for 3D Medical Images](https://mlsanity.com/paper/2307.06614/) (2023)
- [Towards a Multimodal MRI-Based Foundation Model for Multi-Level Feature Exploration in Segmentation, Molecular Subtyping, and Grading of Glioma](https://mlsanity.com/paper/2503.06828/) (2025)
- [Enhancement Without Contrast: Stability-Aware Multicenter Machine Learning for Glioma MRI Imaging](https://mlsanity.com/paper/2509.10767/) (2025)
- [NeuroRAD-FM: A Foundation Model for Neuro-Oncology with Distributionally Robust Training](https://mlsanity.com/paper/2509.15416/) (2025)

## Abstract (arXiv)

Here we present the University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) dataset. The UCSF-PDGM dataset includes 500 subjects with histopathologically-proven diffuse gliomas who were imaged with a standardized 3 Tesla preoperative brain tumor MRI protocol featuring predominantly 3D imaging, as well as advanced diffusion and perfusion imaging techniques. The dataset also includes isocitrate dehydrogenase (IDH) mutation status for all cases and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status for World Health Organization (WHO) grade III and IV gliomas. The UCSF-PDGM has been made publicly available in the hopes that researchers around the world will use these data to continue to push the boundaries of AI applications for diffuse gliomas.