Biomedical Statistics and Informatics

Biomedical Statistics and Informatics

The Division of Biomedical Statistics & Informatics in the UCSF Department of Neurosurgery is involved in a variety of projects related to brain tumor research.


Brain Tumor Center Database

The Brain Tumor Database (BTCDB) offers a multitude of benefits that significantly impact the field of neuro-oncology and precision medicine at UCSF. Firstly, the BTCDB serves as a comprehensive repository of patient data, encompassing demographics, vital status, surgical information, clinical pathology, and imaging data. This wealth of information allows researchers and clinicians to access a holistic view of each patient's journey, aiding in personalized treatment strategies and informed decision-making. By centralizing and organizing diverse datasets, the BTCDB streamlines data management processes, enabling efficient data retrieval and analysis for research purposes.

Secondly, the BTCDB fosters collaboration and data sharing among institutions and researchers, facilitating the exchange of knowledge and insights in the neuro-oncology domain. Through collaborative projects and shared datasets, the BTCDB promotes synergy among stakeholders, leading to the generation of impactful research outcomes and the development of novel treatment approaches. By avoiding duplication of efforts and promoting internal and external collaborations, the BTCDB accelerates the pace of research and enhances the quality of studies conducted in the field.

Lastly, the BTCDB plays a pivotal role in advancing precision medicine initiatives by enabling the integration of biomarkers, genetic data, and neurocognitive assessments into clinical decision-making processes. The database's ability to provide real-time and batch processing of data, coupled with configurable data visualization tools, empowers clinicians to derive meaningful insights from complex datasets. By leveraging the BTCDB for precision medicine applications, healthcare providers can tailor treatment plans to individual patient profiles, leading to more targeted and effective interventions in neuro-oncology.


Extent of Resection (EOR) Studies

Glioblastoma EOR Study

We examine the interactive effects of volumetric extent of resection with molecular and clinical factors to develop a new roadmap for cytoreductive surgery. Based on a 20-year retrospective cohort of 850 glioblastoma patients who had initial surgery at UCSF, we employed survival models and recursive partitioning (RPA) to investigate multivariate relationships of overall survival (OS). This is the first study to combine resection of contrast-enhancing and non-enhancing tumor in conjunction with molecular and clinical information in a large single-institution study.    

Low-Grade Glioma EOR Study

This study is to test the hypothesis that extent of resection for newly diagnosed WHO II gliomas positively impacts Overall Survival, Progression Free Survival, and time to malignant transformation regardless of tumor genetics.



Gliomas are associated with an immunosuppressive network impacting the tumor microenvironment, bone marrow and the peripheral blood compartments and systemic immune suppressive cells are widely recognized in glioblastoma (GBM) patients. We are developing, testing and using a highly innovative approach called immunomethylomics to measure immunosuppression in glioma patients. Our goal is to develop a non-invasive blood test that helps assess response to glioma therapies and improves understanding of the interaction between the immune system and gliomas. We believe that blood immune profiles may be associated with overall survival and may differentiate true progression from pseudoprogression in GBM patients.


Glioblastoma Precision Medicine Program

Analysis of tumors via next-generation sequencing (NGS) is now routinely used in clinical practice.  The glioblastoma precision medicine program is to sequence all newly diagnosed WHO grade IV diffuse gliomas using our UCSF500 NGS panel since December 2017.  The UCSF500 Cancer Panel assesses approximately 500 cancer-associated genes for mutations, copy number alterations, and structural rearrangements, including fusions. We review our experience over a 3-year period.



Predicting Tumor Biology Using Multi-modality MRI and Machine Learning

Non-invasive imaging markers of tumor burden, aggressiveness, and effects of therapy are critical in identifying the appropriate therapeutic strategy for an individual patient. The modalities that have shown the most promise in quantifying surrogate markers of malignant progression in patients with gliomas include H-1 MR spectroscopic imaging (MRSI), diffusion-weighted MRI and perfusion-weighted MRI. As recent efforts to standardize acquisition and processing protocols for accumulating multi-institutional datasets are underway, there is the potential for assembling results at a large enough scale to accurately predict the biological behavior of individual tumors. The majority of prior studies have focused on linking parameters from single imaging modalities to outcome or diagnostic classification in relatively small patient populations (<50), have not taken heterogeneity of the lesion into account, especially when defining outcome measures, and have used radiographic response as a surrogate marker of pathology. The overall goal of the project is to assess the clinical value of combining multi-parametric imaging with novel advances in statistical modeling, machine learning, and artificial intelligence to evaluate tumor heterogeneity and identify regions at risk for tumor progression in patients with glioma. Predictive spatial maps of tumor biology will be generated using image-guided tissue samples to link anatomic, physiological and metabolic imaging parameters with histological characteristics. This approach will contribute to patient care by directing tissue sampling to make more accurate diagnoses, improving the characterization of residual disease, assisting in the planning of focal therapy, and detecting malignant progression.