Chul Moon
Southern Methodist University, TX·Mathematics & Statistics
Doctors use the shape and pattern of a tumor as clues for diagnosis and treatment. Modern medical imaging now shows tumors in high detail, yet most studies describe tumor shape in limited ways.
- Fields
Mathematics & Statistics
Computer Science
Medicine & Clinical Research
- Topics
topological data analysis
persistent homology
tumor shape analysis
- Grants
- 1 known grant, through Jun 2027
What they work on
- Develop methods that pair a tumor's location with its topological shape features in three-dimensional MRI scans of gliomas, and test how these features relate to the tumors' genomic characteristics.
- Build topological shape analyses of regions containing different cell types and their interactions in lung adenocarcinoma pathology images, using shape descriptions that do not depend on image size.
- Test whether topological tumor features predict prognosis and survival beyond standard clinical factors, using survival models applied to glioma and lung adenocarcinoma images.
- Release user-friendly software and educational resources so other researchers can apply topology-based tumor shape analysis to medical images.
More about the research
This project uses topology, the branch of mathematics that describes shape through features such as connected pieces, loops and holes. It aims to turn tumor images into numerical shape descriptions and build statistical models that link those descriptions to patient outcomes. The team's preliminary results suggest that topological image features capture tumor shape and predict prognosis and survival, even after accounting for key clinical factors.
Why it matters
Tumor shape and growth patterns may influence how cancers progress and spread. Measuring shape with topology could give doctors prognostic information beyond standard clinical factors for patients with lung adenocarcinoma or glioma. It could also help link shape to tumor genomics and support treatment decisions. Free software from the project could let other researchers apply these methods.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Tao Hou
University of Oregon, OR·Computer Science
Devise efficient algorithms and new data structures for computing representatives, the concrete cycles in a dataset that realize each feature found by persistent homology, a tool tracking holes and loops across scales.
Brittany Fasy
Montana State University, MT·Mathematics & Statistics·Early-career
Quantify how well topological transforms, families of descriptors such as persistence diagrams or Euler characteristic curves computed over many directions or subspaces, represent a topological space.
Mohammed Qazi
Tuskegee University, AL·Mathematics & Statistics
Develop the mathematics of diffusion models for generative AI, which create new data by reversing a noising process, using stochastic modeling with applications such as wireless communications.
The school
Southern Methodist University: 7 labs on PI Pages, $63M a year on research, 136 research doctorates a year.
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