[RAISE at UCR] Fwd: Colloquium, Dr. Rockne from City of Hope on Wednesday, November 12

[Vassilis Tsotras]

Please find below information about an upcoming colloquium from the Center
for Quantitative Modeling in Biology on an AI-driven framework for
analyzing MRI tumor data.

V. Tsotras

_____________________

Colloquium
Interdisciplinary Center for Quantitative Modeling in Biology

Prof. Russel Rockne
<https://www.cityofhope.org/research/find-a-scientist/russell-rockne>,
Director
Division of Mathematical Oncology and Computational Systems Biology
City of Hope <https://www.cityofhope.org/>

Time: 12:30pm, November 12, 2025, Wednesday
Location: Skye Hall 284

Title: Mathematical modeling in cancer research: how to build, validate, and
apply models with biologists and clinicians

Abstract: The integration of machine learning with mechanistic modeling is t
ransforming cancer research. This lecture introduces Localized Convolutional
Function Regression (LCFR), a novel AI-driven framework for analyzing
dynamic
contrast-enhanced MRI (DCE-MRI) data to noninvasively quantify interstitial
fluid
transport in tumors. LCFR leverages weak-form regression and domain-specific
basis functions to estimate spatially varying coefficients of partial
differential
equations governing advection-diffusion-reaction dynamics. This approach
enables
simultaneous measurement of perfusion, diffusion, and interstitial fluid
velocity in 3D,
overcoming limitations of traditional voxel-wise ODE fitting and enhancing
interpretability
and computational efficiency. Key topics will include: The mathematical
formulation
of LCFR and its connection to sparse identification of nonlinear dynamics
(SINDy).
Validation across in silico, in vitro, and in vivo models, including
hydrogel phantoms
and murine glioma. Application to clinical imaging data from glioblastoma
and breast
cancer patients, revealing tissue-specific differences in fluid dynamics.
Implications
for understanding tumor microenvironment, drug delivery, and treatment
response.
This lecture will provide attendees with a conceptual and practical
foundation for
integrating AI-based model discovery into clinical imaging workflows,
offering new
avenues for personalized cancer modeling and predictive analytics in
oncology,
as well as perspective on how to build, validate, and apply models with
biologists
and clinicians.
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