Program

The conference schedule may be downloaded here.

Computational Oncology

Schedule

All times are listed in Central Standard Time.

MONDAY, January 10, 2022

7:30 - 7:40 Welcome

Session 1 Mathematical Modeling of Tumor Growth - Fundamental Ideas (Part 1)

7:40 - 8:30 J. Tinsley Oden, University of Texas at Austin

Goal-Oriented A-Posteriori Estimation of Model Error as an Aid to Parameter Estimation: Application to Nonlinear BVPs and Models of Tumor Growth

8:30 - 9:00 Hector Gomez, Purdue University

Inverting Tumor Angiogenesis with Interstitial Flow and Chemokine Matrix-binding Affinity

9:00 - 9:20 Saskia Haupt, University of Heidelberg

Mathematically Modeling Multiple Pathways of Carcinogenesis Using the Kronecker Structures

9:20 - 9:40 Paras Jain, Indian Institute of Science, Bangalore

Population Dynamics of Epithelial-Mensenchymal Heterogeneity in Cancer Cells

9:40 - 10:00 Guillermo Lorenzo, University of Texas at Austin

Biomechanical Interactions Between Prostate Cancer and Coexisting Benign Prostatic Hyperplasia

10:00 - 10:30 Break


Session 2 Mathematical Modeling of Tumor Growth - Fundamental Ideas (Part 2)

10:30 - 11:00 John Lowengrub, University of California

Predictive Nonlinear Modeling of Malignant Myelopoiesis and TKI Therapy

11:00 - 11:30 Luciana R C Barros, Cancer Institute of Sao Paulo

Cells Immunotherapy: From the Bedside to Mathematical Modeling

11:30 - 11:50 Daniel Comacho-Gomez, University of Zaragoza

Hybrid Discrete-Continuum Model to Evaluate DIPG Cells Invasion and Matrix Degradation

11:50 - 12:10 Myrianthi Hadjicharalambous, University of Cyprus

Understanding Anti-Angiogenic Treatment and Metronomic Chemotherapy Through a Computational Cancer Model

12:10 - 12:30 Rui Travasso, University of Coimbra

Intratumoral VEGF Nanotrapper Reduces Gliobastoma Vascularization and Tumor Cell Mass

12:30 - 14:00 Lunch Break


Session 3 Integrating Data with Models - Advances and Challenges (Part 1)

14:00 - 14:30 Thomas Yankeelov, University of Texas at Austin

Towards Imaging-Based Digital Twins for Clinical Oncology

14:30 - 15:00 Russell Rockne, Beckman Reserach Institute of City of Hope

"Big" Data for Mathematical Models: Too Much and Not Enough

15:00 - 15:20 Baoshan Liang, University of Buffalo

An Image-Based Bayesian Framework for Subject-Specific Tumor Growth Prediction

15:20 - 15:40 Stefano Pasetto, H. Lee Moffitt Cancer Center and Research Institute

Intermittent Hormone Therapy Models Analysis and Bayesian Model Comparison for Prostate Cancer

15:40 - 16:00 Caleb Phillips, University of Texas at Austin

Assessing the Invertibility of Model Selection Frameworks for the Prediction of Patient Outcomes for Clinical Breast Cancer

TUESDAY, January 11

Session 4 Integrating Data with Models - Advances and Challenges (Part 2)

8:00 - 8:30 Mohit Kumar Jolly, Indian Institute of Science, Bengaluru

Phenotypic Heterogeneity in Melanoma: Integrating Data-Based and Mechanism-Based Approaches

8:30 - 9:00 Heiko Enderling, H. Lee Moffitt Cancer Center and Research Institute

Harnessing the Complex Tumor-Immune Interactions with Radiotherapy

9:00 - 9:20 Vikram Adhikarla, Beckman Research Institute, City of Hope

Modeling Tumor Response to Targeted Radionuclide Therapies

9:20 - 9:40 Zoi Tokoutsi, Philips Research

Challenges in Model Validation and Personalization: An Animal Study for Microwave Ablation

9:40 - 10:00 Barbara Wirthl, Technical University of Munich

Multiphase Porous Media Models of Tumor Growth - A Global Sensitivity Analysis Based on

Gaussian-Process Metamodeling

10:00 - 10:30 Break


Session 5 Emerging Opportunities - Advanced Modeling and Imaging Techniques (Part 1)

10:30 - 11:00 Katarzyna Rejniak, Moffit Cancer Center

Modeling ECM Mechanical and Metabolic Architecture During Early Ductal Invasions

11:00 - 11:30 Savannah Partridge, University of Washington

Functional MRI Signatures of Breast Tumor Microenvironment and Immune Response

11:30 - 11:50 Xavier Gracia-Andres, Universitat Politecnica de Valencia

Structural Characterization of Cancerous Vertebrae by Combining Machine Learning Techniques with the Cartesian Grid FEM

11:50 - 12:10 Anum Kazerouni, University of Washington

Identification of Pre-Treatment Tumor Habitats for the Prediction of Neoadjuvant Therapy Response in Triple Negative Breast Cancer

12:10 - 12:30 Lahcen Akerkouch and Trung Le, North Dakota State University

A Hybrid Particle-Continuum Approach for Modelling Cellular Dynamics in Flows.

12:30 -14:00 Lunch Break


Session 6 Emerging Opportunities - Advanced Modeling and Imaging Techniques (Part 2)

14:00 - 14:30 John Hazle, MD Anderson Cancer Center

Emerging Opportunities - Advanced Modeling and Imaging Techniques

14:30 - 15:00 David Fuentes, MD Anderson Cancer Center

An Information Theoretic Approach Toward Hyperpolarized Data Acquisition in Brain

15:00 - 15:20 Debosmita Biswas, University of Washington

Diffusion Weighted Imaging for Improving the Diagnostic Performance of Breast MRI

15:20 - 15:40 Kishore Hari, Indian Institute of Science

Mechanistic Modeling of Cancer Therapy Resistance

15:40 - 16:00 Sarthak Sahoo, Indian Institute of Science

Roles of Phenotypic Plasticity and Non-Genetic Hyeterogeneity in Growth in ER+ Breast Cancers

16:00 - 16:30 Concluding Session