Heiko Enderling

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Assistant Professor / Assistant Investigator

Tufts University School of Medicine

United States

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About

Heiko Enderling is an Assistant Professor and Assistant Investigator at Tufts University School of Medicine in the United States. His research areas focus on mathematical oncology, machine learning applications in personalized radiotherapy, tumor growth dynamics, and immunotherapy strategies. Recent publications include studies on Bayesian predictions in high-grade gliomas, photoimmunotherapy effects, and mathematical modeling of tumor progression.

Recent Grants

Grant: Close

Increasing the likelihood of a patient-specific abscopal effect in metastatic renal cell carcinoma

Open Date: 2014-04-01

Close Date: 2016-06-01

Grant: Close

Intercellular interactions modulate carcinogenesis course: a dynamic systems study

Open Date: 2011-02-01

Close Date: 2015-02-01

Grant: Close

Paradoxical Proliferation-apoptosis-migration Dynamics in Tumor Progression

Open Date: 2008-06-01

Close Date: 2011-05-01

Grant: Close

Mathematical Modelling of Intraoperative Radiotherapy for Early Breast Cancer

Open Date: 2006-06-01

Close Date: 2006-12-01

Articles (17)

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2025

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2021

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2020

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2020

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2020

Collaborators (7)

Bryan Spring

Associate Professor

Northeastern University

UNITED STATES

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Kateřina Staňková

Delft University of Technology

NETHERLANDS

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Olaf Wolkenhauer

Professor

University of Rostock

GERMANY

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Tayyaba Hasan

Massachusetts Institute of Technology

UNITED STATES

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Jan Poleszczuk

Assistant Professor

Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences

POLAND

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Katarzyna Rejniak

University of South Florida

UNITED STATES

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Chiara Maria Lavinia Loeffler

Technische Universität Dresden

GERMANY

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Heiko Enderling

Research Interests

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About

Recent Grants

Articles (17)

Personalized radiotherapy doses from the integration of mathematical oncology and machine learning

A sample size analysis of a mathematical model of longitudinal tumor volume and progression‐free survival for Bayesian individual dynamic predictions in recurrent high‐grade glioma

Fractionated photoimmunotherapy stimulates an anti-tumour immune response: an integrated mathematical and in vitro study

Harnessing Flex Point Symmetry to Estimate Logistic Tumor Population Growth

Calibrating tumor growth and invasion parameters with spectral spatial analysis of cancer biopsy tissues

Fractal calculus in tumor growth simulations: The proof is in the pudding

Simulating tumor volume dynamics in response to radiotherapy: Implications of model selection

Predicting Patient-Specific Tumor Dynamics: How Many Measurements Are Necessary?

Classical mathematical models for prediction of response to chemotherapy and immunotherapy

Range-Bounded Adaptive Therapy in Metastatic Prostate Cancer

Rethinking the immunotherapy numbers game

Mathematical oncology: A new frontier in cancer biology and clinical decision making

Bayesian Framework to Augment Tumor Board Decision Making

Are all models wrong?

High School Internship Program in Integrated Mathematical Oncology (HIP IMO): Five-Year Experience at Moffitt Cancer Center

Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma

Prostate-specific antigen dynamics predict individual responses to intermittent androgen deprivation

Collaborators (7)

Bryan Spring

Kateřina Staňková

Olaf Wolkenhauer

Tayyaba Hasan

Jan Poleszczuk

Katarzyna Rejniak

Chiara Maria Lavinia Loeffler

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