H. Enderling

Logistic tumor-population growth and ghost-points symmetry

This paper introduces a method using logistic function symmetry to estimate tumor growth parameters from limited data, achieving faster and more accurate results than conventional regression approaches.

Breast Cancer Reaction-Diffusion from Spectral-Spatial Analysis in Immunohistochemistry

The paper uses spatial-spectral analysis of immunohistochemistry data to model tumor growth dynamics via reaction-diffusion equations in breast cancer patients.

Classical Mathematical Models for Prediction of Response to Chemotherapy and Immunotherapy

This study evaluates classical mathematical models for predicting chemotherapy and immunotherapy responses using real-world patient tumor volume data, finding the Gompertz model provides the best balance between fit and complexity.

High school Internship Program in Integrated Mathematical Oncology (HIP IMO) - five-year experience at Moffitt Cancer Center

The paper describes a high school internship program in mathematical oncology, detailing its structure, curriculum, and outcomes over five years.

T2 heterogeneity provides a sensitive measure of early tumor response to radiotherapy

This paper introduces a T2 MRI-based biomarker to assess early tumor response to radiotherapy in pancreatic cancer models, showing high sensitivity and robustness.

Harnessing tumor immune ecosystem dynamics to personalize radiotherapy

This study uses a computational model to analyze tumor immune ecosystem dynamics and predict how radiotherapy affects immune responses in lung cancer patients.

Predicting patient-specific radiotherapy responses in head and neck cancer to personalize radiation dose fractionation

The paper introduces a mathematical model, PSI, to predict radiotherapy response in HPV+ oropharyngeal cancer and suggests hyperfractionation for higher PSI patients.

Prostate-Specific Antigen Dynamics Predict Individual Responses to Intermittent Androgen Deprivation

The paper uses mathematical models of prostate cancer stem cell dynamics to predict individual responses to intermittent androgen deprivation therapy based on prostate-specific antigen (PSA) measurements.

Harnessing patient-specific response dynamics to optimize evolutionary therapies for metastatic clear cell renal cell carcinoma - Learning to adapt

This paper develops a dynamic tumor-immune model to predict patient response to treatment using NLR as a biomarker and simulates optimal therapies for virtual RCC patients.

Overcoming non-small cell lung cancer radiation resistance by modulating the tumor-immune ecosystem

This paper proposes mathematical models to adapt radiotherapy plans based on tumor-immune ecosystem composition and immune responses to improve outcomes in non-small cell lung cancer.

Proliferation Saturation Index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses

This paper develops a Bayesian model using proliferation saturation index to predict individual patient radiotherapy responses from clinical data.

Temporally feathered intensity modulated radiation therapy: a planning technique to reduce normal tissue toxicity

This paper introduces a novel radiation therapy planning technique called temporally feathered radiotherapy (TFRT) that reduces normal tissue toxicity by optimizing the timing of radiation doses.

The evolution of tumour composition during fractionated radiotherapy: implications for outcome

This study models how tumour oxygen levels change during radiotherapy and shows how oxygen heterogeneity affects radiation response and treatment outcomes.