Maximilian A.R. Strobl

This paper curates a large corpus of mathematical oncology literature using AI, revealing its evolution from cancer biology to therapeutic modeling over 140 years.

This paper develops and validates a treatment strategy called evolutionary antifragile therapy, which uses treatment fluctuations to limit tumor resistance evolution.

This paper develops computational frameworks to design personalized multi-strike therapies for ovarian cancer that reduce toxicity while enhancing tumor extinction.

This paper uses spatial statistics from single time-point images to classify game-theoretic interactions between drug-sensitive and resistant cancer cells using an agent-based model.

This paper develops mathematical biomarkers to predict treatment outcomes for adaptive therapy in cancer, showing personalized strategies outperform standard-of-care protocols.

This paper uses agent-based models to analyze how metastatic tumor dynamics inform adaptive therapy schedules and identifies how tumor heterogeneity affects treatment outcomes.

This study develops a mathematical model to analyze spatial interactions in ovarian cancer microenvironments and shows how niche composition affects treatment response.