“This week in Mathematical Oncology” — Feb 19, 2026

> mathematical-oncology.org

From the editor:

Welcome to another edition of the math oncology newsletter, which features topics like clonal hematopoiesis, predictive modeling, agent-based models, adaptive therapy, and a fun blog post from Franco Pradelli called “3 facts you didn’t know about Math Onco”.

Enjoy,

Jeffrey West
jeffrey.west@moffitt.org

1. Modeling the evolutionary dynamics of clonal hematopoiesis
   Sadegh Marzban, Thomas Stiehl, Zhuoer Xie, Morten Andersen, …, Matthew J. Walter, Eric Padron, Nancy Gillis, Jeffrey West

2. Creating strong predictive models in oncology
   Michael F. Gensheimer

3. Selective sweep probabilities in spatially expanding populations
   Alexander Stein, Kate Bostock, Ramanarayanan Kizhuttil, Maciej Bak & Robert Noble

4. Optimal control theory as a method for designing multidrug adaptive therapy regimens
   Afton Widdershins, Elsa Hansen, Andrew Read & Raymond Hohl

5. Agent-based modeling of cellular dynamics in adoptive cell therapy.
   Wang, Y., Casarin, S., Daher, M. et al.

6. Optimization framework for overcoming tyrosine kinase inhibitor resistance: Multi-objective selection, scheduling, and adaptive therapy
   Mohanad A. Deif, Mohamed A. Hafez, Mohammad Khishe

1. Randomness-aware multiscale models of glioma invasion and treatment
   M. Conte, S. Hiremath, C. Surulescu

2. aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
   Pengsong Zhang, Xiang Hu, Guowei Huang, …, Lili Pan, Zhenzhong Lan, Xinyu Liu

3. Collaborative large language models (LLMs) are all you need for screening in systematic reviews
   Mihir Parmar, Syed Arsalan Ahmed Naqvi, Kainat Warraich, …, Mohammad Hassan Murad, Chitta Baral, Irbaz Bin Riaz

1. Three facts you did not know about publishing in Mathematical Oncology
   The Mathematical Oncology Blog
   Franco Pradelli: “Publishing and interacting with journals and editors is an integral part of a scientist’s job. Choosing the right venue for a scientific result is far closer to an art than to a science, and it can often be a source of stress even for experienced researchers. Here, we highlight three findings that emerged from our study, facts you may not know about publishing in Mathematical Oncology.”

The newsletter now has a dedicated homepage where we post the cover artwork for each issue, curated by Maximilian Strobl, Sarah Groves, and Veronika Hofmann. We encourage submissions that coincide with the release of a recent paper from your group. This week’s artwork:

Based on the paper: A Mechanistic Model of Brain Necrosis Progression Based on Vascular Heterogeneity published in International Journal of Radiation Oncology*Biology*Physics

Artist: Nicolò Cogno, Ibrahim Chamseddine

Caption: Radiotherapy is one of the most widely used cancer treatments, combining strong effectiveness with limited invasiveness. Thanks to decades of technological progress, treatments have become increasingly precise. Still, a small but meaningful number of patients develop long-term side effects. For people treated for brain or nasopharyngeal cancers, one of the most serious is brain necrosis, a form of tissue damage that can significantly affect cognitive function.
Why does this happen to some patients but not others who receive similar treatments? In our work, we use mathematical and computational models to explore this question. By simulating how necrosis can develop and spread in the brain under different conditions, we aim to better understand the sources of variability in patient response. Our model incorporates patient-specific features such as brain anatomy and vascular structure and uses them to generate realistic simulations of lesion growth over time. The images show these inputs alongside a simulated example of a progressing necrotic lesion. By helping explain why toxicity risk differs across patients, this modeling framework may ultimately support more personalized treatment strategies and contribute to reducing the incidence of radiation-induced brain injury.

Visit the mathematical oncology page to view jobs, meetings, and special issues. We will post new additions here, but the full list can found at mathematical-oncology.org.

1. Jobs

• PhD Studentship: Developing machine learning methods to infer the evolution of haematological cancers (Calum Gabbutt; Imperial College London)

• Research Fellow in Laboratory and Computational Systems Biology Cancer Research Ref: 43066 (Postdoc position, University of Sussex, Brigthon and Sussex Medical School, Simon Mitchell)

2. Conferences / Meetings

3. Special issues

Approximate current subscriber count, N:
N(t) = 0.808t+80 (where t = days since Dec. 1st, 2017)