Newswise — SACRAMENTO, Calif. — Ionizing radiation is one of our oldest and most valuable tools in the fight against cancer, and is still employed as a treatment in approximately half of all new cancer cases. Traditionally administered in small daily doses (“fractions”) of 2 Grays over several weeks, the field of radiation oncology has increasingly shifted toward alternative dosing schemes in which very-large-dose fractions are used. These are stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT). In these modalities, an ultra-large fraction (possibly 10+ Grays) is delivered to a tumor within a single day, and treatment only occurs on one or a handful of days.
However, a major obstacle to optimizing the success of SRS and SBRT has been an inability to predict, with acceptable accuracy, how effective a very-large-dose fraction will be at killing cancer cells. That is because the mathematical model most commonly used for predicting cell survival at a given dose—the linear-quadratic (LQ) model—is known to become inaccurate at very large doses.
A recently published paper by mathematical biologist Dr. Lydia Bilinsky, formerly of Duke University and the U.S. Food and Drug Administration (FDA), and currently a Research Fellow at the Ronin Institute for Independent
Scholarship 2.0, offers a powerful solution to this problem.
Published in BIOMATH, a journal of the Bulgarian Academy of Sciences, Dr. Bilinsky’s peer-reviewed paper introduces a novel dose-survival equation which accurately predicts cell survival across the entire clinically relevant dose range—including the very high end utilized in SRS and SBRT.
The new equation also helps with another critical hurdle for the success of SRS and SBRT, which is that some cell-cycle phases are more radiosensitive than others. In conventional multi-week fractionation, cancer cells are naturally caught on some days in their more radiosensitive phases, a fact which is relied upon for treatment success (“redistribution”). This effect is not present in SRS and SBRT, since radiation is only administered on one or a handful of days.
Figure: Sample fits of the new dose-survival equation to various in vitro data sets (black is the fit to data, red is the theoretical dose-survival curve for cells in their most radiosensitive cell cycle phases, blue is the theoretical dose-survival curve for cells in their less radiosensitive cell cycle phases). The black dose-survival curve represents the “average radiation response”that is observed for an asynchronously replicating population of cancer cells, as in an in vitro set-up. It is an average of the theoretical red and blue dose-survival curves, reflecting the fact that some of the cells were in the most radiosensitive phases, while others in the less radiosensitive phases, of the cell cycle when ionizing radiation was administered.
Dr. Bilinsky’s model addresses this problem by uncovering the dose-survival relationship which governs cells while they are in their less-radiosensitive phases (shown in blue in the figure). By utilizing this relationship rather than the phase-averaged one (shown in black in the figure) to select fraction size, clinicians could theoretically do away with the need for radiation to catch cells in their more radiosensitive phases.
Although conventional fractionation suffices for the successful treatment of many types of tumor, a large number of clinic visits are required, increasing costs and inconveniencing patients. Furthermore, some tumors are known to be treated more successfully using larger fraction sizes. Incorporating Dr. Bilinsky’s equation into the design of future clinical trials investigating SRS and SBRT should do much to increase their success rates, ultimately improving the treatment options available to cancer patients.
About the Article
The research was conducted by Dr. Lydia Bilinsky, a Fellow at the Ronin Institute for Independent Scholarship 2.0, a distributed research organization supporting independent scholars. Full computational materials and reproducibility protocols have been archived for independent verification. The views, findings, and conclusions expressed in the paper and this release are those of the author, Dr. Bilinsky, and do not necessarily represent the views or positions of the Ronin Institute for Independent Scholarship 2.0.
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About the Ronin Institute for Independent Scholarship 2.0
The Ronin Institute for Independent Scholarship 2.0 supports a global community of scholars pursuing research outside of traditional academic institutions. By providing infrastructure, collaboration networks, and scholarly identity, the Institute enables rigorous, independent inquiry across the sciences and humanities. Website: ronininstitute.org/
Note to editors:
The article is available online at:
https://biomath.math.bas.bg/biomath/index.php/biomath/article/view/j.biomath.2025.07.035
The author welcomes inquiries regarding the framework’s methodology and implications.
www.ronininstitute.org Sacramento, CA 95816 USA [email protected]