On the role of optimization in the cancer treatment by radiation therapy (RT)
Bibliographic record
Abstract
In this paper, we present a mathematical model that analyzes cancer treatment via radiotherapy and proposes ways to control its progression. To identify preventive measures for cancer in its early stages, scientists have studied both risk factors and protective factors. In our model, we consider the disease‐free equilibrium points, namely, the trivial equilibrium (TE), healthy cell absenteeism equilibrium (HCAE), cancer cell absenteeism equilibrium (CCAE), and cancer cell incidence equilibrium (CCIE). We use nonlinear analysis techniques and the spectral radius method to study the stability and instability of systems. Since the reproduction number plays a critical role in stability analysis, we use the spectral radius method to evaluate it. We have also added a control function to the model to enhance it by including interactions between healthy and cancer cells. Optimization techniques are used to identify the limitations and needs of the problem and derive the best solutions to control the tumor. We conduct sensitivity analysis with respect to various parameters to study the model's robustness. To validate our methods and results, we provide simulations and numerical analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".