Piecewise affine systems modelling for optimizing hormone therapy of prostate cancer
Bibliographic record
Abstract
Prostate cancer is one of the most common malignant neoplasms in men with an overall incidence of approximately 15 per cent during the normal life span. Androgen-deprivation therapy (hormone therapy) is an effective treatment of this disease when progressed to an advanced stage. Despite impressive responses, such treatment when applied on a continuous basis is not curative and eventually culminates in androgen-independent disease. On the other hand, intermittent androgen suppression (IAS) was first conceived as a potential way of delaying progression to androgen-independence, in addition offering the possibility of reducing adverse effects and improving the quality of life. Although the validity of this approach has been confirmed in several clinical studies, the optimal scheduling of the cycles of on- and off-treatment remains to be explored. In the present article, we show that IAS lends itself to mathematical modelling with hybrid dynamical systems and that the model we have developed can be used to select the best strategy for keeping prostate cancer in an androgen-dependent state as long as possible. Our results also suggest that the current way of using IAS exceeds what is necessary for optimal control; in fact, we have found that to achieve optimal control, the amount of therapy (dose and duration of drugs) can be reduced by a factor of one half.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".