A mathematical study of the impact of cell plasticity on tumour control probability
Bibliographic record
Abstract
The tumour control probability (TCP) is a treatment planning tool that evaluates the probability of tumour eradication and helps in the assessment of the relative efficacy of different radiotherapy regimens. The response of tumours to radiation differs greatly even between patients with same types of cancers. Tumour heterogeneity or cellular diversity among cancer cells has a pronounced impact on the success of administered radiotherapy protocols. Tumour heterogeneity can be explained using the cancer stem cells (CSCs) hypothesis, which posits that CSCs are responsible for tumour initiation and propagation as well as therapeutic resistance. Moreover, the existence of plasticity or bidirectional transition between CSCs and non-CSCs indicates that, sometimes, non-CSCs appear to mimic CSC phenotypes, resulting in an increase in resistance. Here, we have developed a stochastic model to investigate the impact of plasticity on the efficacy of radiotherapy. The effect of plasticity on TCP is explored by applying the model to standard and hyper-fractionated schedules for a three week period of treatment as well as standard, hyper-fractionated, and accelerated hyper-fractionated schedules with an equal total dose of 30 Gy. Our results confirm that tumour control becomes increasingly difficult in the presence of plasticity as well as for the most resistant tumours. For the case with equal total dose, it is observed that increasing fractionation, at first enhances the probability of CSCs and tumour removal, but ultimately results in lower TCPS+P and TCPS. In addition, the combination of radiotherapy and targeted therapy (with increasing CSC differentiation) improves both the probability of CSC and tumour removal, in the absence of plasticity. However, in the presence of plasticity, the impact of combination therapy is not significant.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".