SP-0335: Clinical data informing dose/dose per fraction and scheduling strategies
Bibliographic record
Abstract
It is well-known that radiotherapy modulates the immune system in such a way that together with immune therapy it may provoke a local and systemic anti-cancer immune response.As there is substantial heterogeneity between patients, tumours, the micro-environment and intratumour-heterogeneity, it would be surprising that the radiation-immune response would not be affected by classical radiotherapy parameters such as dose, dose per fraction, dose rate, overall treatment time and the timing of radiotherapy with a certain immune therapy.Pre-clinical models may give a clue to all these questions, but as human tumours and the immune system are fundamentally different from that of rodents, a simple extrapolation from lab results to patients is inappropriate.Clinical trials remain therefore essential.To the best of my knowledge, at the time of writing, no prospective studies that specifically were designed to address dose/ fractionation questions have been published.Moreover, in the absence of established biomarkers, the only endpoints that can be used are related to clinical outcome, such as response rates, progression-free survival (PFS) and overall survival (OS).Prospective non-randomized studies that combined interferon, IL-2, GM-CSF or ipilimumab with radiotherapy ranged from a single fraction of 8 Gy to 60 Gy in 2 Gy fractions, with no clear differences in outcome.Abscopal responses were observed with radiotherapy in patients who progressed after ipilimumab and in whom IO was continued after 30 Gy/ 10 fractions, 20/5 and 24/1.In NSCLC, abscopal respons was seen after 30 Gy/ 10 fractions.In conclusion, the optimal dose/ fractionation to induce immune activation is far from being elucidated and may occur over a wide range of doses and fractionations. SP-0336 Ongoing and upcoming clinical trials evaluating different RT schedules in combination with immunotherapy F.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".