Hypo-fractionated boost in locally advanced non-small cell lung cancer: temporal distribution of boost fractions
Bibliographic record
Abstract
To propose new schemas for radiation boosting of primary tumors, in locally advanced non-small cell lung cancers (NSCLC), in conjunction with standard chemoradiotherapy. To investigate the effect of temporal distributions of the boost fractions on tumor control. NSCLC cases, previously treated with 60 Gy in 30 fractions, were retrospectively planned by adding a radiation boost (25 Gy in 5 fractions) to the primary tumor. Several integrated and sequential boosting schedules were considered. Biological doses were calculated for targets and organs at risk (OAR). Tumor control probabilities (TCP) were calculated using an empirical model and a stochastic model that accounts more systematically for tumor growth kinetics and cell kill. For heterogeneous patient populations, the TCPs for different boost schedules ranged from 82% to 84% and from 73% to 74% for integrated and sequential boosting respectively. For individual tumors with specific growth parameters, the TCP varied by up to 19% between the different schedules. The TCP for sequential boosting was expected to be up to 67% lower than front integrated boosting. The gap in TCP between schedules was higher for tumors with higher clonogenic cell numbers, lower radio-sensitivity, shorter doubling times and lower cell loss. The proposed boosting schemas are dosimetrically feasible and biologically effective. We suggest that the boosts are most effective when given during the first week of treatment and least effective when given sequentially after the end of treatment. The effect of boost scheduling and the effectiveness of front boosting are expected to be most significant for tumors with high clonogenic cell numbers, fast growing rates, low cell loss and low radio-sensitivity. Ultimately, animal studies and clinical trials, guided by biology modeling as presented in the present work, will be needed to verify the effectiveness of fine tuning temporal distributions of radiotherapy fractions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".