Range uncertainty reductions in proton therapy and resulting improvements in quality-adjusted life expectancy (QALE) for head-and-neck cancer patients
Bibliographic record
Abstract
Abstract Objective. Due to higher dose conformality to the target, proton radiotherapy for cancer has received rapidly-growing interest. However, uncertainties in the in vivo proton range and methods to reduce them remain active areas of research. Based on 20 patients with head-and-neck cancer, this study aims to quantify the benefits of proton range uncertainty reductions in terms of the resulting improvements in quality-adjusted life expectancy (QALE). Approach. For each patient, two different proton therapy treatment plans were created, which assumed a current clinical range uncertainty of approximately 3.5% (IMPT3.5%) and a potentially achievable range uncertainty of 1.0% (IMPT1%). A Markov model considering the probability of tumor control and the development of xerostomia, larynx edema, secondary cancer, and/or metastases as well as death from primary cancer, secondary cancer, metastases, or unrelated causes was constructed, and for every patient and treatment plan, 10 000 simulations of the patient’s entire lifetime from the time of treatment until death were performed. Main results. A 3.5%–1% range uncertainty reduction increased QALE by up to 0.4 quality-adjusted life years (QALYs) in the nominal and up to 0.6 QALY in the worst-case scenario, equivalent to 4.8 months and 7.2 months of life in perfect health. This was largely the result of a reduction in healthy tissue toxicity rates, which were reduced by up to 8.5 percentage points (pp) and 10.0 pp in the nominal and worst-case scenario, respectively. Significance. The benefits of a 3.5%–1% range uncertainty reduction in 20 patients with head-and-neck cancer were quantified in terms of the associated improvement in QALE. The highest QALE improvements were observed in patients in the top quartile of youngest patients at the time of treatment, due to the longer potential lifespan over which prevented healthy tissue toxicities would have impacted the patients’ quality of life.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".