Proton arc therapy and associated improvements in quality-adjusted life expectancy for head-and-neck cancer patients compared to volumetric modulated arc therapy and intensity-modulated proton therapy
Bibliographic record
Abstract
Abstract Objective. Proton arc therapy (PAT), which relies on target irradiation from arc trajectories rather than just a few angles, is currently under investigation and has demonstrated dosimetric benefits over current clinical intensity-modulated proton therapy (IMPT) in numerous in silico studies. However, the resulting impact on the patient’s quality-adjusted life expectancy (QALE), which takes into account quality-of-life reductions resulting from e.g. healthy tissue toxicities, remains to be investigated. This study aims to compare photon-based volumetric modulated arc therapy (VMAT) to IMPT and PAT with respect to the associated QALE. Approach. For each of 20 patients with head-and-neck cancer, three treatment plans (VMAT, IMPT, and PAT) were generated, and a Markov model was constructed to simulate the associated QALE. The model—iterations of which have previously been used for comparisons of photon and proton therapy (but not PAT)—considered the calculated tumor control probability, the probability of healthy tissue toxicities, metastases, and/or secondary cancer, and death from primary cancer, secondary cancer, metastases, or unrelated causes. For all patients and treatment plans, 10 000 simulations of the patient’s entire lifespan subsequently to treatment were performed. Main results. Mean values and standard deviations of IMPT and PAT QALE improvements relative to VMAT were (0.1 ± 0.2) quality-adjusted life years (QALYs) and (0.3 ± 0.2) QALY, respectively. The highest benefits observed were 0.5 QALY in the case of IMPT and 1.0 QALYs in the case of PAT, equivalent to 6.0 months and 12.0 months of life in perfect health. Compared to IMPT, PAT improved QALE by up to 0.8 QALYs (9.6 months; (0.2 ± 0.2) QALY). Significance. PAT was associated with higher QALE values in all 20 cases compared to VMAT and IMPT. In 12 cases, the QALE benefits of utilizing PAT rather than IMPT consistently exceeded the benefits of utilizing IMPT rather than VMAT.
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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 | 0.003 |
| 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".