Secondary cancer risk in head‐and‐neck cancer patients: A comparison of RBE‐weighted proton therapy and photon therapy
Bibliographic record
Abstract
Abstract Background Secondary cancer is a serious side effect from external beam radiotherapy (EBRT). Conventional EBRT is performed using a beam of photons, however, due to their ability to produce more conformal dose distributions, the use of protons is becoming more wide‐spread. Due to this sparing it would be expected that proton therapy could be associated with lower secondary cancer rates compared to photon therapy. However, since proton therapy data is still being accumulated and the follow‐up period is often relatively short thus far, simulation studies can complement the existing data and extrapolate to longer time frames. Purpose This study aims to estimate and compare the risk of secondary cancer when treating head‐and‐neck cancer patients with proton therapy or photon therapy, while combining a whole‐body computational human phantom with the patient treatment planning computed tomography (CT) scan in order to study organs that are partially or fully outside of the treatment planning CT. In addition, proton therapy secondary cancer rates are investigated further by including variable relative biological effectiveness (RBE) models. Methods For 20 head‐and‐neck cancer patients, two clinical radiotherapy treatment plans were created, one for proton therapy and one for photon therapy. For proton therapy, linear energy transfer (LET) distributions were simulated and used to calculate the variable RBE‐weighted dose distributions for six different variable RBE models, in addition to the constant RBE of 1.1 widely used clinically. In order to obtain the dose deposited outside the treatment planning CT scan, an adjustable whole‐body digital reference phantom was stitched to the treatment planning CT. Based on the resulting dose distributions, the risk of secondary cancer was calculated for each modality. Results Averaged across all patients and relevant organs, photon therapy compared to proton therapy with a constant RBE of 1.1 was estimated to be 1.8 times more likely to cause secondary cancer. This risk ratio varied between 1.6 and 2.0, depending on the variable RBE model used. Cases with lifetime attributable risk (LAR) values below 0.1% were excluded from this analysis to prevent the benefits of proton therapy (the ratio ) from being artificially elevated in cases in which . Conclusion Proton therapy was associated with lower estimated secondary cancer rates compared to photon therapy when treating head‐and‐neck cancer patients. This trend was observed even when considering different variable RBE models to calculate the proton therapy dose distributions.
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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".