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Record W4409741135 · doi:10.1088/1361-6560/add07d

Range uncertainty reductions in proton therapy and resulting improvements in quality-adjusted life expectancy (QALE) for head-and-neck cancer patients

2025· article· en· W4409741135 on OpenAlexafffund
Sebastian Tattenberg, Peilin Liu, Anthony Mulhem, Xiaoda Cong, Christopher Thome, Cornelia Hoehr, Xuanfeng Ding

Bibliographic record

VenuePhysics in Medicine and Biology · 2025
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsNOSM UniversityLaurentian UniversityTRIUMF
FundersNational Research Council CanadaMitacs
KeywordsProton therapyLife expectancyMedicineHead and neck cancerLarynxCancerQuality-adjusted life yearQuality of life (healthcare)Radiation therapyNuclear medicineSurgeryInternal medicineCost effectivenessEnvironmental healthRisk analysis (engineering)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.224
GPT teacher head0.461
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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