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Record W4416704666 · doi:10.1001/jamaoto.2025.4179

Osteoradionecrosis After Intensity-Modulated Radiation Therapy or Proton Therapy in Oropharyngeal Carcinoma

2025· article· en· W4416704666 on OpenAlexaff
Fan Yang, Edward Christopher Dee, Annu Singh, Yingzhi Wu, James O. Suggitt, Teeradon Treechairusame, Elizabeth Silverio Polanco, Arya Honawar, Zhigang Zhang, Dennis Mah, Kevin Sine, Andy Shim, Haibo Lin, Jung Julie Kang, C. Jillian Tsai, Sean M. McBride, Nadeem Riaz, Daphna Y. Gelblum, Kaveh Zakeri, Linda Chen, A. Shamseddine, Yao Yu, Joseph M. Huryn, SaeHee K. Yom, Jennifer R. Cracchiolo, Ian Ganly, Marc A. Cohen, Eric J. Sherman, Alan L. Ho, Richard J. Wong, Cherry L. Estilo, Nancy Y. Lee

Bibliographic record

VenueJAMA Otolaryngology–Head & Neck Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsOsteoradionecrosisRadiation therapyProton therapyRetrospective cohort studyHomogeneousCohortCarcinoma

Abstract

fetched live from OpenAlex

Importance: Osteoradionecrosis (ORN) is a potentially debilitating late complication of radiotherapy (RT) for head and neck cancer. While proton therapy offers superior dose conformality, its impact on ORN risk remains uncertain, particularly in patients with oropharyngeal squamous cell carcinoma (OPSCC). Objective: To characterize the incidence, severity, and predictors of ORN in a large institutional cohort of patients with OPSCC treated with curative-intent RT, and to compare outcomes between proton therapy and intensity-modulated radiation therapy (IMRT). Design, Setting, and Participants: This retrospective cohort study included consecutive patients with OPSCC treated from January 2013 to December 2023 at a single high-volume academic institution. Patients received either IMRT or proton therapy (uniform scanning or pencil beam scanning). ORN diagnosis and grading were determined through standardized multidisciplinary review. The primary outcome was the 3-year rate of ORN. Cox regressions identified predictors of ORN. Data were analyzed from December 2024 to April 2025. Exposures: Radiotherapy modality (proton vs IMRT), patient demographic characteristics, smoking status, human papillomavirus status, tumor and/or node stage, chemotherapy, and radiation dosage. Results: The analysis included 1564 patients (mean [SD] age, 61.5 [9.6] years; 208 females [13.3%] and 1356 males [86.7%]) of whom 1389 patients (88.8%) had undergone IMRT, and 175 patients (11.2%) had undergone proton-based treatment. The 3-year incidence of any-grade ORN was 3.02% (95% CI, 2.22%-4.09%). ORN rates were significantly higher after proton therapy compared with IMRT (6.36% vs 2.69% at 3 years; hazard ratio [HR], 2.62; 95% CI, 1.39-4.93). Of 1344 definitive patients, 47 of 1210 patients in the IMRT group (3-year rate, 2.38%; 95% CI, 1.61-3.51%) developed ORN vs 11 of 134 patients in the proton group (3-year rate 7.47%; 95% CI, 3.40-16.02% [HR, 3.62; 95% CI, 1.85-7.09]). On multivariable analysis, proton therapy (HR, 2.92; 95% CI, 1.55-5.50), concurrent chemotherapy (HR, 3.29; 95% CI, 1.03-10.50), and smoking (HR, 2.33; 95% CI, 1.38-3.92) were independently associated with ORN. Grade 3 or greater ORN occurred in 0.67% of patients and did not appear to differ by RT modality. Conclusions and Relevance: In this large, relatively homogeneous cohort of patients with OPSCC, proton therapy was associated with a higher rate of ORN compared with IMRT, particularly in the definitive setting, although high-grade ORN remained uncommon across both modalities. These retrospective findings should be considered exploratory and underscore the need for future hypothesis-driven studies to refine dose constraints and optimize treatment planning to mitigate ORN risk among patients with OPSCC.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.322
Teacher spread0.288 · 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 designObservational
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".

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Citations3
Published2025
Admission routes1
Has abstractyes

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