BIOMARKERS FOR PREDICTING MANDIBULAR OSTEORADIONECROSIS IN ORAL AND OROPHARYNGEAL CANCER PATIENTS
Bibliographic record
Abstract
Background: Mandibular osteoradionecrosis (ORN) is a severe complication following radiotherapy (RT) in patients with oral and oropharyngeal cancers. It is characterized by non-healing necrotic bone, ORN leads to significant morbidity, including pain, infection, and impaired oral function. Identifying biomarkers that can predict ORN risk is crucial for early intervention and personalized treatment, potentially reducing the severity and incidence of ORN. Objective: This systematic review aims to evaluate the current evidence on biomarkers that predict the development of ORN in patients receiving radiotherapy for oral and oropharyngeal cancers. By identifying key molecular and genetic markers, the review seeks to highlight potential clinical applications in risk stratification and patient management. Methodology: A systematic search was conducted in PubMed, EMBASE, and Cochrane databases, including studies published between 2000 and 2024. Studies that examined associations between biomarkers and ORN risk in head and neck cancer patients treated with radiotherapy were included. Data extraction focused on patient demographics, biomarker types, study outcomes, and incidence of ORN. The quality of studies was assessed using standardized tools such as the Newcastle-Ottawa Scale and Cochrane Risk of Bias tool. Results: 72 studies met the inclusion criteria, identifying several key biomarkers associated with ORN risk. These include inflammatory cytokines (e.g., TNF-α, IL-6), matrix metalloproteinases (MMP-2, MMP-9), and hypoxia-inducible factors (HIF-1α). Elevated levels of these markers were significantly correlated with increased risk of ORN, reflecting their role in inflammation, tissue hypoxia, and impaired bone healing. Genetic polymorphisms in bone remodeling and angiogenesis pathways, such as VEGF and BMP, were also linked to ORN susceptibility, though further validation is required. Conclusion: This systematic review highlights the potential of biomarkers in predicting ORN risk in patients undergoing radiotherapy. The data underscore the need for larger studies to confirm these findings and integrate biomarkers into clinical practice.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".