The Effect of Radiation and Chemoradiation Therapy on the Head and Neck Mucosal Microbiome: A Review
Bibliographic record
Abstract
Radiation (RT) and chemoradiation therapy (CRT) play an essential role in head and neck cancer treatment. However, both cause numerous side effects in the oral cavity, paranasal sinuses, and pharynx, having deleterious consequences on patients' quality of life. Concomitant with significant advances in radiation oncology, much attention has turned to understanding the role of the microbiome in the pathogenesis of treatment-induced tissue toxicity, to ultimately explore microbiome manipulation as a therapeutic intervention. This review sought to discuss current publications investigating the impact of RT and CRT-induced changes on the head and neck microbiome, using culture-independent molecular methods, and propose opportunities for future directions. Based on 13 studies derived from a MEDLINE, EMBASE, and Web of Science search on November 7, 2021, use of molecular methods has uncovered various phyla and genera in the head and neck microbiome, particularly the oral microbiome, not previously known using culture-based methods. However, limited research has investigated the impact of RT/CRT on subsites other than the oral cavity and none of the studies aimed to examine the relationship between the head and neck microbiome and treatment effectiveness. Findings from this review provide helpful insights on our current understanding of treatment-induced oral mucositis, dental plaque, and caries formation and highlight the need for future research to examine the effect of RT/CRT on the sinonasal and oropharyngeal microbiome. In addition, future research should use larger cohorts, examine the impact of the microbiome on treatment response, and study the effect of manipulating the microbiome to overcome therapy resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".