MRI findings of radiation-induced changes of masticatory muscles: A systematic review
Bibliographic record
Abstract
BACKGROUND: Radiotherapy to the head and neck regions can result in serious consequences to the temporomandibular joint (TMJ) and chewing muscles. Magnetic resonance imaging (MRI) demonstrates soft-tissue alterations after radiotherapy, such as morphology and signal intensity. OBJECTIVE: The purpose of this review is to critically and systematically analyse the available evidence regarding the masticatory muscles alterations, as demonstrated on MRI, after radiotherapy for head and neck cancer. DATA SOURCES: Electronic search of MEDLINE, EMBASE, EBM reviews and Scopus. INCLUSION CRITERIA: Reports of any study design investigating radiation-induced changes in masticatory muscles after radiotherapy in patients with head and neck cancer were included. RESULTS AND SYNTHESIS METHODS: An electronic database search resulted in 162 papers. Sixteen papers were initially selected as potentially relevant studies; however, only four papers satisfied all inclusion criteria. The included papers focused on the MRI appearance of masticatory muscles following radiotherapy protocol. Two papers reported outcome based on retrospective clinical and imaging records, whereas the remaining two papers were case reports. Irradiated muscles frequently show diffuse increase in T2 signal and post-gadolinium enhancement post-irradiation. Also, muscle size changes were reported based on subjective comparison with the contralateral side. The quality of all included papers was considered poor with high risk of bias. CONCLUSION: There is no evidence that MRI interpretations indicate specific radiation-induced changes in masticatory muscles. There is a clear need for a cohort study comparing patients with pre- and post-radiotherapy MRI.
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 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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".