Recent advances in PET/MR imaging for head and neck tumors: a systematic review of the last three years
Bibliographic record
Abstract
PURPOSE: This systematic review addresses the clinical relevance of PET/MR in patients with head and neck (HN) tumors, highlighting studies conducted over the last three years to provide an updated perspective on integrating a hybrid PET/MR scan into different clinical scenarios. METHODS: We employed a search algorithm, combining terms ("PET/MR" OR ("PET" AND ("MR" OR "MRI")) OR "PET/MRI" OR "PET-MR" OR "PET-MRI" OR ("PET" AND "magnetic")) AND ("head" AND "neck"). Studies written in English and published throughout 2021, 2022 and 2023 up to November 15th were considered if were focused on: suspected HN tumors; confirmed HN tumors before surgery/radiotherapy/chemotherapy; HN tumor recurrence or therapy response assessment. Reviews, editorials or letters, case report/series or any other original unrelated studies to the topics were excluded. RESULTS: Twenty out of 169 studies were deemed eligible. The HN tumor cohorts included sinonasal tumors, nasopharyngeal carcinoma and tumors in the oropharynx, oral cavity, hypopharynx and larynx, mostly of squamous cell carcinoma histology. Sixteen out of 20 articles focused on initial staging, including prognostic information before primary treatment, whereas 4/20 articles explored the clinical significance of PET/MR in restaging settings or other clinical purposes. CONCLUSION: This review consolidates previous findings by showing the relationship between morphology, metabolism, cellularity, and perfusion in HN tumors. Metrics provided by PET/MR are able to predict the histologic grade of HN tumors, EGFR status and patient outcome. PET/MR demonstrates high diagnostic performance for detecting locoregional tumor recurrence, distant metastases and second primary cancers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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".