The Role of Positron Emission Tomography for the Management of Sinonasal Malignancies: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Positron emission tomography (PET) scan is a valuable imaging modality widely used in the management of cancers. Its usage is well defined for most head and neck malignancies. However, there is a lack of consensus regarding the utility of PET scan for sinonasal malignancies. This is highlighted by the latest international consensus statement on endoscopic skull base surgery. OBJECTIVE: This systematic review aims to clarify the role of PET scan in the management of sinonasal malignancies. METHODS: We conducted a comprehensive literature search using PubMed, MEDLINE, EMBASE, Web of Science, CINAHL, and Cochrane databases for research studies of interest. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) updated statement was used to guide the review. RESULTS: In total, 1807 articles were assessed for eligibility. Thirty-nine original papers, published between 2004 and 2021, met inclusion criteria. Seven articles focused on the role of PET scan for inverted papilloma, 23 for sinonasal carcinoma, 4 for melanoma, and 3 for lymphoma, and finally, 3 articles focused on the use of specific PET scan tracers for sinonasal malignancies. Qualitative summaries for each potential role of PET scans were provided. In general, included studies were retrospective in nature with low level of evidence. CONCLUSIONS: In general, and across all types of sinonasal malignancies, PET scan yielded positive results regarding detection and initial staging. It was also considered as the modality of choice for detection of distant metastases, except in the case of sinonasal lymphoma. PET scan's main limit resides in its inability to detect lesions in or close to the metabolic activity of the brain.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".