Radiologic Evaluation of Paranasal Sinus Anatomical Variations: A Systematic Review of CT and CBCT Studies and Their Surgical Implications
Bibliographic record
Abstract
Anatomical variations of the paranasal sinuses may influence surgical safety and outcomes in endoscopic sinus and skull-base procedures. This review compiles radiologic evidence to quantify variant prevalence and delineate surgical significance. Objectives: To evaluate computed tomography (CT) and cone-beam computed tomography (CBCT) studies for the prevalence, morphology, and clinical relevance of paranasal sinus anatomical variations, emphasizing their implications for endoscopic sinus and skull-base surgery. Methods: A systematic search of PubMed, Scopus, and Cochrane databases (January 2010–March 2025) was conducted following PRISMA 2020 guidelines. A total of 612 articles were screened, and 17 studies fulfilled the inclusion criteria. Eligible studies included original human CT or CBCT analyses reporting prevalence or morphology of variants (Onodi, Haller, Keros, accessory maxillary ostium [AMO], and roof asymmetry) with relevant surgical commentary. Weighted means were derived from pooled prevalence data across comparable imaging modalities using frequency-based aggregation. Study quality was evaluated using QUADAS-2 and modified Newcastle–Ottawa scales. Results: Seventeen studies were included. Weighted mean prevalence values were Onodi 34%, Haller 45%, and AMO 42%, with deep Keros type III fossae present in 5–9%. Ranges reflect inter-study heterogeneity in imaging protocol and cohort size. Radiology-guided findings highlighted optic-nerve proximity in Onodi, cribriform vulnerability in Keros III, orbital risk with Haller cells, mucus recirculation with AMO, and corridor distortion from concha bullosa or ethmoid-roof asymmetry. Conclusions: Anatomical variants of surgical relevance are frequent and population-dependent. Structured radiologic reporting using CT or CBCT improves pre-operative planning, mitigates optic-nerve and skull-base risks, and enhances procedural safety.
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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 | 0.022 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".