Imaging and resectability issues of sinonasal tumors
Bibliographic record
Abstract
Sinonasal tumors can invade into the critical structures of the anterior and central skull base. Although the determination of precise tumor histology is difficult with imaging, radiology is important in helping differentiate malignant from benign disease. Imaging helps to map the anatomical extent of intracranial and intraorbital tumor, which has important implications for staging, treatment and prognosis. Imaging also helps to facilitate and plan for craniofacial or endoscopic surgical approaches and radiation planning. This paper will review the locoregional invasion patterns for sinonasal tumors, with emphasis on their imaging features. The authors will discuss the implications for staging, resection potential, choice and details of radiotherapy with or without chemotherapy and prognosis. The imaging assessment of structures and compartments that are critical to the skull base team are highlighted: orbit, cavernous sinus, anterior cranial fossa dura/intracranial tumor, lateral frontal sinus, vascular tumor encasement, perineural tumor spread and tumor effect on the surrounding bony structures.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".