Sinonasal Malignancy Following Cranial Irradiation: A Scoping Review and Case Report of Sinonasal Teratocarcinosarcoma
Bibliographic record
Abstract
Abstract Background Radiation therapy is a mainstay of treatment for brain tumors, but delayed complications include secondary malignancy which may occur months to years after treatment completion. Methods We reviewed the medical records of a 41-year-old female treated with 60 Gy of radiation for a recurrent astrocytoma, who 6 years later developed a locally advanced sinonasal teratocarcinosarcoma. We searched MEDLINE, Embase, and Web of Science to conduct a scoping review of biopsy-proven sinonasal malignancy in patients who previously received cranial irradiation for a brain tumor. Results To our knowledge, this is the first report of a patient to present with a sinonasal teratocarcinosarcoma after receiving irradiation for a brain tumor. Our scoping review of 1,907 studies produced 14 similar cases of secondary sinonasal malignancy. Median age of primary cancer diagnosis was 39.5 years old (standard deviation [SD]: 21.9), and median radiation dose was 54 Gy (SD: 20.3). Median latency time between the primary cancer and secondary sinonasal cancer was 9.5 years (SD: 5.8). Olfactory neuroblastoma was the most common sinonasal cancer (n = 4). Fifty percent of patients died from their sinonasal cancer within 1.5 years. Conclusion Patients who receive radiation exposure to the sinonasal region for treatment of a primary brain tumor, including low doses or scatter radiation, may be at risk of a secondary sinonasal malignancy later in life. Physicians who monitor at-risk patients must be vigilant of symptoms which may suggest sinonasal malignancy, and surveillance should include radiographic review with careful monitoring for a secondary malignancy throughout the entire irradiated field.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".