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 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".