Non-Hodgkins lymphoma of the nasal cavity: A case report
Bibliographic record
Abstract
We present a rare case of an 81-year-old woman presenting with acute left nasal blockage caused by a large nasal mass of unknown origin. The mass was subsequently diagnosed as diffuse large B-cell non-Hodgkin lymphoma (NHL). Nasal/paranasal space involvement in NHL is uncommon, representing only 0.2%-2% of cases. In this case, the nasal NHL mass exhibited a favorable prognosis, in contrast to previously reported sinonasal lymphomas with poor outcomes. The patient underwent excisional biopsy and was treated with 3 cycles of R-CHOP chemotherapy, resulting in complete resolution of the mass confirmed by a follow-up CT scan and no signs of disease after 1 year. Differentiating sinonasal lymphomas from other neoplasms can be challenging due to their variable morphology and location. Diffuse presentations of sinonasal lymphoma can aid in distinguishing them from discrete lesions associated with other sinonasal neoplasms. However, differentiation from acute invasive sinonasal infection remains difficult. MRI can help identify lymphomas through the characteristic hypointense T2 signal and diffusion restriction, with the combined use of CT to aid in differentiating masses of unknown morphology. Nonetheless, squamous cell carcinoma, which mimics lymphoma features on MRI, poses additional challenges to accurate identification. This case highlights the rarity of nasal NHLs, their potential for excellent prognosis, and the importance of diverse imaging techniques in their diagnosis and differentiation from other sinonasal pathologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".