Primary renal non-Hodgkin’s lymphoma: A narrative review of literature
Bibliographic record
Abstract
Renal involvement by Non-Hodgkin's lymphoma (NHL) is very rare, and involvement of the kidney as the primary site of NHL (PRNHL) is much more uncommon. Gold standard for the diagnosis of PRNHL is histology and imaging modalities although helpful are not specific. Nephrectomy has been mostly recommended for low grade lymphomas, and for high grade PRNHLs, chemotherapy without nephrectomy has been recommended as the treatment of choice. This tumor is aggressive with poor prognosis. This poor prognosis is partly because of delayed diagnosis and partly because of unnecessary surgeries, so it should be kept in mind, especially in bilateral renal tumors with unusual imaging characteristics, to take a tissue biopsy before nephrectomy. In this review, we will discuss all the detailed aspects of clinical, pathologic, and imaging characteristics of 83 cases of PRNHL reported in the last 20 years in the English literature so far. For this purpose, all the published cases of the primary non-Hodgkin's lymphoma of kidney were reviewed via a search in PubMed, Scopus, and Google Scholar, (1999-2019), using the keywords of "Primary renal lymphoma" and "Non-Hodgkin's lymphoma and kidney," "renal Non-Hodgkin's lymphoma," "renal lymphoma," and "lymphoma and kidney." There were 83 cases in the published English literature which were reviewed for this article. There was some missing information in some cases which has been recorded as "not reported."
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".