Sarcoidosis and Cancer: A Complex Relationship
Bibliographic record
Abstract
Sarcoidosis is a systemic disease of unknown etiology, characterized by the presence of non-caseating granulomas in various organs, mainly the lungs, and the lymphatic system. Since the individualization of sarcoidosis-lymphoma association by Brincker et al., the relationship between sarcoidosis or granulomatous syndromes and malignancies has been clarified through observational studies worldwide. Two recent meta-analyses showed an increased risk of neoplasia in sarcoidosis. The granulomatosis can also reveal malignancy, either solid or hematological, defining paraneoplastic sarcoidosis. Recent cancer immunotherapies, including immune checkpoint inhibitors (targeting PD-1, PD-L1, or CTLA-4) and BRAF or MEK inhibitors were also reported as possible inducers of sarcoidosis-like reactions. Sarcoidosis and neoplasia, especially lymphoma, can show overlapping presentations, thus making the diagnosis and treatment harder to deal with. There are currently no formal recommendations to guide the differential diagnosis workup between the evolution of lymphoma or a solid cancer and a granulomatous reaction associated with neoplasia. Thus, in atypical presentations (e.g., deeply impaired condition, compressive lymphadenopathy, atypical localization, unexplained worsening lymphadenopathy, or splenomegaly), and treatment-resistant disease, targeted biopsies on suspect localizations with histological examination could help the clinician to differentiate neoplasia from sarcoidosis. Pathological diagnosis could sometimes be challenging since very few tumor cells may be surrounded by massive granulomatous reaction. The sensitization of currently available diagnostic tools should improve the diagnostic accuracy, such as the use of more "cancer-specific" radioactive tracers coupled with Positron Emission Tomography scan.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".