Role of EBUS‐TBNA/EUS‐FNA and mass spectrometry for diagnosis and typing of lymph node amyloidosis: 10‐year experience in two tertiary care academic centers
Bibliographic record
Abstract
BACKGROUND: The objectives of this study were to investigate the utility of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA)/endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) for the diagnosis of amyloidosis coupled with the feasibility of mass spectrometry (MS) for amyloid subtyping. METHODS: All patients who had amyloid diagnosed by EBUS-TBNA/EUS-FNA at two tertiary care centers from 2011 to 2020 were retrieved along with the MS subtype, clinical findings, and outcomes. RESULTS: Eight patients were included: seven underwent EBUS-TBNA of mediastinal lymph nodes, and one underwent EUS-FNA of a periportal lymph node. Ages ranged from 37 to 79 years (median, 69 years), with equal numbers of men and women. Presenting clinical history included one case each of follicular lymphoma, lymphoplasmacytic lymphoma, rheumatoid arthritis, possible sarcoid, cirrhosis, and chronic renal insufficiency, and one case each of suspected pulmonary and cardiac amyloidosis. All cases showed waxy, amorphous material on direct smears (n = 5) or ThinPrep slides (n = 3), which were confirmed as amyloid on Congo Red staining. Immunohistochemistry showed dominant lambda staining in two of three cases. MS was performed in all cases and identified five of the light-chain (AL) type, one of the heavy-chain/AL type, and two suggestive of AL amyloidosis. Bone marrow biopsy performed in seven patients demonstrated that three had monoclonal plasma cells and one had lymphoplasmacytic lymphoma. Two of four patients with systemic amyloidosis received chemotherapy and remained alive, whereas three with localized disease remained stable under observation. CONCLUSIONS: EBUS-TBNA/EUS-FNA is effective for amyloidosis diagnosis and provides adequate material for ancillary tests, including MS, which can identify the precursor amyloidogenic protein, leading to appropriate patient management.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".