The value of diagnostic vitrectomy: Histocytopathology techniques for the diagnosis of lymphoma of the retina
Bibliographic record
Abstract
Abstract Purpose: Primary large B-cell lymphoma of the retina, vitreous, and central nervous system (CNS), is an intraocular tumor with an unspecific and insidious clinical presentation. Pars plana vitrectomy (PPV) and optical coherence tomography (OCT) are useful diagnostic tools for this malignant process. The aim of this study is to evaluate the diagnostic efficacy of PPV for these intraocular lesions under a modified diagnostic protocol with a clinical pathological correlation with OCT imaging. Materials and Methods: A total of 115 samples were collected after a vitrectomy procedure (aspiration or vitrectomy cassette). The samples were centrifuged, and the precipitates were collected. A cell block was prepared and analyzed with multiple stains and an immunohistochemistry (IHC) panel, including B- and T-cell markers, as well as light chain markers, to establish the monoclonal nature of the tumor. Of the 115 samples, 9 (7.83%) were diagnosed with large B-cell lymphoma of the retina, vitreous, and CNS. Conclusion: Diagnostic vitrectomies for the large B-cell lymphoma of the retina, vitreous, and CNS is an excellent tool for the diagnosis of this entity. A negative diagnostic PPV with a strong suspicious OCT image, where the neoplastic cells are located between the retinal pigmented epithelium and Bruch’s membrane, the latter procedure should be either repeated or a chorioretinal biopsy be performed. In contrast, a positive vitrectomy using the IHC panel for large B-cell lymphoma of the retina, vitreous, and CNS is pathognomonic of this condition. In addition, the OCT is an important tool to help in the diagnosis of this difficult entity. Results: The diagnostic PPV provided suitable vitreous samples to all patients with undetermined uveitis and/or intraocular tumor suspicion. A morphological and immunohistichemical (IHC) analysis enabled a conclusive diagnosis of retina, vitreous and CNS lymphoma in all patients submitted to the procedure.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".