Do Pre‐Treatment Biopsy Characteristics Predict Early Tumour Progression in Feline Diffuse Large B Cell Nasal Lymphoma Treated With Radiotherapy?
Bibliographic record
Abstract
The standard of care treatment for localised feline nasal lymphoma (FeNL) is radiation therapy (RT). Early local or systemic failure occurs in 17%-45% of cats treated with RT with or without chemotherapy. The aim of this study was to determine if pre-treatment biopsy characteristics could predict early tumour progression in FeNL. Inclusion criteria consisted of histologically confirmed FeNL, available paraffin blocks of diagnostic quality, localised to the sinonasal cavity on staging pre-RT, treated with IMRT/IGRT (10 × 4.2 Gy) without chemotherapy and at least 1 year follow-up. All pre-RT biopsies were reviewed and evaluated with CD3, CD20, CD79a, pan-CK and Ki-67 immunohistochemistry and the mitotic activity index was determined. The primary endpoint was progression-free survival (PFS) at 1 year and hazard-ratios (HR) with confidence interval (CI) were calculated. Twenty-eight cats fit the inclusion criteria, and all had diffuse large B-cell lymphoma. Seventeen cats (61%) were progression free at 1 year. Of the 11 cats that progressed in the first year, two had local progression, two had both local and systemic progression and seven had systemic progression. The mitotic index (HR: 1.03, CI 0.9-1.19, p = 0.645), Ki-67 (HR: 1.00, CI 0.98-1.02, p = 0.845) and > 30% of tumour-infiltrating T cells (HR: 0.38, CI 0.09-1.56, p = 0.175) were not significantly associated with PFS. In this uniformly RT treated population of FeNL, none of the evaluated pre-RT histologic parameters could predict early treatment failure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".