The Prognostic Impact of CD163-Positive Macrophages in Follicular Lymphoma: A Study from the BC Cancer Agency and the Lymphoma Study Association
Bibliographic record
Abstract
PURPOSE: We aimed to assess the prognostic significance of follicular lymphoma-associated macrophages in the era of rituximab treatment and maintenance. EXPERIMENTAL DESIGN: We applied immunohistochemistry for CD68 and CD163 to two large tissue microarrays (TMA). The first TMA included samples from 186 patients from the BC Cancer Agency (BCCA) who had been treated with first-line systemic treatment including rituximab, cyclophosphamide, vincristine, and prednisone. The second contained 395 samples from PRIMA trial patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone, and randomized to rituximab maintenance or observation. Macrophage infiltration was assessed using Aperio image analysis. Each of the two cohorts was randomly split into training/validation sets. RESULTS: An increased CD163-positive pixel count was predictive of adverse outcome in the BCCA dataset [5-year progression-free survival (PFS) 38% vs. 72%, respectively, P = 0.004 in the training cohort and 5-year PFS 29% vs. 61%, respectively, P = 0.004 in the validation cohort]. In the PRIMA trial, an increased CD163 pixel count was associated with favorable outcome (5-year PFS 60% vs. 44%, respectively, P = 0.011 in the training cohort and 5-year PFS 55% vs. 37%, respectively, P = 0.030 in the validation cohort). CONCLUSIONS: CD163-positive macrophages predict outcome in follicular lymphoma, but their prognostic impact is highly dependent on treatment received.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".