Prognostic Significance of Macrophage Infiltration in Leiomyosarcomas
Bibliographic record
Abstract
PURPOSE: Macrophages are migratory cells that are frequently recruited to the site of tumors. Their presence is associated with poor clinical outcome in a variety of epithelial malignancies. The aim of this study is to examine the prognostic significance of tumor-associated macrophages in sarcomas. EXPERIMENTAL DESIGN: Global gene expression profiling data of a series of soft tissue tumors were analyzed for macrophage-associated gene expression. Immunohistochemistry on tissue microarrays containing leiomyosarcoma cases with known clinical outcome was used to verify the presence of macrophages and to examine the relationship between tumor-associated macrophages and clinical outcome. RESULTS: Gene expression profiling revealed high-level expression of several macrophage-associated genes such as CD163 and CD68 in a subset of leiomyosarcomas, indicating the presence of variable numbers of tumor-infiltrating macrophages. This was confirmed by CD68 and CD163 immunostaining of a tissue microarray containing 149 primary leiomyosarcomas. Kaplan-Meier survival analysis showed that high density of tumor-infiltrating macrophages as identified by CD163 or CD68 staining is associated with a significantly worse disease-specific survival in nongynecologic leiomyosarcomas, whereas leiomyosarcomas arising from the gynecologic tract showed no significant association between macrophage infiltration and survival. The presence of tumor necrosis did not correlate significantly with outcome. CONCLUSIONS: An increased density of CD163- or CD68-positive tumor-infiltrating macrophages is associated with poor outcome in nongynecologic leiomyosarcomas. This may help the clinical management of patients with leiomyosarcomas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".