Quantifying tumor associated macrophages in breast cancer: a comparison of iron and fluorine-based MRI cell tracking
Bibliographic record
Abstract
Abstract Tumor associated macrophages (TAMs) are associated with tumor growth and metastasis. MRI can detect TAMs labeled with iron oxide (USPIO) or perfluorocarbon (PFC) agents. This study compared these two cell tracking approaches for imaging TAMs in vivo. 4T1 tumors were imaged with MRI at 4 days or 3 weeks post cell implantation after intravenous (i.v.) administration of either USPIO or PFC. Signal loss was detected within tumors at both time points post USPIO. Images acquired at 4 days demonstrated signal loss encompassing the entire tumor and around the periphery at 3 weeks. Number of black voxels suggested higher numbers of TAMs in the tumor at the later time point. After PFC administration, Fluorine-19 (19F) signal was detected in a similar spatial distribution as signal loss post USPIO. 19F signal quantification revealed that the number of 19F spins was not significantly different at the two time points, suggesting a similar number of TAMs were present in tumors but accumulated in different regions. 19F signal was higher centrally in tumors at 4 days and heterogenous around the periphery at 3 weeks. This study revealed that 19F-based cell tracking methods better represent TAM density and provides additional information not achievable with iron-based methods.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".