Imaging macrophage distribution and density in mammary tumors and lung metastases using fluorine‐19 MRI cell tracking
Bibliographic record
Abstract
Purpose The presence of tumor‐associated macrophages (TAMs) correlates with breast cancer progression and metastatic spread. Metastasis‐associated macrophages (MAMs) are also recruited to distant sites, where they support metastatic growth. In this study, we demonstrate that in vivo fluorine‐19 ( 19 F)‐based MRI cell tracking can evaluate the density and distribution of macrophages within murine breast cancer tumors and associated metastases. Methods Three murine breast cancer cell lines with different metastatic potentials (4T1, 168FARN, and 67NR) were implanted into the mammary fat pad in mice. In vivo whole body 19 F MRI was performed on tumor‐bearing mice 24 hours post‐intravenous injection of a perfluorocarbon (PFC) agent, which was taken up by macrophages in situ. Results TAMs were detected mainly in the periphery of primary tumors, and higher numbers of TAMs were detected in the more aggressive 4T1 tumors. Tumors had significantly greater 19 F spins/mm 3 when they were smaller, suggesting more TAM infiltration in early‐stage tumors. 19 F signal was observed within lung metastases in mice with 4T1 tumors, and fluorescence microscopy confirmed the presence of PFC‐positive macrophages. Conclusion This study shows for the first time proof of the ability to use MRI cell tracking to visualize MAMs in the lungs. The ability to detect and monitor the number of TAMs in individual tumors with 19 F MRI would allow for identification of breast tumors with heavy infiltration of TAMs and could be used as a biomarker for decisions about how to best treat these patients as well as for monitoring responses to therapy. Magn Reson Med 80:1138–1147, 2018. © 2018 International Society for Magnetic Resonance in Medicine.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".