MRI Detection of Nonproliferative Tumor Cells in Lymph Node Metastases Using Iron Oxide Particles in a Mouse Model of Breast Cancer
Bibliographic record
Abstract
Cell tracking with magnetic resonance imaging (MRI) and iron nanoparticles is commonly used to monitor the fate of implanted cells in preclinical disease models. Few studies have employed these methods to study cancer cells because proliferative iron-labeled cancer cells will lose the label as they divide. In this study, we evaluate the potential for retention of the iron nanoparticle label, and resulting MRI signal, to serve as a marker for slowly dividing cancer cells. Green fluorescent protein-transfected MDA-MB-231 breast cancer cells were labeled with red fluorescent micron-sized superparamagnetic iron oxide (MPIO) nanoparticles. Cells were examined in vitro at multiple time points after labeling by staining for iron-labeled cells and by flow cytometric detection of the fluorescent MPIO. Severe combined immune deficiency (SCID) mice were implanted with 5 x 10(5) MPIO-labeled or unlabeled cells in the mammary fat pad and MRI was performed weekly until 28 days after injection. Microscopy was performed to validate MRI. In vitro assays revealed a very small percentage of cells that retained MPIO at 14 days after labeling. Regions of signal loss were observed in MRI of primary tumors that developed from iron-labeled cancer cells. Small focal regions of signal loss were detected in images of the axillary and brachial nodes in six of eight mice, at day 14 or later, with microscopy confirming the presence of iron-labeled cancer cells. Our data suggest an interesting role for cell tracking with iron particles since label retention leads to persistent signal void, allowing proliferative status to be determined.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".