Tracking the fates of iron-labeled tumor cells in vivo using Magnetic Particle Imaging
Bibliographic record
Abstract
ABSTRACT The use of imaging to detect and monitor the movement and accumulation of cells in living subjects can provide significant insights that can improve our understanding of metastasis and guide therapeutic development. For cell tracking using Magnetic Resonance Imaging (MRI), cells are labeled with iron oxides and the effects of the iron on water provides contrast. However, due to low specificity and difficulties in quantification with MRI, other modalities and approaches need to be developed. Magnetic Particle Imaging (MPI) is an emerging imaging technique which directly detects magnetic iron, allowing for a specific, quantitative and sensitive readout. Here, we use MPI to image iron-labeled tumor cells longitudinally, from implantation and growth at a primary site to movement to distant anatomic sites. In vivo bioluminescent imaging (BLI) was used to localize tumor metastases and computed tomography (CT) allowed for correlation of these signals to anatomic locations. These three imaging modalities provide information on immune escape and metastasis of iron-labeled, and unlabeled, tumor cells, and the accumulation of cell-free iron contrast over time. We identified iron signals by MPI and tumor cells via BLI, and correlated these positive contrast images with CT scans to reveal the anatomic sites with cancer cells; histologic analysis confirmed the presence of iron-labeled tumor cells in the tissues, suggesting that the metastatic cells retained enough iron for MPI detection. The use of multi-modality cell tracking reveals the movement, accumulation and fates of labeled cells that will be helpful understanding cancer progression and guiding the development of targeted therapies.
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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) | 0.000 | 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) | 0.000 | 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".