Cellular Magnetic Resonance Imaging: In Vivo Imaging of Melanoma Cells in Lymph Nodes of Mice
Bibliographic record
Abstract
Metastasis is responsible for most deaths due to malignant melanoma. The clinical significance of micrometastases in the lymph is a hotly debated topic, but an improved understanding of the lymphatic spread of cancer remains important for improving cancer survival. Cellular magnetic resonance imaging (MRI) is a newly emerging field of imaging research that is expected to have a large impact on cancer research. In this study, we demonstrate the cellular MRI technology required to reliably image the lymphatic system in mice and to detect iron-labeled metastatic melanoma cells within the mouse lymph nodes. Melanoma cells were implanted directly into the inguinal lymph nodes in mice, and micro-MRI was performed using a customized 1.5-T clinical MRI system. We show cell detection of as few as 100 iron-labeled cells within the lymph node, with injections of larger cell numbers producing increasingly obvious regions of signal void. In addition, we show that cellular MRI allows monitoring of the fate of these cells over time as they develop into intranodal tumors. This technology will allow noninvasive investigations of cellular events in cancer metastasis within an entire animal and will facilitate progress in understanding the mechanisms of metastasis within the lymphatic system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".