Three-Dimensional Imaging and Quantification of Both Solitary Cells and Metastases in Whole Mouse Liver by Magnetic Resonance Imaging
Bibliographic record
Abstract
The metastatic cell population, ranging from solitary cells to actively growing metastases, is heterogeneous and unlikely to respond uniformly to treatment. However, quantification of the entire experimental metastatic cell population in whole organs is complicated by requirements of an imaging modality with the large field of view and high spatial resolution necessary to detect both single cells and metastases in the same organ. Thus, it is difficult to assess differential responses of these distinct metastatic populations to therapy. Here, we develop a magnetic resonance imaging (MRI) technique capable of quantifying the full population of metastatic cells in a secondary organ. B16F1 mouse melanoma cells were labeled with micron-sized iron oxide particles (MPIO) and injected into mouse liver via the mesenteric vein. Livers were removed immediately or at day 9 or 11, following doxorubicin or vehicle control treatment, and imaged using a 3T clinical magnetic resonance scanner and custom-built gradient coil. Both metastases (>200 microm) and MPIO-labeled single cells were detected and quantified from MR images as areas of hyperintensity or hypointensity (signal voids), respectively. We found that 1mg/kg doxorubicin treatment inhibited metastasis growth (n = 11 per group; P = 0.02, t test) but did not decrease the solitary metastatic cell population in the same livers (P > 0.05). Thus, the technique presented here is capable of quickly quantifying the majority of the metastatic cell population, including both growing metastases and solitary cells, in whole liver by MRI and can identify differential responses of growing metastases and solitary cells to therapy.
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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 | 0.001 | 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) | 0.001 | 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".