Manganese-Enhanced Magnetic Resonance Imaging for Early Detection and Characterization of Breast Cancers
Bibliographic record
Abstract
Very early cancer detection is the key to improving cure. Our objective was to investigate manganese (Mn)-enhanced magnetic resonance imaging (MRI) for very early detection and characterization of breast cancers. Eighteen NOD scid gamma mice were inoculated with MCF7, MDA, and LM2 breast cancer cells and imaged periodically on a 3 T scanner beginning on day 6. T1-weighted imaging and T1 measurements were performed before and 24 hours after administering MnCl2. At the last imaging session, Gd-DTPA was administered and tumors were excised for histology (hematoxylin-eosin and CD34 staining). All mice, except for two inoculated with MCF7 cells, developed tumors. Tumors enhanced uniformly on Mn and showed clear borders. Early small tumors (≤ 5 mm3) demonstrated the greatest enhancement with a relative R1 (1/T1) change of 1.57 ± 0.13. R1 increases correlated with tumor size (r = -.34, p = .04). Differences in R1 increases among the three tumor subtypes were most evident in early tumors. Histology confirmed uniform cancer cell distribution within tumor masses and vasculature in the periphery, which was consistent with rim-like enhancement on Gd-DTPA. Mn-enhanced MRI is a promising approach for detecting very small breast cancers in vivo and may be valuable for very early cancer detection.
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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) | 0.000 | 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 | 0.001 |
| 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".