Manganese‐enhanced MRI of minimally gadolinium‐enhancing breast tumors
Bibliographic record
Abstract
PURPOSE: To investigate the potential of manganese (Mn)-enhanced MRI for sensitive detection and delineation of tumors that demonstrate little enhancement on Gd-DTPA. MATERIALS AND METHODS: Eighteen nude rats bearing 1 to 2 cm in diameter orthotopic breast tumors (ZR75 and LM2) were imaged on a 3 Tesla (T) clinical scanner. Gd-DTPA was administered intravenously and MnCl2 subcutaneously, both at 0.05 mmol/kg. T1 -weighted imaging and T1 measurements were performed precontrast, 10 min post-Gd-DTPA, and 24 h post-MnCl2 . Tumors were excised and histologically assessed using H&E (composition and necrosis) and CD34 (vascularity). RESULTS: Most tumors (78%) demonstrated little enhancement (< 20% change in R1 ) on Gd-DTPA. MnCl2 administration achieved greater and more uniform enhancement throughout the tumor mass (i.e., not restricted to the tumor periphery), with R1 changing over 20% in 72% of tumors. MnCl2 -induced R1 changes compared with Gd-induced changes were significantly greater in both ZR75 (P < 0.01) and LM2 tumors (P < 0.05). Histology confirmed very low vascularity in both tumor models, and necrotic areas were well delineated only on Mn-enhanced MRI. CONCLUSION: Mn-enhanced MRI is a promising approach for detection of low-Gd-enhancing tumors.
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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) | 0.001 | 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".