Manganese-porphyrin-enhanced MRI for the detection of cancer cells: A quantitative in vitro investigation with multiple clinical subtypes of breast cancer
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) contrast agents (CAs) are chemical compounds that can enhance image contrast on T1- or T2- weighted MR image. We have previously demonstrated the potential of MnCl2, a manganese-based CA, in cellular imaging of breast cancer using T1-weighted MRI. In this work, we examined the potential of another class of manganese-based CAs, manganese porphyrins (MnPs), for sensitive cellular detection of multiple clinical subtypes of breast cancer using quantitative MRI. Using a clinical 3.0-T MRI scanner, the relaxivities of two MnPs, MnTPPS4 and MnTPPS3NH2, and conventional Gd-DTPA (control) were measured in ultrapure water and their T1 contrast enhancement patterns were characterized in multiple clinical subtypes of breast cancer. The toxicity of the three CAs was evaluated in vitro. Compared to Gd-DTPA, both MnTPPS3NH2 and MnTPPS4 enabled a more sensitive multi-subtype detection of four breast cell lines at doses that posed no cytotoxic effects, with MnTPPS3NH2 producing the greatest positive enhancement. The superior T1 enhancement capabilities of MnPs over Gd-DTPA are statistically significant and are likely due to their greater cellular uptake and relaxivities. The results demonstrate that multiple clinical subtypes of breast cancer can be imaged on a 3.0-T MRI scanner using MnPs as T1 cellular CAs.
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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 | 0.000 | 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".