Using 16α-[18F]-Fluoro-17β-Estradiol PET to Visualize Estrogen Receptor α Expression in Human Breast Cancer Xenografts in Female Ovariectomized Mice
Bibliographic record
Abstract
To demonstrate how estrogen receptor alpha (ERα) positive breast cancer xenografts may be visualized in BALB/c nude mice using 16α-[18F]-fluoro-17β-estradiol (18F-FES) positron emission tomography (PET), ovariectomized BALB/c nude mice were injected with ERα-positive breast cancer cells (MCF-7, 3 × 106 cells; shoulder [n = 10] or 4th inguinal mammary fat pad [n = 10]) or ERα-negative breast cancer cells (MDA-MB-231, 1 × 106 cells; mammary fat pad [n = 5]). Mice harboring MCF-7 cells received subcutaneous injections of 20 µg of 17β-estradiol (20 µg/20 µL; corn oil:ethanol, 9:1) in the nape of their necks 2 days prior to cell injection, followed by daily injections five times per week for 5 weeks. Tumor volumes were measured according to the formula: (L*W2)/2 (L; length, W; width). Once tumor volumes reached approximately 100 mm3, 17β-estradiol injections were halted 2 days prior to mice receiving 18F-FES for PET imaging to avoid competitive binding with ERα. Upon 18F-FES administration via the lateral tail vein, PET/MRI was performed for 15 min at 1 h to 1.5 h post-injection. 18F-FES uptake was not observed in ERα-negative, MDA-MB-231 tumor-bearing mice. 18F-FES uptake was most pronounced in mice harboring MCF-7 tumors in the shoulder. In MCF-7 tumors grown in the inguinal mammary fat pad, 18F-FES uptake was less visible, as the intestinal excretion pattern of 18F-FES obscured the radioactivity detectable in these tumors. To use 18F-FES PET as a tool to visualize ERα expression in ERα-positive breast xenografts, we demonstrate that the visibility of 18F-FES uptake is clear in tumors located away from the abdominal region of mice, such as in the shoulder.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".