Towards a nanoscale mammographic contrast agent: development of a modular pre-clinical dual optical/x-ray agent
Bibliographic record
Abstract
Contrast-enhanced digital mammography (CEDM) can provide improved breast cancer detection and characterization compared to conventional mammography by imaging the effects of tumour angiogenesis. Current small-molecule contrast agents used for CEDM are limited by a short plasma half-life and rapid extravasation into tissue interstitial space. To address these limitations, nanoscale agents that can remain intravascular except at sites of tumour angiogenesis can be used. For CEDM, this agent must be both biocompatible and strongly attenuate mammographic energy x-rays. Nanoscale perfluorooctylbromide (PFOB) droplets have good x-ray attenuation and have been used in patients for other applications. However, the macroscopic scale of x-ray imaging (50-100 µm) is inadequate for direct verification that PFOB droplets localize at sites of breast tumour angiogenesis. For efficient pre-clinical optimization for CEDM, we integrated an optical marker into PFOB droplets for microscopic assessment (≪50 µm). To develop PFOB droplets as a new nanoscale mammographic contrast agent, PFOB droplets were labelled with fluorescent quantum dots (QDs). The droplets had mean diameters of 160 nm, fluoresced at 635 nm and attenuated x-ray spectra at 30.5 keV mean energy with a relative attenuation of 5.6 ± 0.3 Hounsfield units (HU) mg(-1) mL(-1) QD-PFOB. With the agent loaded into tissue phantoms, good correlation between x-ray attenuation and optical fluorescence was found (R(2) = 0.96), confirming co-localization of the QDs with PFOB for quantitative assessment using x-ray or optical methods. Furthermore, the QDs can be removed from the PFOB agent without affecting its x-ray attenuation or structural properties for expedited translation of optimized PFOB droplet formulations into patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".