Strongly Magnetic Iron Nanoparticles Improve the Diagnosis of Small Tumours in the Reticuloendothelial System by Magnetic Resonance Imaging
Bibliographic record
Abstract
Despite advances in non-invasive medical imaging, accurate nodal staging of malignancy continues to rely on surgery. Superparamagnetic iron oxide nanoparticles (IONP) with lymphotropic qualities have shown some promise as contrast agents for MRI of the lymph nodes, but recent large-scale studies failed to show consistent detection of tumours below 5 mm. Herein we compare imaging of splenic and lymph node tissue using iron/iron oxide core/shell nanoparticles (Fe NP) that have superior magnetic qualities to IONP, to determine whether improved negative contrast in T(2)-weighted MRI can enhance the diagnosis of small tumours in the reticuloendothelial system. To provide an in vivo pre-clinical model of human lymph node micrometastases, breast cancer cells were injected into the spleens of mice, providing localised areas of tumour growth. MR images of groups of tumour-bearing and sham-treated animals were generated using a 1.5 T imaging system and analysed by two independent, blinded radiologists. Fe NP improved the sensitivity and specificity of MRI when compared to IONP, enabling accurate detection of tumours as small as 1-3 mm. The use of Fe NP as contrast agents have the potential to improve the diagnostic accuracy of MRI in cancer patients, leading to more rapid and effective treatment.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".