Magnetic Particle Imaging Lymphography (MPIL): A novel technique for lymph node mapping
Bibliographic record
Abstract
Abstract During metastasis, tumour cells drain to nearby lymph nodes, the first of which are named the sentinel lymph node(s) (SLN). SLN biopsy (SLNB) determines if metastasis has occurred. Traditionally, SLNB involves injecting a Technetium-labeled tracer peritumourally, pre-operative imaging with SPECT to locate the SLN, and surgery guided by a gamma probe to remove them. Limitations include short tracer half-life which can make scheduling the SLNB difficult, and radiation dose to patients and healthcare workers. Alternatively, magnetic localization, with superparamagnetic iron oxide nanoparticles (SPIONs) as the tracer and a magnetometer to detect SPIONs in the SLN during surgery can be used, however, this lacks pre-operative imaging. Magnetic Particle Imaging (MPI) is a new imaging modality that directly detects SPIONs, holding potential for pre-operative imaging in magnetic SLNB. SPIONs for SLNB should have rapid drainage, high SLN accumulation, and high specificity to the SLN. For MPI Lymphography (MPIL), high particle sensitivity is also important. This study assesses the in vivo pharmacokinetics for SLN mapping with MPIL in a murine model, using five commercially available SPIONs of varying iron core sizes and surface coatings. We show that some SPIONs provide higher MPI signal at the SLN and show the potential to detect higher echelon nodes (HENs). PEGylation of SPIONs and mannose targeted SPIONs increase clearance from the injection site and the mannose targeted SPION reduces flow to HENs.
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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 | 0.001 | 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) | 0.001 | 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".