Magnetic particle imaging enables nonradioactive quantitative sentinel lymph node identification: feasibility proof in murine models
Bibliographic record
Abstract
Abstract Background Sentinel lymph node biopsy (SLNB) is an important cancer diagnostic staging procedure. Conventional SLNB procedures with 99mTc radiotracers and scintigraphy are constrained by tracer half-life and, in some cases, insufficient image resolution. Here, we explore an alternative magnetic (nonradioactive) image-guided SLNB procedure. Purpose To demonstrate that magnetic particle imaging (MPI) lymphography can sensitively, specifically, and quantitatively identify and map sentinel lymph modes (SLNs) in murine models in multiple regional lymphatic basins. Materials and Methods Iron oxide nanoparticles were administered intradermally to healthy C57BL/6 mice (male, 12-week-old, n = 5). The nanoparticles (0.675 mg Fe/kg) were injected into the tongue, forepaw, base of tail, or hind footpad, then detected by 3-dimensional MPI at multiple timepoints between 1 hour and 4 to 6 days. In this mouse model, the SLN is represented by the first lymph node draining from the injection site. SLNs were extracted to verify the MPI signal ex vivo and processed using Perl’s Prussian iron staining. Paired t-test was conducted to compare MPI signal from SLNs in vivo vs. ex vivo and considered significant if P < .05. Results MPI lymphography identified SLNs in multiple lymphatic pathways, including the cervical SLN draining the tongue, axillary SLN draining the forepaw, inguinal SLN draining the tail, and popliteal SLN draining the footpad. MPI signal in lymph nodes was present after 1 hour and stable for the duration of the study (4-6 days). Perl’s Prussian iron staining was identified in the subcapsular space of excised SLNs. Conclusion Our data support the use of MPI lymphography to specifically detect SLN(s) using a magnetic tracer for a minimum of 4 to 6 days, thereby providing information required to plan the SLN approach in cancer surgery. As clinical-scale MPI is developed, translation will benefit from a history of using iron-oxide nanoparticles in human imaging and recent regulatory-approvals for use in SLNB.
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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) | 0.000 | 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 | 0.000 | 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".