Abstract 4135: First demonstration of magnetic particle imaging for sentinel lymph node identification
Bibliographic record
Abstract
Abstract Sentinel Lymph Node (LN) biopsy involves the identification and surgical removal of the first LN(s) that drain from a primary tumor to evaluate for metastasis by histopathology [1]. For several tumor types, the standard of care is to manage the regional LN basin separately from the primary tumor. For head and neck cancer, melanoma, and complex breast cancer cases, pre-surgical imaging is required to determine the number and location of LN(s) to remove. Most sentinel LN biopsies are performed with nuclear imaging, which relies on short-lived radiotracers and can have poor image quality, making it challenging to identify sentinel LNs in complex anatomies. An alternative and non-inferior workflow uses a non-radioactive iron oxide magnetic tracer (ferucarbotran) with a magnetic probe [2], however this tool can only be used intraoperatively. In this abstract we introduce magnetic particle imaging (MPI) as a pre-surgical imaging technology that detects iron oxides with high sensitivity at mm-scale resolution [3]. Our objective is to demonstrate that MPI provides sensitive and quantitative tracking of ferucarbotran pharmacokinetics from four anatomical sites to primary draining LNs in mice. Methods: Ferucarbotran was administered intradermally to C57BL/6 mice at a standard clinical dose of 0.675 mg Fe/kg to the forepaw, hindpaw, or base of tail, or tongue (n = 4). Full-body 2D and 3D imaging was performed after 20 mins, 24 h, and 48 h, and 144 h using MOMENTUM imager (Magnetic Insight Inc.). MPI signal was quantified at the injection site and draining LNs. LNs of interest were extracted to verify MPI signals ex vivo then were processed for Perl’s Prussian iron staining. Results: After 20 minutes, MPI signal was seen at the injection site and primary LNs. The pharmacokinetics of ferucarbotran to LNs varied based on administration site. For hindpaw, signal was present in the popliteal LN (1.4% of tracer). For forepaw, ferucarbotran accumulated in the primary axillary LN (9%). For base of the tail, signal was observed in inguinal LN (2%). For tongue, MPI signal was detected in cervical LNs (14%). In all mice, MPI signal at the injection site decreased over time and signal in primary LNs persisted for at least 6 days. Conclusions: We demonstrated MPI is a quantitative, hotspot imaging technique for identifying primary LNs. As iron oxide tracer pharmacokinetics varies with injection site, this imaging technique could provide fundamental information required for surgical planning. Unlike nuclear imaging, persistence of MPI signal for several days provides tremendous flexibility in clinical workflow and introduces the potential for an image-guide delayed SLN biopsy [4] . This preclinical LN imaging is timely as our team is actively building and testing the world’s first large-bore, clinical-scale MPI scanner.[1] Leong SP, Clin Exp Metastasis (2022). [2] Alvarado MD, et al. Ann Surg Oncol (2019). [3] Sarnitas EU, et al. J Magn Reson (2014). [4] Karakatsanis A, et al. Ann Surg Oncol (2023). Citation Format: Olivia C. Sehl, A. Rahman Mohtasebzadeh, Kelvin Guo, Petrina Kim, Benjamin Fellows, Marcela Weyhmiller, Paula J. Foster, Patrick W. Goodwill, Joan M. Greve. First demonstration of magnetic particle imaging for sentinel lymph node identification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4135.
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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 | 0.000 |
| 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".