Abstract 1138: Non-invasive detection and quantification of tumor-associated macrophage density with magnetic particle imaging
Bibliographic record
Abstract
Abstract Background: There is increasing interest in the role of tumor-associated macrophages (TAMs) in promoting cancer growth and metastatic potential.(Obeid 2013) Increased TAM density has been correlated with poor prognosis and they can contribute up to 50% of the mass in breast tumors.(Bingle 2002) Previous studies have used cellular MRI to indirectly image the spatial distribution of TAMs, through the in situ uptake of superparamagnetic iron oxide (SPIO) nanoparticles (Makela 2017). However, iron-based MRI is limited by low specificity and challenging quantification. Magnetic Particle Imaging (MPI) is an emerging cellular imaging technique that can directly detect and quantify SPIO in vivo (Zheng 2016). MPI produces a positive contrast signal that is not attenuated by biological tissue. In this study, we investigated the first application of MRI and MPI to detect and differentiate between murine breast cancer models with varying metastatic potentials. Methods: Female BALB/c mice were implanted with 300,000 4T1 (n=3) or 168FARN (n=3) into the inguinal mammary fat pad. Tumors were grown for 3 weeks. 24 hours prior to imaging, 100μL of ferucarbotran (Magnetic Insight) was administered IV. One mouse from each group was first imaged on a 3T clinical MRI (GE Healthcare) with a custom-gradient insert located at Robarts Research Institute. A balanced steady-state free precession pulse sequence was used with imaging time of 30mins. The mice were then sacrificed and fixed in formalin. MPI was performed at Stanford University with the MPI system (Magnetic Insight) alongside three reference fiducials of known concentration. Iron quantification was performed with VivoQuant (inviCRO). Tumor tissue was extracted for histology and ex vivo imaging. Results: MRI signal voids were observed throughout the 4T1 tumor, but predominately in the tumor periphery. Fewer voids were observed in the 168FARN model, consistent with previous studies.3 MPI signal was observed in two out of three 4T1 tumors. Signal is seen across the entire tumor since the MPI is a sagittal projection compared to a single MRI slice. MPI quantification of SPIO in the 4T1 tumors indicated a consistent uptake, of 4.7 and 5.9 μg of iron. Signal was also detectable in the liver and tail at the site of injection. Fewer voids were observed in the 168FARN model, consistent with previous studies (Makela 2017). MPI signal was not detected in the tumors of the 168FARN group, consistent with the low iron uptake observed in the MRI. Histological analysis will be performed to verify the SPIO and macrophage distribution in tumor samples. Conclusions: By combining the high spatial resolution of MRI with the accurate quantification and specificity of MPI, additional information can be obtained on TAM presence and distribution. In addition to aiding therapeutic development, this information could be utilized to more accurately grade cancer. Citation Format: Jeffrey M. Gaudet, Ashley V. Makela, Paula J. Foster. Non-invasive detection and quantification of tumor-associated macrophage density with magnetic particle imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1138.
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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".