Abstract 1138: Non-invasive detection and quantification of tumor-associated macrophage density with magnetic particle imaging
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».