Comparing subharmonic imaging and immunohistochemical markers of angiogenesis
Notice bibliographique
Résumé
This work examines angiogenesis associated with the growth of tumors and metastasis. The process of pathologic angiogenesis (neovascularization) can be broken down into three main steps: 1) activation 2) migration and 3) proliferation of endothelial cells. Development of techniques that can measure the functional and structural properties of tumor vascularity at different stages during progression of the tumor, is therefore, imperative for monitoring and controlling the spread of cancer. Contrast enhanced ultrasound imaging has the capability of scanning in real-time without fear of radiation. Contrast agents are composed of gas-filled microbubbles, which exhibit nonlinear scattering when insonified by an ultrasound beam. This leads to the emission of harmonic components of the fundamental signal in the received echoes. The component emitted at half of the fundamental frequency (f₀/2) is called the subharmonic component, and is used in this study as it provides an improved contrast-to-tissue-ratio for ultrasound imaging of tumor angiogenesis. Pre-clinical and clinical ultrasound imaging studies of tumor angiogenesis are often conducted with pathological markers as the reference standard. There is, however, no established standard for how to analyze specimen markers and compare them to imaging parameters. Hence, the purpose of this study was to compare different methods for obtaining tumor neovascularity parameters based on immunohistochemical markers of angiogenesis and compare those to contrast-enhanced subharmonic ultrasound imaging (SHI) in a murine xenograft model. Eighty-five athymic, nude, female rats were implanted with 5 x 10⁶ breast cancer cells (MDA-MB-231) in the mammary fat pad. The ultrasound contrast agent Defmity (Lantheus Medical Imaging, N Billerica, MA) was injected in a tail vein (dose: 36 [mu]l) and pulse-inversion SHI was performed using a modified (able to perform pulse-inversion subharmonic imaging) Sonix RP scanner (Analogic Ultrasound, Richmond, BC, Canada) with a L9-4 linear array. Transmitting and receiving frequencies used were 8 and 4 MHz, respectively. These frequencies were selected as they fall in the frequency band of the transducer and can be used clinically for breast imaging, since the ultimate goal is to use the monitoring of angiogenesis for human breast cancer studies. After the experiments, tumor specimens were sliced corresponding to the imaging planes and stained for endothelial cells (CD31), vascular endothelial growth factor (VEGF), and cyclooxygenase-2 (COX-2). Tumor neovascularity was assessed in 4 different ways using a histomophometry system based on a Labophot-2 microscope (Nikon, Melville, NJ) at 100x magnification 1) over the entire tumor, 2) in small sub-regions of interest (ROIs), 3) in the tumor periphery (within 2 mm of the margin) and centrally, and 4) in regions of maximum marker expression (so called hotspots). Results from the specimens and from SHI were compared using linear regression analysis. Of the 85 rats implanted 54 (64 %) exhibited tumor growth and 38 were successfully imaged. SHI depicted the tortuous morphology of tumor neovessels and delineated small areas of necrosis. SHI measures of tumor vascularity did not correlate with the immunohistochemical markers when assessed over the entire tumor area (p > 0.60) or over the small sub-ROIs (p > 0.18). However, when the specimens were sub-dived into a central and a peripheral region, COX-2 and VEGF was found to correlate with SHI in the periphery (r = -0.42 with p = 0.005 and r = -0.32 with p = 0.049, respectively). Another correlation in this breast cancer model was seen between SHI and COX-2 in the hotspots of the tumors (r = 0.25; p = 0.007). The results indicate that, when comparing quantitative contrast measures of tumor neovascularity to immunohistochemical markers of angiogenesis in xenograft models it appears that sub-ROIs corresponding to the biologically active region (i.e., the tumor periphery) should be used to account for tumor heterogeneity and development.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».