| Literature DB >> 26664966 |
Abstract
Entities:
Keywords: clinical veterinary radiology; machine learning; teleradiology; translational models; veterinary imaging
Year: 2015 PMID: 26664966 PMCID: PMC4672222 DOI: 10.3389/fvets.2015.00038
Source DB: PubMed Journal: Front Vet Sci ISSN: 2297-1769
Figure 1PET and dynamic contrast enhanced perfusion CT (DCE-pCT) maps from a subcutaneous hemangiopericytoma in the gluteal region of a dog. The PET tracers used included FDG, which is correlated to expression levels of glucose transporter proteins and hexokinases in cancer cells, to the functionality of regional microvasculature and to proliferative activity. The other tracer used was 64Cu-ATSM. It has an incompletely understood trapping mechanism. (A) FDG PET, (B) 3 h 64Cu-ATSM PET, (C) 24 h 64Cu-ATSM PET, (D) DCE-pCT blood flow, (E) DCE-pCT blood volume, (F) DCE-pCT permeability, and (G) maximum intensity projection. All images are in individually optimized window levels. The study was concerned with measuring tumor hypoxia and looked at the comparative performance of FDG and ATSM as hypoxia markers. Nine dogs were included and it was shown that in general a strong correlation exists between the ratio of maximum tumor uptake of 64Cu-ATSM to mean 64Cu-ATSM in muscle at 24 h and the maximum standard uptake value (SUV) of FDG. Differences in uptake between the tracers were seen in hypo-perfused areas. The dog shown here had a hypo-perfused region which showed the highest tumor FDG SUV, moderate 64Cu-ATSM uptake at 3 h and strong uptake at 24 h. It was concluded that the two tracers provide different biological information with overlapping spatial distribution in canine tumors, and that these differences were possibly related to tumor perfusion [based on Ref. (5)].
Figure 2Transverse B-mode ultrasound image and image mask from a European eel (. The mask (right image) shows the area of the ovaries that have been artificially induced to maturation. Machine learning algorithms use texture parameters to classify ovarian maturation in these fish. This work is part of the PRO-EEL project (www.pro-eel.eu) and received funding from the 7th Framework Programme of the European Commission under the theme “Food, Agriculture and Fisheries, and Biotechnology”.