Imaging the life and death of tumors in living subjects: Preclinical PET imaging of proliferation and apoptosis

2010 ◽  
Vol 2 (10) ◽  
pp. 483 ◽  
Author(s):  
Quang-Dé Nguyen ◽  
Eric O. Aboagye
2021 ◽  
Author(s):  
Neil Gerard Quigley ◽  
Katja Steiger ◽  
Sebastian Hoberück ◽  
Norbert Czech ◽  
Maximilian Alexander Zierke ◽  
...  

Abstract PurposeTo develop a new probe for the αvβ6-integrin and assess its potential for PET imaging of carcinomas.MethodsGa-68-Trivehexin was synthesized by trimerization of an optimized αvβ6-integrin selective cyclicnonapeptide on the TRAP chelator core and automated labeling with Ga-68. The tracer wascharacterized by ELISA for activities towards integrin subtypes αvβ6, αvβ8, αvβ3, and α5β1, as well asby cell binding assays on H2009 (αvβ6-positive) and MDA-MB-231 (αvβ6-negative) cells. SCID micebearing subcutaneous xenografts of the same cell lines were used for dynamic (90 min) and static(75 min p.i.) μPET imaging, as well as for biodistribution (90 min p.i.). Structure-activity-relationshipswere established by comparison with the predecessor compound Ga-68-TRAP(AvB6)3. Ga-68-Trivehexin was tested for in-human PET/CT imaging of HNSCC, parotideal adenocarcinoma, andPDAC.ResultsGa-68-Trivehexin showed a high αvβ6-integrin affinity (IC50 = 0.033 nM), selectivity over othersubtypes (IC50-based factors: αvβ8, 188; αvβ3, 82; α5β1, 667), blockable uptake in H2009 cells, andnegligible uptake in MDA-MB-231 cells. Biodistribution and preclinical PET imaging confirmed a hightarget-specific uptake in tumor and a low non-specific uptake in other organs and tissues except theexcretory organs (kidneys and urinary bladder). Preclinical PET corresponded well to in-human results,showing high and persistent uptake in metastatic PDAC and HNSCC (SUVmax = 10–13) as well as inkidneys/urine. Ga-68-Trivehexin enabled PET/CT imaging of small PDAC metastases and showed highuptake in HNSCC but not in tumor-associated inflammation.ConclusionsGa-68-Trivehexin is a valuable probe for imaging of αvβ6-integrin expression in human cancers.


2014 ◽  
Author(s):  
Paul Slobbe ◽  
Albert Windhorst ◽  
Marijke Stigter-van Walsum ◽  
Robert Schuit ◽  
Egbert Smit ◽  
...  

2020 ◽  
Vol 34 (9) ◽  
pp. 653-662
Author(s):  
Qiang Wang ◽  
Dongli Song ◽  
Xiaowei Ma ◽  
Xiaodong Wu ◽  
Lei Jiang

2015 ◽  
Vol 60 (18) ◽  
pp. 7127-7149 ◽  
Author(s):  
J Cal-González ◽  
S C Moore ◽  
M-A Park ◽  
J L Herraiz ◽  
J J Vaquero ◽  
...  

2017 ◽  
Vol 16 ◽  
pp. 153601211773701 ◽  
Author(s):  
Eleni Gourni ◽  
Luigi Del Pozzo ◽  
Mark Bartholomä ◽  
Yvonne Kiefer ◽  
Philipp T. Meyer ◽  
...  

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2275
Author(s):  
Ching-Ching Yang

This study aimed to investigate the feasibility of positron range correction based on three different convolutional neural network (CNN) models in preclinical PET imaging of Ga-68. The first model (CNN1) was originally designed for super-resolution recovery, while the second model (CNN2) and the third model (CNN3) were originally designed for pseudo CT synthesis from MRI. A preclinical PET scanner and 30 phantom configurations were modeled in Monte Carlo simulations, where each phantom configuration was simulated twice, once for Ga-68 (CNN input images) and once for back-to-back 511-keV gamma rays (CNN output images) with a 20 min emission scan duration. The Euclidean distance was used as the loss function to minimize the difference between CNN input and output images. According to our results, CNN3 outperformed CNN1 and CNN2 qualitatively and quantitatively. With regard to qualitative observation, it was found that boundaries in Ga-68 images became sharper after correction. As for quantitative analysis, the recovery coefficient (RC) and spill-over ratio (SOR) were increased after correction, while no substantial increase in coefficient of variation of RC (CVRC) or coefficient of variation of SOR (CVSOR) was observed. Overall, CNN3 should be a good candidate architecture for positron range correction in Ga-68 preclinical PET imaging.


2019 ◽  
Vol 60 (10) ◽  
pp. 1483-1491 ◽  
Author(s):  
Julia G. Mannheim ◽  
Martin Mamach ◽  
Sybille Reder ◽  
Alexander Traxl ◽  
Natalie Mucha ◽  
...  

2021 ◽  
pp. 105429
Author(s):  
Garima Mann ◽  
K Ganesh Kadiyala ◽  
M Thirumal ◽  
Anjani K Tiwari ◽  
Anupama Datta

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