Evidence map›Paper›PMID 26055171›Full record

ArticleTranslational oncology2015

[(18)F]-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography of LAPC4-CR Castration-Resistant Prostate Cancer Xenograft Model in Soft Tissue Compartments.

Keisha C McCall, Su-Chun Cheng, Ying Huang, Nancy E Kohl, Tanya Tupper, Annick D Van den Abbeele, Katherine A Zukotynski, Christopher J Sweeney

Open access · goldAbstract read
In one paragraph

Article in Translational oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Influence on [EJNMMI research · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Keisha C McCallDepartment of Imaging, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA; Center for Biomedical Imaging in Oncology (Lurie Family Imaging Center), Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA. Electronic address: kmccall@partners.org.
Su-Chun ChengDepartment of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA.
Ying HuangDepartment of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA.
Nancy E KohlCenter for Biomedical Imaging in Oncology (Lurie Family Imaging Center), Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA.
Tanya TupperCenter for Biomedical Imaging in Oncology (Lurie Family Imaging Center), Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA.
Annick D Van den AbbeeleDepartment of Imaging, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA; Center for Biomedical Imaging in Oncology (Lurie Family Imaging Center), Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston MA 02115, USA.
Katherine A ZukotynskiDepartment of Medical Imaging, Sunnybrook Hospital, University of Toronto, 263 McCaul St-4th Floor, Toronto, Ontario M5T 1W7, Canada.
Christopher J SweeneyDepartment of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston MA 02115, USA.
Dana-Farber Cancer Institute · USBrigham and Women's Hospital · USSunnybrook Hospital · CA

Funding

Tumor and Circulating Markers as Links Between Obesity and Lethal Prostate CanceP50CA090381 · NCI · DANA-FARBER CANCER INSTITUTE · PI LODA, MASSIMO · 2002 to 2017
$36.2M
NCI NIH HHS P50 CA090381
6 · The paper itself

Abstract

Preclinical xenograft models have contributed to advancing our understanding of the molecular basis of prostate cancer and to the development of targeted therapy. However, traditional preclinical in vivo techniques using caliper measurements and survival analysis evaluate the macroscopic tumor behavior, whereas tissue sampling disrupts the microenvironment and cannot be used for longitudinal studies in the same animal. Herein, we present an in vivo study of [(18)F]-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) designed to evaluate the metabolism within the microenvironment of LAPC4-CR, a unique murine model of castration-resistant prostate cancer. Mice bearing LAPC4-CR subcutaneous tumors were administered [(18)F]-FDG via intravenous injection. After a 60-minute distribution phase, the mice were imaged on a PET/CT scanner with submillimeter resolution; and the fused PET/CT images were analyzed to evaluate tumor size, location, and metabolism across the cohort of mice. The xenograft tumors showed [(18)F]-FDG uptake that was independent of tumor size and was significantly greater than uptake in skeletal muscle and liver in mice (Wilcoxon signed-rank P values of .0002 and .0002, respectively). [(18)F]-FDG metabolism of the LAPC4-CR tumors was 2.1 ± 0.8 ID/cm(3)*wt, with tumor to muscle ratio of 7.4 ± 4.7 and tumor to liver background ratio of 6.7 ± 2.3. Noninvasive molecular imaging techniques such as PET/CT can be used to probe the microenvironment of tumors in vivo. This study showed that [(18)F]-FDG-PET/CT could be used to image and assess glucose metabolism of LAPC4-CR xenografts in vivo. Further work can investigate the use of PET/CT to quantify the metabolic response of LAPC4-CR to novel agents and combination therapies using soft tissue and possibly bone compartment xenograft models.

Identifiers

PMID26055171
PMCPMC4487789
OpenAlexW1959412140

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.