ArticleUltrasonography (Seoul, Korea)2017
Ultrasound contrast-enhanced study as an imaging biomarker for anti-cancer drug treatment: preliminary study with paclitaxel in a xenograft mouse tumor model (secondary publication).
Article in Ultrasonography (Seoul, Korea), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Meta-Analysis of Contrast Enhanced Ultrasound in Judging Benign and Malignant Thyroid Tumors.Computational and mathematical methods in medicine · 2021Pooled it
- Correlations between contrast-enhanced ultrasound and microvessel density in non-small cell lung cancer: A prospective study.Frontiers in oncology · 2023Article
- Comparison of tumor angiogenesis in subcutaneous and orthotopic LNCaP mouse models using contrast-enhanced ultrasound imaging.Translational cancer research · 2021Article
- In Vivo Imaging With Confirmation by Histopathology for Increased Rigor and Reproducibility in Translational Research: A Review of Examples, Options, and Resources.ILAR journal · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of this study was to assess tumor angiogenesis using contrast-enhanced ultrasonography (CEUS) of human prostate cancer cells (PC3) that were implanted in mice before and after paclitaxel injection.
methodsTwelve mice were injected with human PC3. The mice were grouped into two groups; one was the paclitaxel-treated group (n=6) and the other was the control group (n=6). Before administering paclitaxel into the peritoneal cavity, baseline CEUS was performed after the administration of 500 μL (1×108 microbubbles) of contrast agent. The area under the curve (AUC) up to 50 seconds after injection was derived from the time-intensity curves. After injection of paclitaxel or saline, CEUS studies were performed at the 1-week follow-up. Changes in tumor volume and the AUC in both two groups were evaluated. After CEUS, the microvessel density (MVD) was compared between the groups.
resultsIn the paclitaxel-treated group, the AUC from CEUS showed a significant decrease 1-week after paclitaxel administration (P=0.030), even though the tumor volume showed no significant changes (P=0.116). In the control group, there was no significant decrease of the AUC (P=0.173). Pathologically, there was a significant difference in MVD between both groups (P=0.002).
conclusionThe AUC from the time intensity curve derived from CEUS showed an early change in response to the anti-cancer drug treatment that preceded the change in tumor size. The findings of CEUS could serve as an imaging biomarker for assessing tumor responses to anti-cancer drug treatment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.