Evidence map›Paper›PMID 28416757›Full record

ArticleOncotarget2017

Theranostic imaging of liver cancer using targeted optical/MRI dual-modal probes.

Qingshan Chen, Wenting Shang, Chaoting Zeng, Kun Wang, Xiaoyuan Liang, Chongwei Chi, Xiao Liang, Jian Yang, Chihua Fang, Jie Tian

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 11% 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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. Article
  9. Simultaneous enhancement of TTheranostics · 2022
    Article
  10. Review
  11. Review
  12. Article
  13. Review
  14. Article
  15. Development andWorld journal of gastroenterology · 2019
    Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. 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

10 authors at 5 institutions in 1 country.

Qingshan ChenDepartment of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China.
Wenting ShangKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Chaoting ZengDepartment of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China.
Kun WangKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Xiaoyuan LiangKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Chongwei ChiKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Xiao LiangKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Jian YangDepartment of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China.
Chihua FangDepartment of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China.
Jie TianKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Beijing National Laboratory for Molecular Sciences · CNChinese Academy of Sciences · CNShandong Institute of Automation · CNSouthern Medical University · CNZhujiang Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accurate preoperative detection and intraoperative navigation afforded by imaging techniques have had significant impact on the success of liver cancer surgeries. However, it is difficult to achieve satisfactory performance in both diagnosis and surgical treatment processes using any single modality imaging method. Here, we report the synthesis and characteristics of a novel dual-modality magnetic resonance imaging (MRI) and near-infrared fluorescence (NIRF) probe and verify its feasibility in nude mouse models with liver cancer. The probes are comprised of superparamagnetic iron oxide (SPIO) nanoparticles coated with liposomes to which a tumor-targeted agent, Arg-Gly-Asp peptides (RGD), and a NIRF dye (indocyanine green, ICG) have been conjugated. Specific targeting, biodistribution, and the imaging ability of the probes for MRI-NIRF were examined. Furthermore, we applied the dual-modality methodology toward the preoperative diagnosis and intraoperative guidance of radical resection in mouse models with both orthotopic liver tumors and intrahepatic tumor metastasis. The study demonstrated that both MRI and fluorescent images showed clear tumor delineation after probe injection (SPIO@Liposome-ICG-RGD). The contrast-to-noise ratio obtained from MRI was 31.9 ± 25.4 at post-injection for the preoperative diagnosis, which is helpful for detecting small tumors (0.9 ± 0.5 mm). The maximum tumor to background ratio of NIRF imaging was 2.5 ± 0.3 at 72 h post-injection for effectively capturing miniscule tumor lesions (0.6 ± 0.3 mm) intraoperatively. The novel MRI-NIRF dual modality probes are promising for the achievement of more accurate liver tumor detection and resection.

Indexed as

AnimalsCell Line, TumorFeasibility StudiesFluorescent DyesHep G2 CellsHumansLiver NeoplasmsMagnetic Resonance ImagingMiceMice, NudeMultimodal ImagingNeoplasm TransplantationOligopeptidesOptical ImagingSpectroscopy, Near-InfraredTheranostic Nanomedicinearginyl-glycyl-aspartic acidFluorescent DyesOligopeptidesdual-modalityintraoperative navigationliver cancerMRI/opticalpreoperative diagnosis

Identifiers

PMID28416757
PMCPMC5464824
OpenAlexW2590661335

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.