Evidence map›Paper›PMID 28042342›Full record

ArticleTheranostics2017

The Landscape of Clinical Trials Evaluating the Theranostic Role of PET Imaging in Oncology: Insights from an Analysis of ClinicalTrials.gov Database.

Yu-Pei Chen, Jia-Wei Lv, Xu Liu, Yuan Zhang, Ying Guo, Ai-Hua Lin, Ying Sun, Yan-Ping Mao, Jun Ma

Abstract read
In one paragraph

Article in Theranostics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov.International journal of environmental research and public health · 2021
    Review
  6. Article
  7. Article
  8. 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

9 authors.

Yu-Pei ChenDepartment of Radiation Oncology, Sun Yat-sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, People's Republic of China.
Jia-Wei LvDepartment of Radiation Oncology, Sun Yat-sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, People's Republic of China.
Xu LiuDepartment of Radiation Oncology, Sun Yat-sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, People's Republic of China.
Yuan ZhangDepartment of Radiation Oncology, Sun Yat-sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, People's Republic of China.
Ying GuoClinical Trials Centre, Sun Yat-sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, People's Republic of China.
Ai-Hua LinDepartment of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China.
Ying SunDepartment of Radiation Oncology, Sun Yat-sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, People's Republic of China.
Yan-Ping MaoDepartment of Radiation Oncology, Sun Yat-sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, People's Republic of China.; Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States.
Jun MaDepartment of Radiation Oncology, Sun Yat-sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the war on cancer marked by personalized medicine, positron emission tomography (PET)-based theranostic strategy is playing an increasingly important role. Well-designed clinical trials are of great significance for validating the PET applications and ensuring evidence-based cancer care. This study aimed to provide a comprehensive landscape of the characteristics of PET clinical trials using the substantial resource of ClinicalTrials.gov database. We identified 25,599 oncology trials registered with ClinicalTrials.gov in the last ten-year period (October 2005-September 2015). They were systematically reviewed to validate classification into 519 PET trials and 25,080 other oncology trials used for comparison. We found that PET trials were predominantly phase 1-2 studies (86.2%) and were more likely to be single-arm (78.9% vs. 57.9%,

Indexed as

Clinical Trials as TopicHumansNeoplasmsPositron Emission Tomography Computed TomographyTheranostic NanomedicineClinical trialClinicalTrials.gov.Evidence-based careOncologyPersonalized medicinePET

Identifiers

PMID28042342
PMCPMC5197072

What OpenQuestion holds

Textmetadata
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.