Evidence map›Paper›PMID 27367729›Full record

ReviewCancers2016

Optimizing the Detection of Circulating Markers to Aid in Early Lung Cancer Detection.

Vasudha Murlidhar, Nithya Ramnath, Sunitha Nagrath, Rishindra M Reddy

Open access · goldAbstract readReview
In one paragraph

Review in Cancers, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.3field-weighted citation impact, top 18% 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

6 citing papers in PubMed, 15 citations in OpenAlex.

  1. Translational lung cancer research · 2020
    Article
  2. Review
  3. Review
  4. Review
  5. Review
  6. Tumour Heterogeneity: The Key Advantages of Single-Cell Analysis.International journal of molecular sciences · 2016
    Review
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

4 authors at 1 institution in 1 country.

Vasudha MurlidharDepartment of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. vasmdhar@umich.edu.
Nithya RamnathDivision of Hematology/Oncology, Department of Medicine, University of Michigan, Ann Arbor, MI 48109, USA. nithyar@med.umich.edu.
Sunitha NagrathDepartment of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. snagrath@umich.edu.
Rishindra M ReddySection of Thoracic Surgery, Department of Surgery, University of Michigan, Taubman Center 2120, 1500 E. Medical Center Drive, Ann Arbor, MI 48109, USA. reddyrm@med.umich.edu.
University of Michigan · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Improving early detection of lung cancer is critical to improving lung cancer survival. Studies have shown that computerized tomography (CT) screening can reduce mortality from lung cancer, but this involves risks of radiation exposure and can identify non-cancer lung nodules that lead to unnecessary interventions for some. There is a critical need to develop alternative, less invasive methods to identify patients who have early-stage lung cancer. The detection of circulating tumor cells (CTCs) are a promising area of research, but current technology is limited by a low yield of CTCs. Alternate studies are investigating circulating nucleic acids and proteins as possible tumor markers. It is critical to develop innovative methods for early lung cancer detection that may include CTCs or other markers that are low-risk and low-cost, yet specific and sensitive, to facilitate improved survival by diagnosing the disease when it is surgically curable.

Indexed as

biomarkerscirculating tumor cellslung cancermicroRNAproteomics

Identifiers

PMID27367729
PMCPMC4963803
OpenAlexW2471495200

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.