Evidence map›Paper›PMID 29580432›Full record

ReviewSeminars in oncology2017

Surveillance imaging following definitive radiotherapy for non-small cell lung cancer: What is the clinical impact?

Brandon A Dyer, Megan E Daly

Abstract readReview
In one paragraph

Review in Seminars in oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

2 authors.

Brandon A DyerDepartment of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA.
Megan E DalyDepartment of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA. Electronic address: medaly@ucdavis.edu.

Funding

Staff InvestigatorsP30CA093373 · NCI · UNIVERSITY OF CALIFORNIA DAVIS · PI Laura Marcu · 2002 to 2026
$84.9M
UC Davis Paul Calabresi K12 Clinical Oncology Research Career Development ProgramK12CA138464 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI BIRKELAND, ANDREW CHARLES, LARA, PRIMO N. · 2011 to 2025
$11.4M
NCI NIH HHS K12 CA138464NCI NIH HHS P30 CA093373
6 · The paper itself

Abstract

Lung cancer is the leading cause of cancer death worldwide. Recurrence rates at all stages are high, but evidence-based post-treatment surveillance imaging strategies to detect recurrence are poorly defined, and salvage options are frequently limited. A number of national and international oncology guidelines address post-treatment imaging, but are largely based on low-level, retrospective evidence because of a paucity of high-quality data, particularly in regard to cost-effectiveness and quality-of-life endpoints. Given the lack of randomized data addressing appropriate surveillance imaging modality and interval following definitive treatment of lung cancer, there remains an unmet clinical need. Meaningful surveillance endpoints should include the financial impact, patient quality-of-life outcomes, and access-to-care issues associated with intensive follow-up to ensure that guidelines reflect quality and sustainability. A need for prospective randomized data on the subject of imaging surveillance after definitive local therapy remains an unmet need, and an opportunity for collaboration and further research.

Indexed as

Carcinoma, Non-Small-Cell LungCost-Benefit AnalysisDiagnostic ImagingHumansLung NeoplasmsNeoplasm Recurrence, LocalOutcome Assessment, Health CareQuality of LifeSentinel Surveillancelung cancerradiotherapysurveillance imaging

Identifiers

PMID29580432
PMCPMC5903453

What OpenQuestion holds

Textmetadata
LicenceTDM
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.