Evidence map›Paper›PMID 25806256›Full record

ReviewTranslational lung cancer research2013

Prognostic and predictive biomarkers in early stage non-small cell lung cancer: tumor based approaches including gene signatures.

Simona Carnio, Silvia Novello, Mauro Papotti, Marco Loiacono, Giorgio Vittorio Scagliotti

Abstract readReview
In one paragraph

Review in Translational lung cancer research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 2 pooled it
–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

39 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Preoperative splenic area as a prognostic biomarker of early-stage non-small cell lung cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023
    Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT.International journal of computer assisted radiology and surgery · 2022
    Article
  16. Review
  17. Review
  18. Article
  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

5 authors.

Simona CarnioUniversity of Torino, Department of Oncology, Torino, Italy.
Silvia NovelloUniversity of Torino, Department of Oncology, Torino, Italy.
Mauro PapottiUniversity of Torino, Department of Oncology, Torino, Italy.
Marco LoiaconoUniversity of Torino, Department of Oncology, Torino, Italy.
Giorgio Vittorio ScagliottiUniversity of Torino, Department of Oncology, Torino, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In early stage non-small cell lung cancer (NSCLC) large randomized trials have demonstrated that in patients with radically resected disease adjuvant chemotherapy improves 5-year survival rates. However, a customization of systemic treatment is needed to avoid treatments in patients cured by surgery alone or to justify the use of adjuvant chemotherapy in high risk patients, including those in stage IA. Recently, the possibility of identifying prognostic and predictive factors related to the genetic signatures of the tumor that could affect adjuvant and neo-adjuvant treatment choices for resectable non-small cell lung cancer (NSCLC) has been of interest. This review summarizes the current status and future opportunities for clinical application of genotyping and genomic tests in early NSCLC.

Indexed as

early stagesgene signaturesnon-small cell lung cancer (NSCLC)Prognostic and predictive biomarkers

Identifiers

PMID25806256
PMCPMC4367732

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.