Evidence map›Paper›PMID 28344664›Full record

ReviewTherapeutic advances in medical oncology2017

Current understanding and approach to well differentiated lung neuroendocrine tumors: an update on classification and management.

Talal Hilal

Abstract readReview
In one paragraph

Review in Therapeutic advances in medical oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. [Surgical Resection and Prognosis of Bronchopulmonary Carcinoid].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2019
    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

1 author.

Talal HilalDivision of Hematology and Medical Oncology, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuroendocrine tumors (NETs) are rare neoplasms that can arise from any tissue. They are classified based on embryonic gut derivative (i.e. foregut, midgut and hindgut) with midgut tumors being the most common (e.g. gastrointestinal NET). The second most common category of NETs is that which arises from the lung. In fact, 25% of primary lung cancers are NETs, including small cell lung cancer (SCLC), which comprises 20% of all lung cancers. The remaining 5% are large cell neuroendocrine cancer (LCNEC, 3%), typical carcinoids (TCs, 1.8%), and atypical carcinoids (ACs, 0.2%). The less common TCs/ACs are well differentiated lung NETs. Their incidence has been increasing in more recent years and although these tumors are slow growing, advanced disease is associated with poor survival. There have been advances in classification of lung NETs that have allowed for more appropriate management upfront. They are cured by surgical resection when disease is limited. However, advanced and metastatic disease requires medical therapy that is ever changing and expanding. In this review, the aim is to summarize the current understanding and classification of well differentiated lung NETs (i.e. TCs and ACs), and focus on recent updates in medical management of advanced disease, along with a brief discussion on potential future discoveries.

Indexed as

bronchial carcinoidcarcinoid tumorNETneuroendocrine tumor

Identifiers

PMID28344664
PMCPMC5349423

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.