Evidence map›Paper›PMID 33209647›Full record

ReviewTranslational lung cancer research2020

How should molecular findings be integrated in the classification for lung cancer?

Yin P Hung, Lucian R Chirieac

Abstract readReview
In one paragraph

Review in Translational lung cancer research, 2020. 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
–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

6 citing papers in PubMed.

  1. Targeting Lung Cancer Metabolism: Therapeutic Opportunities and Challenges.Advances in experimental medicine and biology · 2026
    Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Food, Nutrition, Physical Activity and Microbiota: Which Impact on Lung Cancer?International journal of environmental research and public health · 2021
    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

2 authors.

Yin P HungDepartment of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Lucian R ChirieacDepartments of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of molecular diagnostics in the diagnosis and management of patients with advanced lung cancer has become widespread. Although molecular classification has increasingly been incorporated in the pathologic classification of certain types of human tumors (particularly within the hematologic, glial, and bone/soft tissue malignancies), genetic findings have not been formally incorporated into the pathologic classification of lung cancer, which presently relies solely on the assessment of histologic and immunophenotypic characteristics. Whether molecular classification should be adopted in lung cancer would depend on the diagnostic, prognostic, and predictive impacts of such classification-and whether these impacts confer significant values additive to those derived from the routine histologic and immunophenotypic assessment. We provide a brief overview on the genetics of lung cancer, including adenocarcinoma, squamous cell carcinoma, and neuroendocrine tumors (small cell carcinoma, large cell neuroendocrine carcinoma, and carcinoid tumors). We consider the values of molecular information with some examples, in terms of the current diagnostic, prognostic, and predictive impacts. Finally, we discuss the conceptual and technical challenges of adopting a molecular classification for lung cancer in clinical management for patients. While there are conceptual and technical hurdles to tackle in implementing molecular classification in the pathologic classification of lung cancer, such integrated histologic-molecular diagnosis may allow one to personalize and optimize therapy for patients with advanced lung cancer.

Indexed as

ALKEGFRlung cancermolecular classificationMolecular diagnostics

Identifiers

PMID33209647
PMCPMC7653151

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.