Evidence map›Paper›PMID 29796937›Full record

ReviewUpdates in surgery2018

Clinical impact of molecular classifications in gastric cancer.

Daniele Marrelli, Karol Polom, Alessandro Neri, Franco Roviello

Abstract readReview
PubMed Publisher
In one paragraph

Review in Updates in surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
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  7. Review
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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.

Daniele MarrelliDepartment of Medicine, Surgery and Neurosciences, University of Siena, 53100, Siena, Italy. marrelli@unisi.it.
Karol PolomDepartment of Medicine, Surgery and Neurosciences, University of Siena, 53100, Siena, Italy.
Alessandro NeriDepartment of Medicine, Surgery and Neurosciences, University of Siena, 53100, Siena, Italy.
Franco RovielloDepartment of Medicine, Surgery and Neurosciences, University of Siena, 53100, Siena, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treatment options to gastric cancer (GC) have been changing in recent years from a standard to a tailored approach. Different individualized procedures can range from endoscopic resection, D2 with open or minimally invasive approach, to neo-adjuvant therapy followed by extended surgery. In more advanced stages, a combined approach with the inclusion of intraperitoneal chemo-hyperthermia (HIPEC) may represent a new advanced option. The inclusion of histological type according to Laurén classification in the flowchart of treatment could increase both accuracy and effectiveness of such tailored approach. New molecular classifications of GC have been introduced recently and translational clinical studies are ongoing. These classifications are expected to be included in multidisciplinary treatment of GC. In particular, in the group with microsatellite instability a less extended lymphadenectomy may be proposed. Also tailored neo-adjuvant treatment may be proposed according to molecular classifications. The group of patients with epithelial-to-mesenchymal transition shows very high propensity to peritoneal dissemination, as well as N-metastases, and may benefit from prophylactic HIPEC and extended lymphadenectomy when confirmed in prospective trials.

Indexed as

Precision MedicineBiomarkers, TumorChromosomal InstabilityClinical Decision-MakingCombined Modality TherapyHumansMutationStomach NeoplasmsBiomarkers, TumorLauren histotypeMismatch repair deficiencyMultimodality approachStomach cancerTailored treatmentTranslational research

Identifiers

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.