Evidence map›Paper›PMID 36405422›Full record

ReviewThe Journal of clinical and aesthetic dermatology2022

Using Gene Expression Profiling to Personalize Skin Cancer Management.

Jo Ann LeQuang

Open access · greenAbstract readReview
In one paragraph

Review in The Journal of clinical and aesthetic dermatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 44% of its field
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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. The Role of Biomarkers in Guiding Clinical Decision-Making in Oncology.Journal of the advanced practitioner in oncology · 2023
    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

1 author at 1 institution in 1 country.

Jo Ann LeQuangMs. LeQuang is Owner of LeQ Medical in Angleton, Texas; Director of Scientific Communications at NEMA Research, Inc., in Naples, Florida; and Founding Director of No Baby Blisters in Colorado Springs, Colorado.
Nema Research (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Risk-stratification of cancer, traditionally performed through staging, directs optimal disease management decisions with the result of improved patient outcomes. Many forms of cutaneous cancer have overall excellent survival rates, but conventional staging methods are imperfect in identifying high-risk patients. Gene expression profiling (GEP) is a clinically available, objective metric that can be used in conjunction with traditional clinicopathological staging to help clinicians stratify risk in patients with skin cancer, even in those who lack traditional risk markers. For patients with melanoma, the 31-GEP test provides personalized prognostic information that can guide risk-appropriate clinical management and surveillance decisions. The i31-GEP integrates 31-GEP results with clinicopathological features to provide a risk of recurrence (i31-GEP for ROR) and likelihood of having a positive sentinel lymph node biopsy (SLNB) (i31-GEP for SLNB) for patients with melanoma. For patients with cutaneous squamous cell carcinoma who have at least one risk factor, the 40-GEP test allows for better risk stratification by identifying the high-risk patients who are most likely to develop metastasis. These tests can be easily integrated into clinical practice to help guide treatment choices.

Indexed as

31-GEP40-GEPcutaneous squamous cell carcinomagene expression profilingi31-GEPmelanomaprognosisrisk of recurrencerisk stratificationsentinel lymph node biopsy

Identifiers

PMID36405422
PMCPMC9664966
OpenAlexW4309655082

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.