Methods
Step 1: Identification.
Authors independently conducted literature searches of the MEDLINE
database until 9 January 2020. From these, a comprehensive search
strategy was derived consisting of the following MeSH terms and their
relevant subheadings: “Endometrial Neoplasms”, “Lymph Node
Excision”, “Lymph Nodes” and “Treatment Outcome”. “Management”
and “Outcomes” were included as non-MeSH terms. “English and Humans
only” was applied as a filter. In Step 3, list of references of
articles were also searched.
Step 2: Article selection.
Each author screened an equal number of citations by title and abstract.
All
primary studies and systematic reviews that reported LND as a primary
intervention in a sample of women with EC confined to the uterus were
included.
Step 3: Data-charting.
The full texts of all potentially relevant articles were reviewed
collectively by the authors. A data-charting table was used to extract
the following information from each article: general data (title, year
of publication, author’s name and journal name); methodological data
(type of study, sample size, patient characteristics e.g. stage of EC);
and clinical data (use of risk stratification tool, roles of LND
identified, effectiveness of LND in these roles, clinical outcomes e.g.
survival, any alternatives to LND mentioned).
Step 4: Summarising articles.
The data-charting table was constantly updated as the new roles of LND
and “other” categories emerged. From articles that demonstrated a
clear role of LND in EC, articles were organised into the following
roles: ‘diagnostic’, ‘prognostic’, ‘therapeutic’, ‘to guide treatment
decisions’ and ‘to quantify treatment success’. “Other” categories
included: ‘alternatives to LND’, ‘methods of conducting LND’,
‘differences in LN targets’ and ‘treatments of advanced EC’. A critical
appraisal of the included studies was conducted using the Critical
Appraisal Skills Programme (CASP) checklists(15).
Figure 1 summarises the results of Steps 1-4 based on the PRISMA
Extension for Scoping Review checklist(16).