Evidence map›Paper›PMID 38068290›Full record

ArticleJournal of clinical medicine2023

The Advantages of Next-Generation Sequencing Molecular Classification in Endometrial Cancer Diagnosis.

Daniela Rivera, Michele Paudice, Giulia Accorsi, Floriana Valentino, Marta Ingaliso, Ada Pianezzi, Paola Roggieri, Lucia Trevisan, Giulia Buzzatti, Serafina Mammoliti and 5 more

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. 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

15 authors.

Daniela RiveraHereditary Cancer Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy.ORCID 0000-0001-7515-3608
Michele PaudiceDepartment of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Viale Benedetto XV 6, 16132 Genova, Italy.
Giulia AccorsiHereditary Cancer Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy.
Floriana ValentinoHereditary Cancer Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy.
Marta IngalisoDepartment of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Viale Benedetto XV 6, 16132 Genova, Italy.
Ada PianezziHereditary Cancer Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy.
Paola RoggieriHereditary Cancer Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy.
Lucia TrevisanHereditary Cancer Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy.
Giulia BuzzattiHereditary Cancer Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy.
Serafina MammolitiMedical Oncology Unit 1, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy.ORCID 0000-0002-6560-0061
Fabio BarraUnit of Obstetrics and Gynecology, P.O. "Ospedale del Tigullio"-ASL4, Via G. B. Ghio 9, Metropolitan Area of Genoa, 16043 Chiavari, Italy.ORCID 0000-0003-4117-6603
Simone FerreroDepartment of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Largo Paolo Daneo 3, 16132 Genova, Italy.ORCID 0000-0003-2225-5568
Gabriella CirmenaHereditary Cancer Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy.
Viviana GismondiHereditary Cancer Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy.
Valerio Gaetano VelloneDepartment of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Viale Benedetto XV 6, 16132 Genova, Italy.ORCID 0000-0002-5107-1584

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Endometrial cancer (EC) is the most frequent gynecological cancer. The ESGO/ESTRO/ESP 2020 guidelines identify prognostic groups based on morpho-molecular characteristics. This study aims to evaluate the clinical applicability of NGS analysis to define an appropriate risk class and to improve the diagnostic and prognostic stratification of ECs. Cases of serous carcinoma (OHEC) and high- (HGEC) and low-grade (LGEC) endometrioid carcinoma diagnosed with the morphological and immunohistochemical (IHC) protocols were considered. After a standardized pre-analytical phase, tumor DNA was semi-automatically extracted and analyzed using NGS with a panel of 14 genes. A total of 63 cases were considered. NGS analysis was successful in 60 cases; all of these were classified according to the new diagnostic algorithm. The molecular risk classification showed a good correlation with the morphological (k = 0.8). The study showed that the protocols of the pre-analytical and analytical phases used are robust and can lead to molecular results that fall within the standards required, which can be used in clinical practice for more precise diagnostic-therapeutic management of patients. The implementation of the classification is particularly relevant for better prognostic stratification of HGECs. In addition, the identification of a suspicious VUS in

Indexed as

endometrial cancer (EC)multigene-NGS panelPOLETP53

Identifiers

PMID38068290
PMCPMC10707080

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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.