Evidence map›Paper›PMID 34830824›Full record

ArticleCancers2021

Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers.

Byung Wook Kim, Min Chul Choi, Min Kyu Kim, Jeong-Won Lee, Min Tae Kim, Joseph J Noh, Hyun Park, Sang Geun Jung, Won Duk Joo, Seung Hun Song and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 39% 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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Artificial intelligence in the diagnosis and management of gynecologic cancer.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025
    Review
  2. Article
  3. Article
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

11 authors at 3 institutions in 1 country.

Byung Wook KimDepartment of Information and Communication Engineering, Changwon National University, Changwon 51140, Korea.
Min Chul ChoiComprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea.ORCID 0000-0003-4509-6731
Min Kyu KimDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon 51353, Korea.ORCID 0000-0002-1937-3611
Jeong-Won LeeGynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.ORCID 0000-0002-6110-4909
Min Tae KimDepartment of Information and Communication Engineering, Changwon National University, Changwon 51140, Korea.
Joseph J NohGynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
Hyun ParkComprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea.
Sang Geun JungComprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea.
Won Duk JooComprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea.
Seung Hun SongComprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea.
Chan LeeComprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea.
Changwon National University · KRCHA University Bundang Medical Center · KRSamsung Medical Center · KR

Funding

Industry-Academic Cooperation Based Platform R&D funded Korea Ministry of SMEs and Startups in 2020 (S3014213)National Research Foundation of Korea (NRF) grant funded by the Korean government NRF-2019R1A2C4069822
6 · The paper itself

Abstract

To support the implementation of genome-based precision medicine, we developed machine learning models that predict the recurrence of patients with gynecologic cancer in using immune checkpoint inhibitors (ICI) based on clinical and pathologic characteristics, including Lynch syndrome-related screening markers such as immunohistochemistry (IHC) and microsatellite instability (MSI) tests. To accomplish our goal, we reviewed the patient demographics, clinical data, and pathological results from their medical records. Then we identified seven potential characteristics (four MMR IHC [

Indexed as

immune checkpoint inhibitorsLynch syndromemachine learningrecurrence

Identifiers

PMID34830824
PMCPMC8616351
OpenAlexW3213579888

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.