Evidence map›Paper›PMID 42707732›Full record

ArticleFrontiers in oncology2026

Correction: Deep learning accurately and reliably segments pelvic vascular structure in CT scans of gynecologic cancer patients.

Hyeonseung Kim, Min Jin Jeong, Kyoyeong Koo, Youn Jin Choi, Eun Seo Heo, Woohyun Nam, Sangyun Kang, U-Young Lee, Yi-Suk Kim, Keun Ho Lee and 1 more

Abstract readPublished Erratum
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Hyeonseung Kim *SKIA, Inc., Seoul, Republic of Korea.
Min Jin Jeong *Department of Obstetrics and Gynecology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Kyoyeong KooDepartment of Obstetrics and Gynecology, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
Youn Jin ChoiDepartment of Obstetrics and Gynecology, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
Eun Seo HeoSKIA, Inc., Seoul, Republic of Korea.
Woohyun NamSKIA, Inc., Seoul, Republic of Korea.
Sangyun KangSKIA, Inc., Seoul, Republic of Korea.
U-Young LeeThe Catholic Institute for Applied Anatomy, Department of Anatomy, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Yi-Suk KimThe Catholic Institute for Applied Anatomy, Department of Anatomy, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Keun Ho LeeDepartment of Obstetrics and Gynecology, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
Chan-Ung ParkSKIA, Inc., Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

[This corrects the article DOI: 10.3389/fonc.2026.1687859.].

Indexed as

blood vesselscomputed tomography angiographydeep learningfemalegenital neoplasmsiliac artery

Identifiers

PMID42707732
PMCPMC13548007

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.