Evidence map›Paper›PMID 38649693›Full record

ArticleScientific data2024

Large-scale uterine myoma MRI dataset covering all FIGO types with pixel-level annotations.

Haixia Pan, Minghuang Chen, Wenpei Bai, Bin Li, Xiaoran Zhao, Meng Zhang, Dongdong Zhang, Yanan Li, Hongqiang Wang, Haotian Geng and 5 more

Open access · goldAbstract readDataset
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
7.6field-weighted citation impact, top 2% 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

6 citing papers in PubMed, 13 citations in OpenAlex.

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  5. Review
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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 at 4 institutions in 1 country.

Haixia Pan *College of Software, Beihang University, Beijing, 100191, China. haixiapan@buaa.edu.cn.
Minghuang Chen *Department of Obstetrics and Gynecology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.
Wenpei BaiDepartment of Obstetrics and Gynecology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China. baiwp@bjsjth.cn.
Bin LiDepartment of MRI/Emergency and Critical Care Medical Center, Beijing Shijitan Hospital, Capital Medical University/Peking University, Ninth Clinical Medical College, Beijing, 100038, China. libin2434@bjsjth.cn.
Xiaoran ZhaoCollege of Software, Beihang University, Beijing, 100191, China.
Meng ZhangCollege of Software, Beihang University, Beijing, 100191, China.
Dongdong ZhangCollege of Software, Beihang University, Beijing, 100191, China.
Yanan LiCollege of Software, Beihang University, Beijing, 100191, China.
Hongqiang WangCollege of Software, Beihang University, Beijing, 100191, China.
Haotian GengCollege of Software, Beihang University, Beijing, 100191, China.
Weiya KongDepartment of Obstetrics and Gynecology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.
Cong YinDepartment of Obstetrics and Gynecology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.
Linfeng HanCollege of Software, Beihang University, Beijing, 100191, China.
Jiahua LanCollege of Software, Beihang University, Beijing, 100191, China.
Tian ZhaoCollege of Software, Beihang University, Beijing, 100191, China.
Beihang University · CNBeijing Shijitan Hospital · CNCapital Medical University · CNPeking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Uterine myomas are the most common pelvic tumors in women, which can lead to abnormal uterine bleeding, abdominal pain, pelvic compression symptoms, infertility, or adverse pregnancy. In this article, we provide a dataset named uterine myoma MRI dataset (UMD), which can be used for clinical research on uterine myoma imaging. The UMD is the largest publicly available uterine MRI dataset to date including 300 cases of uterine myoma T2-weighted imaging (T2WI) sagittal patient images and their corresponding annotation files. The UMD covers 9 types of uterine myomas classified by the International Federation of Obstetrics and Gynecology (FIGO), which were annotated and reviewed by 11 experienced doctors to ensure the authority of the annotated data. The UMD is helpful for uterine myomas classification and uterine 3D reconstruction tasks, which has important implications for clinical research on uterine myomas.

Indexed as

LeiomyomaMagnetic Resonance ImagingUterine NeoplasmsFemaleHumansUterus

Identifiers

PMID38649693
PMCPMC11035617
OpenAlexW4395010502

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.