Evidence map›Paper›PMID 38106255›Full record

ArticleQuantitative imaging in medicine and surgery2023

Exploring a simplified way to diagnose pelvic lipomatosis: prediction of pelvic fat volume using a single cross-sectional image.

Xin Bai, Rui Fu, Gumuyang Zhang, Lili Xu, Jiahui Zhang, Xiaoxiao Zhang, Li Chen, Qianyu Peng, Zhengyu Jin, Hao Sun

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

Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.0field-weighted citation impact, top 23% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

10 authors at 3 institutions in 1 country.

Xin BaiDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Rui FuCollege of Data Science, Taiyuan University of Technology, Jinzhong, China.
Gumuyang ZhangDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Lili XuDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Jiahui ZhangDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Xiaoxiao ZhangDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Li ChenDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Qianyu PengDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Zhengyu JinDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Hao SunDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNPeking Union Medical College Hospital · CNTaiyuan University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Pelvic lipomatosis (PL) is a rare disease characterized by the overgrowth of pelvic adipose tissue (AT). We investigated the relationships between areas of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) and pelvic fat volume (PFV), and analyzed the feasibility of diagnosing PL from a single cross-sectional image. Methods: The study included 50 patients and 50 controls. We used nnU-Net to segment SAT and VAT automatically. L3 vertebra was set as the zero point (L Results: VAT areas of L Conclusions: VAT areas at the level of FH can help estimate the value of PFV. VAT areas of S4 and FH provide greater power than a single image for the diagnosis of PL.

Indexed as

automatic segmentationDiagnostic imagingfat distributionspelvic lipomatosis (PL)quantitative analysis

Identifiers

PMID38106255
PMCPMC10722009
OpenAlexW4388986310

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.