Evidence map›Paper›PMID 35103236›Full record

ArticleBioMed research international2022

Computed Tomography-Based Radiomic Analysis for Preoperatively Predicting the Macrovesicular Steatosis Grade in Cadaveric Donor Liver Transplantation.

Shengnan Ding, Weimin Yang, Xiaodong Sun, Yan Guo, Guangjie Zhao, Jinzhu Yang, Lei Zhang, Guoyue Lv

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 37% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

8 authors at 3 institutions in 3 countries.

Shengnan DingDepartment of Radiology, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
Weimin YangDepartment of Emergency, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
Xiaodong SunDepartment of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
Yan GuoGE Healthcare, 1 Niudun Road, Zhangjiang High Tech Zone, Shanghai 201203, China.
Guangjie ZhaoDepartment of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
Jinzhu YangNortheast University, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, China.
Lei ZhangDepartment of Radiology, The First Hospital of Jilin University, Changchun, Jilin 130021, China.ORCID https://orcid.org/0000-0002-5567-7647
Guoyue LvDepartment of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, Jilin 130021, China.ORCID https://orcid.org/0000-0002-9778-5080
Jilin University · CNGeneral Electric (Spain) · ESMinistry of Education · BD

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study is aimed at determining the ability of computed tomography- (CT-) based radiomic analysis to distinguish between grade 0/1 and grade 2/3 macrovesicular steatosis (MaS) in cadaveric donor liver transplantation cases. Preoperative noncontrast-enhanced CT images of 150 patients with biopsy-confirmed MaS were analyzed retrospectively; these patients were classified into the low-grade MaS (

Indexed as

Liver TransplantationBiopsyCadaverFatty LiverFemaleHumansImaging, Three-DimensionalMaleMiddle AgedPredictive Value of TestsPreoperative PeriodRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesTomography, X-Ray Computed

Identifiers

PMID35103236
PMCPMC8800621
OpenAlexW4207000239

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.