Evidence map›Paper›PMID 34993080›Full record

ArticleQuantitative imaging in medicine and surgery2022

Quantitative analysis of late iodine enhancement using dual-layer spectral detector computed tomography: comparison with magnetic resonance imaging.

Peijun Liu, Lu Lin, Cheng Xu, Yechen Han, Xue Lin, Yang Hou, Xiaomei Lu, Mani Vembar, Zhengyu Jin, Yining Wang

Open access · diamondAbstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
1.1field-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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Extracellular volume fraction by cardiac computed tomography (ECVEuropean heart journal. Imaging methods and practice · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
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 5 institutions in 2 countries.

Peijun LiuDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Lu LinDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Cheng XuDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yechen HanDepartment of Cardiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xue LinDepartment of Cardiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yang HouDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
Xiaomei LuClinical Science, Philips Healthcare, Beijing, China.
Mani VembarCT Clinical Science, Philips Healthcare, Cleveland, OH, USA.
Zhengyu JinDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yining WangDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNChina Medical University · CNPeking Union Medical College Hospital · CNPhilips (China) · CNPhilips (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo evaluate the segmental myocardial extracellular volume (ECV) fraction and to define a threshold ECV value that can be used to distinguish positive late gadolinium enhancement (LGE) segments from negative myocardial segments using dual-layer spectral detector computed tomography (SDCT), with magnetic resonance imaging (MRI) as a reference.

methodsFifty-six subjects with cardiac disease or suspected cardiac disease, underwent both late iodine enhancement on CT (CT-LIE) scanning and late gadolinium enhancement on MRI (MRI-LGE) scanning. Each procedure occurred within a week of the other. Global and segmental ECVs of the left ventricle were measured by CT and MRI images. According to the location and pattern of delayed enhancement on MRI image, myocardial segments were classified into 3 groups: ischemic LGE segments (group 1), nonischemic LGE segments (group 2) and negative LGE segments (group 3). The correlation and agreement between CT-ECV and MRI-ECV were compared on a per-segment basis. Receiver operating characteristic (ROC) curve analysis was performed to establish a threshold for LIE detection.

resultsAmong the 56 patients, 896 segments were analyzed, and of these, 73 segments were in group 1, 229 segments were in group 2, and 594 segments were in group 3. In segmental analysis, CT-ECV in group 3 (27.0%; 24.9-28.9%) was significantly lower than that in group 1 (33.2%; 30.7-36.3%) and group 2 (34.9%; 32.3-39.8%; all P<0.001). Good correlations were seen between CT-ECV and MRI-ECV for all groups (group 1: r=0.920; group 2: r=0.936; group 3: r=0.799; all P<0.001). Bland-Altman analysis between CT-ECV and MRI-ECV showed a small bias in all 3 groups (group 1: -2.1%, 95% limits of agreement -11.3-7.1%; group 2: -0.6%, 95% limits of agreement -13.1-11.9%; group 3: 1.0%, 95% limits of agreement -12.7-14.7%). CT-ECV could differentiate between LGE-positive and LGE-negative segments with 83.1% sensitivity and 93.3% specificity at a cutoff of 31%.

conclusionsECV values derived from CT imaging showed good correlation and agreement with MR imaging findings, and CT-ECV provided high diagnostic accuracy for discriminating between LGE-positive and LGE-negative segments. Thus, cardiac CT imaging might be a suitable noninvasive imaging technique for myocardial ECV quantification.

Indexed as

Extracellular volume fraction (ECV fraction)myocardial delayed enhancementspectral detector computed tomography (SDCT)

Identifiers

PMID34993080
PMCPMC8666744
OpenAlexW3184370047

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.