Evidence map›Paper›PMID 33206226›Full record

ArticleEuropean radiology2021

Automatic quantification of myocardium and pericardial fat from coronary computed tomography angiography: a multicenter study.

Xiuxiu He, Bang Jun Guo, Yang Lei, Tonghe Wang, Walter J Curran, Tian Liu, Long Jiang Zhang, Xiaofeng Yang

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 18% 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

3 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Xiuxiu HeDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Bang Jun GuoDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Yang LeiDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Tonghe WangDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Walter J CurranDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Tian LiuDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Long Jiang ZhangDepartment of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China. kevinzhlj@163.com.
Xiaofeng YangDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA. xiaofeng.yang@emory.edu.
Emory University · USNanjing Medical University · CNNanjing University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo develop a deep learning-based method for simultaneous myocardium and pericardial fat quantification from coronary computed tomography angiography (CCTA) for the diagnosis and treatment of cardiovascular disease (CVD).

methodsWe retrospectively identified CCTA data obtained between May 2008 and July 2018 in a multicenter (six centers) CVD study. The proposed method was evaluated on 422 patients' data by two studies. The first overall study involves training model on CVD patients and testing on non-CVD patients, as well as training on non-CVD patients and testing on CVD patients. The second study was performed using the leave-center-out approach. The method performance was evaluated using Dice similarity coefficient (DSC), Jaccard index (JAC), 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). The robustness of the proposed method was tested using the nonparametric Kruskal-Wallis test and post hoc test to assess the equality of distribution of DSC values among different tests.

resultsThe automatic segmentation achieved a strong correlation with contour (ICC and R > 0.97, p value < 0.001 throughout all tests). The accuracy of the proposed method remained high through all the tests, with the median DSC higher than 0.88 for pericardial fat and 0.96 for myocardium. The proposed method also resulted in mean MSD, RMSD, HD95, and CMD of less than 1.36 mm for pericardial fat and 1.00 mm for myocardium.

conclusionsThe proposed deep learning-based segmentation method enables accurate simultaneous quantification of myocardium and pericardial fat in a multicenter study. KEY POINTS: • Deep learning-based myocardium and pericardial fat segmentation method tested on 422 patients' coronary computed tomography angiography in a multicenter study. • The proposed method provides segmentations with high volumetric accuracy (ICC and R > 0.97, p value < 0.001) and similar shape as manual annotation by experienced radiologists (median Dice similarity coefficient ≥ 0.88 for pericardial fat and 0.96 for myocardium).

Indexed as

Computed Tomography AngiographyTomography, X-Ray ComputedHumansImage Processing, Computer-AssistedMyocardiumPericardiumRetrospective StudiesCardiovascular diseaseComputed tomography angiographyComputer-assisted image processingDeep learning

Identifiers

PMID33206226
OpenAlexW3100109814

What OpenQuestion holds

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Registered trials

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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.