Evidence map›Paper›PMID 34217490›Full record

ArticleAcademic radiology2021

Prediction of Coronary Calcification and Stenosis: Role of Radiomics From Low-Dose CT.

Fatemeh Homayounieh, Pingkun Yan, Subba R Digumarthy, Uwe Kruger, Ge Wang, Mannudeep K Kalra

Open access · greenAbstract read
In one paragraph

Article in Academic radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

8 citing papers in PubMed, 17 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Fatemeh HomayouniehDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Room 248, Boston, MA 02114. Electronic address: fhomayounieh@mgh.harvard.edu.
Pingkun YanDepartment of Biomedical Engineering, Rensselaer Polytechnic Institute, Tory, New York.
Subba R DigumarthyDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Room 248, Boston, MA 02114.
Uwe KrugerDepartment of Biomedical Engineering, Rensselaer Polytechnic Institute, Tory, New York.
Ge WangDepartment of Biomedical Engineering, Rensselaer Polytechnic Institute, Tory, New York.
Mannudeep K KalraDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Room 248, Boston, MA 02114.
Harvard University · USRensselaer Polytechnic Institute · US

Funding

Deep Radiomic Analysis of Coronary Heart Disease with Lung Screening CTR56HL145172 · NHLBI · RENSSELAER POLYTECHNIC INSTITUTE · PI KALRA, MANNUDEEP KARANVIR SINGH, YAN, PINGKUN · 2019 to 2019
$370k
NHLBI NIH HHS R56 HL145172
6 · The paper itself

Abstract

RATIONALE AND

objectivesWe aimed to assess relationship between single-click, whole heart radiomics from low-dose computed tomography (LDCT) for lung cancer screening with coronary artery calcification and stenosis. MATERIALS AND

methodsThe institutional review board-approved, retrospective study included all 106 patients (68 men, 38 women, mean age 64 ± 7 years) who underwent both LDCT for lung cancer screening and had calcium scoring and coronary computed tomography angiography in our institution. We recorded the clinical variables including patients' demographics, smoking history, family history, and lipid profiles. Coronary calcium scores and grading of coronary stenosis were recorded from the radiology information system. We calculated the multiethnic scores for atherosclerosis risk scores to obtain 10-year coronary heart disease (MESA 10-Y CHD) risk of cardiovascular disease for all patients. Deidentified LDCT exams were exported to a Radiomics prototype for automatic heart segmentation, and derivation of radiomics. Data were analyzed using multiple logistic regression and kernel Fisher discriminant analyses.

resultsWhole heart radiomics were better than the clinical variables for differentiating subjects with different Agatston scores (≤400 and >400) (area under the curve [AUC] 0.92 vs 0.69). Prediction of coronary stenosis and MESA 10-Y CHD risk was better on whole heart radiomics (AUC:0.86-0.87) than with clinical variables (AUC:0.69-0.79). Addition of clinical variables or visual assessment of coronary calcification from LDCT to whole heart radiomics resulted in a modest change in the AUC.

conclusionSingle-click, whole heart radiomics obtained from LDCT for lung cancer screening can differentiate patients with different Agatston and MESA risk scores for cardiovascular diseases.

Indexed as

Coronary Artery DiseaseCoronary StenosisLung NeoplasmsVascular CalcificationAgedConstriction, PathologicCoronary AngiographyCoronary VesselsEarly Detection of CancerFemaleHumansMaleMiddle AgedRetrospective StudiesTomography, X-Ray ComputedCoronary artery diseaseLow dose CTLung Cancer ScreeningRadiomicsSmoking

Identifiers

PMID34217490
PMCPMC8262050
OpenAlexW3182810329

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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.