Evidence map›Paper›PMID 31230138›Full record

ArticleJournal of medical systems2019

Automated Breast Density Measurements From Chest Computed Tomography Scans.

Touseef A Qureshi, Harini Veeraraghavan, Janice S Sung, Jennifer B Kaplan, Jessica Flynn, Emily S Tonorezos, Suzanne L Wolden, Elizabeth A Morris, Kevin C Oeffinger, Malcolm C Pike and 1 more

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

Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 90% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

11 authors at 3 institutions in 1 country.

Touseef A QureshiCedars-Sinai Medical Center, Biomedical Imaging Research Institute, 8700 Beverly Blvd, Pact 400, Los Angeles, CA, 90048, USA.
Harini VeeraraghavanMemorial Sloan Kettering Cancer Center, Department of Medical Physics, 485 Lexington Avenue, New York, NY, 10017, USA.
Janice S SungMemorial Sloan Kettering Cancer Center, Department of Radiology, 1275 York Avenue, New York, NY, 10065, USA.
Jennifer B KaplanMemorial Sloan Kettering Cancer Center, Department of Radiology, 1275 York Avenue, New York, NY, 10065, USA.
Jessica FlynnMemorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, 485 Lexington Avenue, New York, NY, 10017, USA.
Emily S TonorezosMemorial Sloan Kettering Cancer Center, Department of Medicine, 485 Lexington Avenue, New York, NY, USA.
Suzanne L WoldenMemorial Sloan Kettering Cancer Center, Department of Radiation Oncology, 1275 York Avenue, New York, NY, 10065, USA.
Elizabeth A MorrisMemorial Sloan Kettering Cancer Center, Department of Radiology, 1275 York Avenue, New York, NY, 10065, USA.
Kevin C OeffingerDepartment of Medicine, Duke University, 2424 Erwin Dr, Suite, e 601, Durham, NC, 27705, USA.
Malcolm C PikeMemorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, 485 Lexington Avenue, New York, NY, 10017, USA.
Chaya S MoskowitzMemorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, 485 Lexington Avenue, New York, NY, 10017, USA. moskowc1@mskcc.org.
Memorial Sloan Kettering Cancer Center · USCedars-Sinai Medical Center · USDuke University · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

To develop an automated method for quantifying percent breast density from chest computed tomography (CT) scans. A naïve Bayesian classifier based on gray-level intensities and spatial relationships was developed on CT scans from 10 patients diagnosed with Hodgkin lymphoma (HL) and imaged as part of routine clinical care. The algorithm was validated on CT scans from 75 additional HL patients. The classifier was developed and validated using a reference dataset with consensus manual segmentation of fibroglandular tissue. Accuracy was evaluated at the pixel-level to examine how well the algorithm identified pixels with fibroglandular tissue using true and false positive fractions (TPF and FPF, respectively). Quantitative estimates of the patient-level CT percent density were contrasted to each other using the concordance correlation coefficient, ρ

Indexed as

Breast DensityRadiography, ThoracicAdultAlgorithmsBayes TheoremFemaleHumansImage Processing, Computer-AssistedTomography, X-Ray ComputedYoung AdultAlgorithmBayes theoremBreast densityRisk

Identifiers

PMID31230138
PMCPMC7575036
OpenAlexW2950301910

What OpenQuestion holds

Textmetadata
LicenceTDM
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.