Mammographic Breast Density Model Using Semi-Supervised Learning Reduces Inter-/Intra-Reader Variability.

Alyssa T Watanabe, Tara Retson, Junhao Wang, Richard Mantey, Chiyung Chim, Homa Karimabadi

Open access · goldAbstract read

In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. 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

1.3field-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

0 citing papers in PubMed, 5 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

6 authors at 3 institutions in 1 country.

Alyssa T WatanabeDepartment of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90007, USA.

Tara RetsonDepartment of Radiology, University of California, San Diego, CA 92093, USA.ORCID 0000-0002-0009-7733

Junhao WangCureMetrix, Inc., San Diego, CA 92101, USA.

Richard ManteyCureMetrix, Inc., San Diego, CA 92101, USA.

Chiyung ChimCureMetrix, Inc., San Diego, CA 92101, USA.

Homa KarimabadiCureMetrix, Inc., San Diego, CA 92101, USA.

SensorMetrix (United States) · USUniversity of California San Diego · USUniversity of Southern California · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast density is an important risk factor for breast cancer development; however, imager inconsistency in density reporting can lead to patient and clinician confusion. A deep learning (DL) model for mammographic density grading was examined in a retrospective multi-reader multi-case study consisting of 928 image pairs and assessed for impact on inter- and intra-reader variability and reading time. Seven readers assigned density categories to the images, then re-read the test set aided by the model after a 4-week washout. To measure intra-reader agreement, 100 image pairs were blindly double read in both sessions. Linear Cohen Kappa (κ) and Student's

Indexed as

automated breast densitydeep learningmammographyreader variability

Identifiers

PMID37627953

DOI10.3390/diagnostics13162694

PMCPMC10453732

OpenAlexW4385877876

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.