Evidence map›Paper›PMID 40288929›Full record

ArticleCurrent problems in diagnostic radiology

Assessment of Pectoralis muscle invasion using combined DBT and contrast-enhanced mammography: Retrospective multi-reader study.

Olena O Weaver, Alejandro Contreras, Ethan O Cohen, Mary S Guirguis, Megha M Kapoor, Marion E Scoggins, Rosa F Hwang, Rosalind P Candelaria, Wei T Yang, Jennifer B Dennison and 2 more

Abstract read
In one paragraph

Article in Current problems in diagnostic radiology. 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
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.

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

12 authors.

Olena O WeaverDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, United States. Electronic address: OOWeaver@mdanderson.org.
Alejandro ContrerasDepartment of Anatomical Pathology, The University of Texas MD Anderson Cancer Center, United States.
Ethan O CohenDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, United States.
Mary S GuirguisDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, United States.
Megha M KapoorDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, United States.
Marion E ScogginsDepartment of Radiology, UT Southwestern Medical Center, United States.
Rosa F HwangDepartment of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, United States.
Rosalind P CandelariaDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, United States.
Wei T YangDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, United States.
Jennifer B DennisonDepartment of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, United States.
Jia SunDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, United States.
Gary J WhitmanDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, United States.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

objectivesTo evaluate combined digital breast tomosynthesis and contrast-enhanced mammography (DBT/CEM) for predicting pectoralis muscle invasion.

methodsThis retrospective multi-reader cohort study included research patients who underwent combined DBT/CEM for breast cancer staging and had prepectoral masses. Images were independently reviewed by six fellowship-trained breast radiologists. Diagnostic performance, reader confidence, and inter-reader agreement were calculated for each image type/modality.

resultsAmong 10 patients with prepectoral masses on DBT/CEM, muscle invasion was present in 3 and absent in 7. The overall diagnostic accuracy of DBT/CEM for PMI was 0.6 (range 0.4-0.9); for predefined radiologic signs it was 0.5-0.7 for low energy (LE) CEM, 0.4-0.7 for DBT, and 0.4-0.8 for recombined (RC) CEM. Muscle deformity on MLO views had the highest accuracy (0.7-0.8). On a scale of 1-3, mean radiologist confidence for combined DBT/CEM was 1.9 (1.5-2.3; SD=0.65). Median confidence ranged from 1.9 for RC to 2.2 for DBT. Per-case reader agreement was poor (K=-0.01) for DBT/CEM; poor to slight (K= -0.13-0.40, median 0.28) for RC; slight to fair (K = 0.04-0.43, median 0.27 and K = 0.02-0.42, median 0.19, respectively) for DBT and LE. In two patients with subpectoral breast implants CEM was accurate in PMI detection, while MRI had one false-positive result.

conclusionCombined DBT/CEM accuracy and inter-reader agreement are suboptimal for PMI evaluation, except in patients with breast implants. RC images marginally improve accuracy compared to LE images but have lowest radiologist confidence. DBT has lowest accuracy but highest confidence. Muscle deformity on MLO view was the most accurate sign. CRITICAL RELEVANCE STATEMENT: Combined DBT/CEM demonstrated suboptimal diagnostic accuracy, reader confidence, and inter-reader agreement for detecting pectoralis muscle invasion (PMI) in prepectoral breast cancer (BC) except for patients with subpectoral breast implants, where recombined images on implant-displaced CEM views performed better than MRI.

Indexed as

Breast NeoplasmsMammographyPectoralis MusclesRadiographic Image EnhancementAdultAgedContrast MediaFemaleHumansMiddle AgedNeoplasm InvasivenessRetrospective StudiesContrast MediaBreast cancerCEMContrast-enhanced mammographyPectoralis muscle invasionPrepectoral

Identifiers

PMID40288929
PMCPMC12359063

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.